From 6e33e9d72e5a54cea6337f00e92d545f5cbcc4db Mon Sep 17 00:00:00 2001 From: Zhao Shenyang Date: Mon, 29 Apr 2024 16:59:15 +0800 Subject: [PATCH 1/3] chore: remove outdated examples --- examples/custom_model_runner/.gitignore | 3 - examples/custom_model_runner/BUILD | 18 - examples/custom_model_runner/README.md | 105 - examples/custom_model_runner/bentofile.yaml | 6 - examples/custom_model_runner/locustfile.py | 16 - examples/custom_model_runner/net.py | 31 - .../prometheus/prometheus.grpc.yml | 9 - .../prometheus/prometheus.http.yml | 9 - examples/custom_model_runner/requirements.txt | 3 - examples/custom_model_runner/service.py | 59 - examples/custom_model_runner/train.py | 166 - examples/custom_model_runner/utils.py | 29 - .../lda_classifier/README.md | 43 - .../lda_classifier/bentofile.yaml | 7 - .../custom_python_model/lda_classifier/lda.py | 58 - .../lda_classifier/requirements.txt | 2 - .../lda_classifier/service.py | 16 - .../lda_classifier/train.py | 42 - 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b/examples/custom_model_runner/.gitignore deleted file mode 100644 index a9386b08c6d..00000000000 --- a/examples/custom_model_runner/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -data -mnist_png/ -mnist_png.tar.gz diff --git a/examples/custom_model_runner/BUILD b/examples/custom_model_runner/BUILD deleted file mode 100644 index fe0e8551a1b..00000000000 --- a/examples/custom_model_runner/BUILD +++ /dev/null @@ -1,18 +0,0 @@ -package(default_visibility = ["//visibility:private"]) - -load("//bazel:utils.bzl", "run_shell") - -run_shell( - name = "download_mnist_data", - content = [ - " | ".join([ - "wget -qO- https://github.com/myleott/mnist_png/raw/master/mnist_png.tar.gz", - "tar xz", - ]), - ], -) - -run_shell( - name = "train", - content = ["python train.py $@"], -) diff --git a/examples/custom_model_runner/README.md b/examples/custom_model_runner/README.md deleted file mode 100644 index 776d8f6d43d..00000000000 --- a/examples/custom_model_runner/README.md +++ /dev/null @@ -1,105 +0,0 @@ -# Custom PyTorch MNIST runner - -This example showcases how one can extend BentoML's provided runner and build a custom Runner. See [our documentation][#custom-runner] on Runners. - -This example will also demonstrate how one can create custom metrics to monitor the model's performance. -We will provide two Prometheus configs to use for either HTTP or gRPC BentoServer for demonstration. - -### Requirements - -Install requirements with: - -```bash -pip install -r requirements.txt -``` - -### Instruction - -1. Train and save model: - -```bash -python -m train -``` - -2. Download test data - -```bash -wget -qO- https://github.com/myleott/mnist_png/raw/master/mnist_png.tar.gz | tar xz -``` - -3. Start a development server: - - - - - - - - - - - - - -
Protocol Command
HTTP - -```bash -bentoml serve-http service.py:svc --development -``` - -
gRPC - -```bash -bentoml serve-grpc service.py:svc --development -``` - -
- -4. Send test requests - - - - - - - - - - - -
Protocol Command
HTTP - -```bash -curl -F 'image=@mnist_png/testing/8/1007.png' http://127.0.0.1:3000/predict -``` - -
gRPC - -```bash -grpcurl -d @ -plaintext 0.0.0.0:3000 bentoml.grpc.v1.BentoService/Call < - -5. Load testing - -Start production server: - -```bash -bentoml serve -``` - -From another terminal: - -```bash -pip install locust -locust -H http://0.0.0.0:3000 -``` - -[#custom-runner]: https://docs.bentoml.com/en/latest/concepts/runner.html#custom-runner diff --git a/examples/custom_model_runner/bentofile.yaml b/examples/custom_model_runner/bentofile.yaml deleted file mode 100644 index 5d051ea4d4f..00000000000 --- a/examples/custom_model_runner/bentofile.yaml +++ /dev/null @@ -1,6 +0,0 @@ -service: "service:svc" -include: - - "service.py" - - "mnist.py" -python: - requirements_txt: "./requirements.txt" diff --git a/examples/custom_model_runner/locustfile.py b/examples/custom_model_runner/locustfile.py deleted file mode 100644 index 08a14b33c85..00000000000 --- a/examples/custom_model_runner/locustfile.py +++ /dev/null @@ -1,16 +0,0 @@ -from locust import HttpUser -from locust import between -from locust import task - - -class MnistTestUser(HttpUser): - @task - def predict(self): - url = "/predict" - filename = "./mnist_png/testing/9/1000.png" - files = [ - ("file", (filename, open(filename, "rb"), "image/png")), - ] - self.client.post(url, files=files) - - wait_time = between(0.05, 2) diff --git a/examples/custom_model_runner/net.py b/examples/custom_model_runner/net.py deleted file mode 100644 index b9c1a0665d8..00000000000 --- a/examples/custom_model_runner/net.py +++ /dev/null @@ -1,31 +0,0 @@ -from __future__ import annotations - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class CNN(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 32, 3, 1) - self.conv2 = nn.Conv2d(32, 64, 3, 1) - self.dropout1 = nn.Dropout(0.25) - self.dropout2 = nn.Dropout(0.5) - self.fc1 = nn.Linear(9216, 128) - self.fc2 = nn.Linear(128, 10) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.conv2(x) - x = F.relu(x) - x = F.max_pool2d(x, 2) - x = self.dropout1(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = F.relu(x) - x = self.dropout2(x) - x = self.fc2(x) - output = F.log_softmax(x, dim=1) - return output diff --git a/examples/custom_model_runner/prometheus/prometheus.grpc.yml b/examples/custom_model_runner/prometheus/prometheus.grpc.yml deleted file mode 100644 index 071406f8cac..00000000000 --- a/examples/custom_model_runner/prometheus/prometheus.grpc.yml +++ /dev/null @@ -1,9 +0,0 @@ -global: - scrape_interval: 5s - evaluation_interval: 15s - -scrape_configs: - - job_name: prometheus - metrics_path: "/" - static_configs: - - targets: ["0.0.0.0:3001"] diff --git a/examples/custom_model_runner/prometheus/prometheus.http.yml b/examples/custom_model_runner/prometheus/prometheus.http.yml deleted file mode 100644 index f7708037971..00000000000 --- a/examples/custom_model_runner/prometheus/prometheus.http.yml +++ /dev/null @@ -1,9 +0,0 @@ -global: - scrape_interval: 5s - evaluation_interval: 15s - -scrape_configs: - - job_name: prometheus - metrics_path: "/metrics" - static_configs: - - targets: ["0.0.0.0:3000"] diff --git a/examples/custom_model_runner/requirements.txt b/examples/custom_model_runner/requirements.txt deleted file mode 100644 index 48a2882badb..00000000000 --- a/examples/custom_model_runner/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -bentoml -torch -torchvision diff --git a/examples/custom_model_runner/service.py b/examples/custom_model_runner/service.py deleted file mode 100644 index 283d1c039a3..00000000000 --- a/examples/custom_model_runner/service.py +++ /dev/null @@ -1,59 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - import PIL.Image - -import time - -import numpy as np -from utils import exponential_buckets - -import bentoml - -mnist_model = bentoml.pytorch.get("mnist_cnn:latest") -_BuiltinRunnable = mnist_model.to_runnable() - -inference_duration = bentoml.metrics.Histogram( - name="inference_duration", - documentation="Duration of inference", - labelnames=["torch_version", "device_id"], - buckets=exponential_buckets(0.001, 1.5, 10.0), -) - - -class CustomMnistRunnable(_BuiltinRunnable): - def __init__(self): - super().__init__() - import torch - - print("Running on device:", self.device_id) - self.torch_version = torch.__version__ - print("Running on torch version:", self.torch_version) - - @bentoml.Runnable.method(batchable=True, batch_dim=0) - def __call__(self, input_data: np.ndarray) -> np.ndarray: - start = time.perf_counter() - output = super().__call__(input_data) - inference_duration.labels( - torch_version=self.torch_version, device_id=self.device_id - ).observe(time.perf_counter() - start) - return output.argmax(dim=1) - - -mnist_runner = bentoml.Runner( - CustomMnistRunnable, - method_configs={"__call__": {"max_batch_size": 50, "max_latency_ms": 600}}, -) - -svc = bentoml.Service( - "pytorch_mnist_demo", runners=[mnist_runner], models=[mnist_model] -) - - -@svc.api(input=bentoml.io.Image(), output=bentoml.io.NumpyNdarray()) -async def predict(image: PIL.Image.Image) -> np.ndarray: - arr = np.array(image).reshape([-1, 1, 28, 28]) - res = await mnist_runner.async_run(arr) - return res.numpy() diff --git a/examples/custom_model_runner/train.py b/examples/custom_model_runner/train.py deleted file mode 100644 index 20d39aaf574..00000000000 --- a/examples/custom_model_runner/train.py +++ /dev/null @@ -1,166 +0,0 @@ -from __future__ import print_function - -import argparse - -import net -import torch -import torch.nn.functional as F -import torch.optim as optim -from torch.optim.lr_scheduler import StepLR -from torch.utils.data import DataLoader -from torchvision import datasets -from torchvision import transforms - - -def train(args, model, device, train_loader, optimizer, epoch): - model.train() - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(device), target.to(device) - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print( - "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( - epoch, - batch_idx * len(data), - len(train_loader.dataset), - 100.0 * batch_idx / len(train_loader), - loss.item(), - ) - ) - if args.dry_run: - break - - -def test(model, device, test_loader): - model.eval() - test_loss = 0 - correct = 0 - with torch.no_grad(): - for data, target in test_loader: - data, target = data.to(device), target.to(device) - output = model(data) - test_loss += F.nll_loss( - output, target, reduction="sum" - ).item() # sum up batch loss - pred = output.argmax( - dim=1, keepdim=True - ) # get the index of the max log-probability - correct += pred.eq(target.view_as(pred)).sum().item() - - test_loss /= len(test_loader.dataset) - - print( - "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - test_loss, - correct, - len(test_loader.dataset), - 100.0 * correct / len(test_loader.dataset), - ) - ) - - -def main(): - # Training settings - parser = argparse.ArgumentParser(description="PyTorch MNIST Example") - parser.add_argument( - "--batch-size", - type=int, - default=64, - metavar="N", - help="input batch size for training (default: 64)", - ) - parser.add_argument( - "--test-batch-size", - type=int, - default=1000, - metavar="N", - help="input batch size for testing (default: 1000)", - ) - parser.add_argument( - "--epochs", - type=int, - default=5, - metavar="N", - help="number of epochs to train (default: 5)", - ) - parser.add_argument( - "--lr", - type=float, - default=1.0, - metavar="LR", - help="learning rate (default: 1.0)", - ) - parser.add_argument( - "--gamma", - type=float, - default=0.7, - metavar="M", - help="Learning rate step gamma (default: 0.7)", - ) - parser.add_argument( - "--no-cuda", action="store_true", default=False, help="disables CUDA training" - ) - parser.add_argument( - "--dry-run", - action="store_true", - default=False, - help="quickly check a single pass", - ) - parser.add_argument( - "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)" - ) - parser.add_argument( - "--log-interval", - type=int, - default=10, - metavar="N", - help="how many batches to wait before logging training status", - ) - args = parser.parse_args() - use_cuda = not args.no_cuda and torch.cuda.is_available() - - torch.manual_seed(args.seed) - - device = torch.device("cuda" if use_cuda else "cpu") - - train_kwargs = {"batch_size": args.batch_size} - test_kwargs = {"batch_size": args.test_batch_size} - if use_cuda: - cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True} - train_kwargs.update(cuda_kwargs) - test_kwargs.update(cuda_kwargs) - - transform = transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] - ) - train_ds = datasets.MNIST("data", train=True, download=True, transform=transform) - test_ds = datasets.MNIST("data", train=False, transform=transform) - train_loader = DataLoader(train_ds, **train_kwargs) - test_loader = DataLoader(test_ds, **test_kwargs) - - model = net.CNN().to(device) - optimizer = optim.Adadelta(model.parameters(), lr=args.lr) - - scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) - for epoch in range(1, args.epochs + 1): - train(args, model, device, train_loader, optimizer, epoch) - test(model, device, test_loader) - scheduler.step() - - import bentoml - - model = bentoml.pytorch.save_model( - "mnist_cnn", - model, - signatures={"__call__": {"batchable": True}}, - external_modules=[net], - ) - print(f"Saved: {model}") - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/examples/custom_model_runner/utils.py b/examples/custom_model_runner/utils.py deleted file mode 100644 index 63e6e066535..00000000000 --- a/examples/custom_model_runner/utils.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -INF = float("inf") - -MAX_BUCKET_COUNT = 100 - - -def exponential_buckets(start: float, factor: float, end: float) -> tuple[float, ...]: - """ - Creates buckets of a Prometheus histogram where the lowest bucket has an upper - bound of start and the upper bound of each following bucket is factor times the - previous buckets upper bound. The return tuple include the end as the second - last value and positive infinity as the last value. - """ - - assert start > 0.0 - assert start < end - assert factor > 1.0 - - bound = start - buckets: list[float] = [] - while bound < end: - buckets.append(bound) - bound *= factor - - if len(buckets) > MAX_BUCKET_COUNT: - buckets = buckets[:MAX_BUCKET_COUNT] - - return tuple(buckets) + (end, INF) diff --git a/examples/custom_python_model/lda_classifier/README.md b/examples/custom_python_model/lda_classifier/README.md deleted file mode 100644 index ce0848f658f..00000000000 --- a/examples/custom_python_model/lda_classifier/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Custom LDA classifier model via bentoml.picklable_model - -This example is based on https://github.com/eriklindernoren/ML-From-Scratch - -`bentoml.picklable_model` represents a generic model type in BentoML, that uses -`cloudpickle` for model serialization under the hood. Most pure python code based -ML model implementation should work with `bentoml.picklable_model` out-of-the-box. - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train a custom LDA model: - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -``` -curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/classify -``` - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize iris_classifier_lda:latest -``` diff --git a/examples/custom_python_model/lda_classifier/bentofile.yaml b/examples/custom_python_model/lda_classifier/bentofile.yaml deleted file mode 100644 index cd7f0400543..00000000000 --- a/examples/custom_python_model/lda_classifier/bentofile.yaml +++ /dev/null @@ -1,7 +0,0 @@ -service: "service.py:svc" -include: -- "service.py" -- "lda.py" -python: - packages: - - numpy diff --git a/examples/custom_python_model/lda_classifier/lda.py b/examples/custom_python_model/lda_classifier/lda.py deleted file mode 100644 index 007ae42e466..00000000000 --- a/examples/custom_python_model/lda_classifier/lda.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import division -from __future__ import print_function - -import numpy as np - - -def calculate_covariance_matrix(X, Y=None): - """Calculate the covariance matrix for the dataset X""" - if Y is None: - Y = X - n_samples = np.shape(X)[0] - covariance_matrix = (1 / (n_samples - 1)) * (X - X.mean(axis=0)).T.dot( - Y - Y.mean(axis=0) - ) - - return np.array(covariance_matrix, dtype=float) - - -class LDA: - """The Linear Discriminant Analysis classifier, also known as Fisher's linear discriminant. - Can besides from classification also be used to reduce the dimensionaly of the dataset. - """ - - def __init__(self): - self.w = None - - def transform(self, X, y): - self.fit(X, y) - # Project data onto vector - X_transform = X.dot(self.w) - return X_transform - - def fit(self, X, y): - # Separate data by class - X1 = X[y == 0] - X2 = X[y == 1] - - # Calculate the covariance matrices of the two datasets - cov1 = calculate_covariance_matrix(X1) - cov2 = calculate_covariance_matrix(X2) - cov_tot = cov1 + cov2 - - # Calculate the mean of the two datasets - mean1 = X1.mean(0) - mean2 = X2.mean(0) - mean_diff = np.atleast_1d(mean1 - mean2) - - # Determine the vector which when X is projected onto it best separates the - # data by class. w = (mean1 - mean2) / (cov1 + cov2) - self.w = np.linalg.pinv(cov_tot).dot(mean_diff) - - def predict(self, X): - y_pred = [] - for sample in X: - h = sample.dot(self.w) - y = 1 * (h < 0) - y_pred.append(y) - return y_pred diff --git a/examples/custom_python_model/lda_classifier/requirements.txt b/examples/custom_python_model/lda_classifier/requirements.txt deleted file mode 100644 index 362816fef7c..00000000000 --- a/examples/custom_python_model/lda_classifier/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -scikit-learn -bentoml>=1.0.0 diff --git a/examples/custom_python_model/lda_classifier/service.py b/examples/custom_python_model/lda_classifier/service.py deleted file mode 100644 index 9a603f1f11c..00000000000 --- a/examples/custom_python_model/lda_classifier/service.py +++ /dev/null @@ -1,16 +0,0 @@ -import typing - -import numpy as np - -import bentoml -from bentoml.io import JSON -from bentoml.io import NumpyNdarray - -iris_clf_runner = bentoml.picklable_model.get("iris_clf_lda:latest").to_runner() - -svc = bentoml.Service("iris_classifier_lda", runners=[iris_clf_runner]) - - -@svc.api(input=NumpyNdarray(dtype="float", shape=(-1, 4)), output=JSON()) -async def classify(input_series: np.ndarray) -> typing.List[float]: - return await iris_clf_runner.predict.async_run(input_series) diff --git a/examples/custom_python_model/lda_classifier/train.py b/examples/custom_python_model/lda_classifier/train.py deleted file mode 100644 index c2a0bfb3a2f..00000000000 --- a/examples/custom_python_model/lda_classifier/train.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import print_function - -# import custom model class -from lda import LDA -from sklearn import datasets -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - -import bentoml - - -def main(): - # Load the dataset - data = datasets.load_iris() - X = data.data - y = data.target - - # Three -> two classes - X = X[y != 2] - y = y[y != 2] - - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) - - # Fit and predict using LDA - lda = LDA() - lda.fit(X_train, y_train) - y_pred = lda.predict(X_test) - - accuracy = accuracy_score(y_test, y_pred) - print("Accuracy:", accuracy) - - # Save model with BentoML - saved_model = bentoml.picklable_model.save_model( - "iris_clf_lda", - lda, - signatures={"predict": {"batchable": True}}, - ) - print(f"Model saved: {saved_model}") - - -if __name__ == "__main__": - main() diff --git a/examples/custom_python_model/simple_pickable_model/README.md b/examples/custom_python_model/simple_pickable_model/README.md deleted file mode 100644 index 8082cc047c4..00000000000 --- a/examples/custom_python_model/simple_pickable_model/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Simple Python model via bentoml.picklable_model - - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Save the simple python model: - -```bash -python ./model.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request: - -```bash -curl -X POST -H "content-type: application/json" --data "[1,2,3,4,5]" http://127.0.0.1:3000/square -``` - -4. Build Bento - -```bash -bentoml build -``` - -5. Build docker image - -```bash -bentoml containerize simple_square_svc:latest -``` - -```bash -docker run -p 3000:3000 simple_square_svc:ixbjlmqg3cjbluqj -``` diff --git a/examples/custom_python_model/simple_pickable_model/bentofile.yaml b/examples/custom_python_model/simple_pickable_model/bentofile.yaml deleted file mode 100644 index 82864690e01..00000000000 --- a/examples/custom_python_model/simple_pickable_model/bentofile.yaml +++ /dev/null @@ -1,7 +0,0 @@ -service: "service:svc" -include: - - "model.py" - - "service.py" -python: - packages: - - "numpy" diff --git a/examples/custom_python_model/simple_pickable_model/model.py b/examples/custom_python_model/simple_pickable_model/model.py deleted file mode 100644 index 59a10466198..00000000000 --- a/examples/custom_python_model/simple_pickable_model/model.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing import List - -import numpy as np - -import bentoml - - -def my_python_model(input_list: List[int]) -> List[int]: - return np.square(np.array(input_list)) - - -if __name__ == "__main__": - # `save_model` saves a given python object or function - saved_model = bentoml.picklable_model.save_model( - "my_python_model", my_python_model, signatures={"__call__": {"batchable": True}} - ) - print(f"Model saved: {saved_model}") diff --git a/examples/custom_python_model/simple_pickable_model/requirements.txt b/examples/custom_python_model/simple_pickable_model/requirements.txt deleted file mode 100644 index bd75c582e0e..00000000000 --- a/examples/custom_python_model/simple_pickable_model/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -bentoml>=1.0.0 diff --git a/examples/custom_python_model/simple_pickable_model/service.py b/examples/custom_python_model/simple_pickable_model/service.py deleted file mode 100644 index 6e797f6917e..00000000000 --- a/examples/custom_python_model/simple_pickable_model/service.py +++ /dev/null @@ -1,10 +0,0 @@ -import bentoml - -square_runner = bentoml.picklable_model.get("my_python_model:latest").to_runner() - -svc = bentoml.Service("simple_square_svc", runners=[square_runner]) - - -@svc.api(input=bentoml.io.JSON(), output=bentoml.io.JSON()) -async def square(input_arr): - return await square_runner.async_run(input_arr) diff --git a/examples/custom_runner/nltk_pretrained_model/BUILD b/examples/custom_runner/nltk_pretrained_model/BUILD deleted file mode 100644 index ae5d9ed1961..00000000000 --- a/examples/custom_runner/nltk_pretrained_model/BUILD +++ /dev/null @@ -1,8 +0,0 @@ -package(default_visibility = ["//visibility:private"]) - -load("//bazel:utils.bzl", "run_shell") - -run_shell( - name = "download_nltk_models", - content = ["python -m download_nltk_models"], -) diff --git a/examples/custom_runner/nltk_pretrained_model/README.md b/examples/custom_runner/nltk_pretrained_model/README.md deleted file mode 100644 index dc0501bf037..00000000000 --- a/examples/custom_runner/nltk_pretrained_model/README.md +++ /dev/null @@ -1,98 +0,0 @@ -# Custom Runner with pre-trained NLTK model - -For ML libraries that provide built-in trained models, such as NLTK, users may create a -custom Runner directly without saving the model to the model store. - -This example showcases how one can create a custom Runner directly without saving models -to model store for frameworks that provide buil-tin trained models. See [our documentation][#custom-runner] on Runners. - -This example will also demonstrate how one can create custom metrics to monitor the model's performance. -We will provide two Prometheus configs to use for either HTTP or gRPC BentoServer for demonstration. - -### Requirements - -Install requirements with: - -```bash -pip install -r requirements.txt -``` - -### Instruction - -1. Download required NLTK pre-trained models: - -```bash -python -m download_nltk_models -``` - -2. Run the service: - - - - - - - - - - - - - -
Protocol Command
HTTP - -```bash -bentoml serve-http service.py:svc -``` - -
gRPC - -```bash -bentoml serve-grpc service.py:svc -``` - -
- -3. Send in test request: - - - - - - - - - - - -
Protocol Command
HTTP - -```bash -curl -X POST -H "content-type: application/text" --data "BentoML is great" http://127.0.0.1:3000/analysis -``` - -
gRPC -
- -```bash -grpcurl -d @ -plaintext 0.0.0.0:3000 bentoml.grpc.v1.BentoService/Call <=1.0.0 diff --git a/examples/custom_runner/nltk_pretrained_model/service.py b/examples/custom_runner/nltk_pretrained_model/service.py deleted file mode 100644 index 37dd17fd02f..00000000000 --- a/examples/custom_runner/nltk_pretrained_model/service.py +++ /dev/null @@ -1,83 +0,0 @@ -from __future__ import annotations - -import time -import typing as t -from statistics import mean -from typing import TYPE_CHECKING - -import nltk -from nltk.sentiment import SentimentIntensityAnalyzer - -import bentoml -from bentoml.io import JSON -from bentoml.io import Text - -if TYPE_CHECKING: - from bentoml._internal.runner.runner import RunnerMethod - - class RunnerImpl(bentoml.Runner): - is_positive: RunnerMethod - - -inference_duration = bentoml.metrics.Histogram( - name="inference_duration", - documentation="Duration of inference", - labelnames=["nltk_version", "sentiment_cls"], - buckets=( - 0.005, - 0.01, - 0.025, - 0.05, - 0.075, - 0.1, - 0.25, - 0.5, - 0.75, - 1.0, - 2.5, - 5.0, - 7.5, - 10.0, - float("inf"), - ), -) - -polarity_counter = bentoml.metrics.Counter( - name="polarity_total", - documentation="Count total number of analysis by polarity scores", - labelnames=["polarity"], -) - - -class NLTKSentimentAnalysisRunnable(bentoml.Runnable): - SUPPORTED_RESOURCES = ("cpu",) - SUPPORTS_CPU_MULTI_THREADING = False - - def __init__(self): - self.sia = SentimentIntensityAnalyzer() - - @bentoml.Runnable.method(batchable=False) - def is_positive(self, input_text: str) -> bool: - start = time.perf_counter() - scores = [ - self.sia.polarity_scores(sentence)["compound"] - for sentence in nltk.sent_tokenize(input_text) - ] - inference_duration.labels( - nltk_version=nltk.__version__, sentiment_cls=self.sia.__class__.__name__ - ).observe(time.perf_counter() - start) - return mean(scores) > 0 - - -nltk_runner = t.cast( - "RunnerImpl", bentoml.Runner(NLTKSentimentAnalysisRunnable, name="nltk_sentiment") -) - -svc = bentoml.Service("sentiment_analyzer", runners=[nltk_runner]) - - -@svc.api(input=Text(), output=JSON()) -async def analysis(input_text: str) -> dict[str, bool]: - is_positive = await nltk_runner.is_positive.async_run(input_text) - polarity_counter.labels(polarity=is_positive).inc() - return {"is_positive": is_positive} diff --git a/examples/custom_runner/nltk_pretrained_model/utils.py b/examples/custom_runner/nltk_pretrained_model/utils.py deleted file mode 100644 index 63e6e066535..00000000000 --- a/examples/custom_runner/nltk_pretrained_model/utils.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -INF = float("inf") - -MAX_BUCKET_COUNT = 100 - - -def exponential_buckets(start: float, factor: float, end: float) -> tuple[float, ...]: - """ - Creates buckets of a Prometheus histogram where the lowest bucket has an upper - bound of start and the upper bound of each following bucket is factor times the - previous buckets upper bound. The return tuple include the end as the second - last value and positive infinity as the last value. - """ - - assert start > 0.0 - assert start < end - assert factor > 1.0 - - bound = start - buckets: list[float] = [] - while bound < end: - buckets.append(bound) - bound *= factor - - if len(buckets) > MAX_BUCKET_COUNT: - buckets = buckets[:MAX_BUCKET_COUNT] - - return tuple(buckets) + (end, INF) diff --git a/examples/custom_runner/torch_hub_yolov5/.gitignore b/examples/custom_runner/torch_hub_yolov5/.gitignore deleted file mode 100644 index 4b6ebe5ff71..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/.gitignore +++ /dev/null @@ -1 +0,0 @@ -*.pt diff --git a/examples/custom_runner/torch_hub_yolov5/README.md b/examples/custom_runner/torch_hub_yolov5/README.md deleted file mode 100644 index cf62e877baf..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/README.md +++ /dev/null @@ -1,87 +0,0 @@ -# Serving YOLOv5 model with BentoML - -This project demonstrate how to use pretrained YOLOv5 model from Torch hub, and use -it to build a prediction service in BentoML. - -The model used in this example is built upon https://github.com/ultralytics/yolov5 - -## Before you started - -Install required dependencies: - -```bash -pip install -r ./requirements.txt -``` - -Download the model file via `torch.hub.load`: - -```python -import torch -torch.hub.load('ultralytics/yolov5', 'yolov5s') -``` - -Now you should have a `yolov5s.pt` file created in current directory. - -## Run the service - -Launch the service locally: - -```bash -bentoml serve service.py:svc -``` - - -## Test the endpoint - -Visit http://127.0.0.1:3000 to submit input images via the web UI. - -The sample service provides two different endpoints: -* `/invocation` - takes an image input and returns predictions in a tabular data format -* `/render` - takes an image input and returns the input image with boxes and labels rendered on top - - -To test the `/invocation` endpoint: - -```bash -curl -X 'POST' \ - 'http://localhost:3000/invocation' \ - -H 'accept: image/jpeg' \ - -H 'Content-Type: image/jpeg' \ - --data-binary '@data/bus.jpg' -``` - -Sample result: -``` -[{"xmin":51.4846191406,"ymin":399.7782592773,"xmax":225.7452697754,"ymax":894.1701049805,"confidence":0.8960712552,"class":0,"name":"person"},{"xmin":25.1442546844,"ymin":230.5268707275,"xmax":803.268371582,"ymax":767.0746459961,"confidence":0.8453037143,"class":5,"name":"bus"},{"xmin":219.5371704102,"ymin":398.213684082,"xmax":350.1277770996,"ymax":861.6119384766,"confidence":0.7823933363,"class":0,"name":"person"},{"xmin":671.8472290039,"ymin":432.5200195312,"xmax":810.0,"ymax":877.744934082,"confidence":0.6512392759,"class":0,"name":"person"}]% -``` - - -Test the `/render` endpoint to receive an image with boxes and labels: -```bash -curl -X 'POST' \ - 'http://localhost:3000/render' \ - -H 'accept: image/jpeg' \ - -H 'Content-Type: image/jpeg' \ - --data-binary '@data/bus.jpg' \ - --output './output.jpeg' -``` - -Sample result: - -![output-4](https://user-images.githubusercontent.com/489344/178635310-99dc7fde-5224-4fab-84cf-1a87277a0450.jpeg) - - - -## Build Bento - -The `bentofile.yaml` have configured all required system packages and python dependencies. - -```bash -bentoml build -``` - -Once the Bento is built, containerize it as a Docker image for deployment: - -```bash -bentoml containerize yolo_v5_demo:latest -``` diff --git a/examples/custom_runner/torch_hub_yolov5/bentofile.yaml b/examples/custom_runner/torch_hub_yolov5/bentofile.yaml deleted file mode 100644 index 2707307c1b9..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/bentofile.yaml +++ /dev/null @@ -1,11 +0,0 @@ -service: "service.py:svc" -include: - - "service.py" - - "yolov5s.pt" -python: - requirements_txt: "./requirements.txt" -docker: - system_packages: - - ffmpeg - - libsm6 - - libxext6 diff --git a/examples/custom_runner/torch_hub_yolov5/data/bus.jpg b/examples/custom_runner/torch_hub_yolov5/data/bus.jpg 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zx4)sP*I{!XJ9w8ulR?xT7W;>kZ&6wHsAP~XGtGLBk94o?UByvU#5iC(SDo6x(8xy8 z4NFF`mYSLl4Yip#1d)MU{NF5{Fnud->TpPlJTF|;8KCn%VbFJ^RnbXCSRVPS>+;4} zjy43L5|;AJw(||vj*sK+NX|2Wyz(;U@mA3B3;A-N^ zi_~L2ln9z;7~Bc`J!z`p*PQ1)Di1hggWvR~sVyPE+O+o6EKa8c;EzfOBygabbYLP4 z%ilF3k&yd8F2bl*AxNJijD2ZdKb#)ItZo~+FOyRc~&DMJ%twP z!tD-Oe8xer1K%~Huz3XFeQP=+vQpXaz%_kX{hi#f;Yp^Q*qKYhu!*&6hwiz-6|1P( z09cW^^PXylgCYP=H++ye^{uN<<24hLc>Hugan-%)(QCPo3rBpPp+6rYzq zhL%QEPzV`3)J9Bl!zZW%y=Km+`Hx()79TJBB=xCA(wOb-Rs6+alh&jT4gmYZ^rN)RLmA1CV!3U)+oy7c$5zCTOU|=wFp7m}v3gZBR zdjZaBns(0zJuy}mO~R( U)TTw=tivogI5l>*#uwE8*<&4X;s5{u diff --git a/examples/custom_runner/torch_hub_yolov5/download_model.py b/examples/custom_runner/torch_hub_yolov5/download_model.py deleted file mode 100755 index 94ceb95350f..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/download_model.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python - -import torch - -torch.hub.load("ultralytics/yolov5", "yolov5s") diff --git a/examples/custom_runner/torch_hub_yolov5/requirements.txt b/examples/custom_runner/torch_hub_yolov5/requirements.txt deleted file mode 100644 index ab870bce024..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -Pillow>=7.1.2 -PyYAML>=5.3.1 -bentoml>=1.0.0 -matplotlib>=3.2.2 -numpy>=1.18.5 -opencv-python>=4.1.1 -pandas>=1.1.4 -protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 -ipython -requests>=2.23.0 -scipy>=1.4.1 # Google Colab version -seaborn>=0.11.0 -tensorboard>=2.4.1 -torch>=1.7.0,!=1.12.0 # https://github.com/ultralytics/yolov5/issues/8395 -torchvision>=0.8.1,!=0.13.0 # https://github.com/ultralytics/yolov5/issues/8395 -tqdm>=4.41.0 diff --git a/examples/custom_runner/torch_hub_yolov5/service.py b/examples/custom_runner/torch_hub_yolov5/service.py deleted file mode 100644 index 174a6e863fc..00000000000 --- a/examples/custom_runner/torch_hub_yolov5/service.py +++ /dev/null @@ -1,58 +0,0 @@ -import bentoml -from bentoml.io import Image -from bentoml.io import PandasDataFrame - - -class Yolov5Runnable(bentoml.Runnable): - SUPPORTED_RESOURCES = ("nvidia.com/gpu", "cpu") - SUPPORTS_CPU_MULTI_THREADING = True - - def __init__(self): - import torch - - self.model = torch.hub.load("ultralytics/yolov5:v6.2", "yolov5s") - - if torch.cuda.is_available(): - self.model.cuda() - else: - self.model.cpu() - - # Config inference settings - self.inference_size = 320 - - # Optional configs - # self.model.conf = 0.25 # NMS confidence threshold - # self.model.iou = 0.45 # NMS IoU threshold - # self.model.agnostic = False # NMS class-agnostic - # self.model.multi_label = False # NMS multiple labels per box - # self.model.classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs - # self.model.max_det = 1000 # maximum number of detections per image - # self.model.amp = False # Automatic Mixed Precision (AMP) inference - - @bentoml.Runnable.method(batchable=True, batch_dim=0) - def inference(self, input_imgs): - # Return predictions only - results = self.model(input_imgs, size=self.inference_size) - return results.pandas().xyxy - - @bentoml.Runnable.method(batchable=True, batch_dim=0) - def render(self, input_imgs): - # Return images with boxes and labels - return self.model(input_imgs, size=self.inference_size).render() - - -yolo_v5_runner = bentoml.Runner(Yolov5Runnable, max_batch_size=30) - -svc = bentoml.Service("yolo_v5_demo", runners=[yolo_v5_runner]) - - -@svc.api(input=Image(), output=PandasDataFrame()) -async def invocation(input_img): - batch_ret = await yolo_v5_runner.inference.async_run([input_img]) - return batch_ret[0] - - -@svc.api(input=Image(), output=Image()) -async def render(input_img): - batch_ret = await yolo_v5_runner.render.async_run([input_img]) - return batch_ret[0] diff --git a/examples/custom_web_serving/fastapi_example/README.md b/examples/custom_web_serving/fastapi_example/README.md deleted file mode 100644 index fe044d6aaab..00000000000 --- a/examples/custom_web_serving/fastapi_example/README.md +++ /dev/null @@ -1,68 +0,0 @@ -# BentoML 🤝 FastAPI Demo Project - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train an Iris classifier model, similiar to the quickstart guide: - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -Test the `/predict_bentoml` endpoint: - -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 7.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width": 2.2}' http://127.0.0.1:3000/predict_bentoml - -[2]% -``` - -Now for FastAPI integration endpoints: - -Test the `/predict_fastapi` endpoint: - -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 6.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width": 2.2}' http://127.0.0.1:3000/predict_fastapi - -{"prediction":2}% -``` - -Test the `/predict_fastapi_async` endpoint: - -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 6.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width": 2.2}' http://127.0.0.1:3000/predict_fastapi - -{"prediction":2}% -``` - -Test the custom `/metadata` endpoint - -```bash -$ curl http://127.0.0.1:3000/metadata - -{"name":"iris_clf_with_feature_names","version":"kgjn3haltwvixuqj"}% -``` - - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize iris_fastapi_demo:latest -``` diff --git a/examples/custom_web_serving/fastapi_example/bentofile.yaml b/examples/custom_web_serving/fastapi_example/bentofile.yaml deleted file mode 100644 index 781668e8a6d..00000000000 --- a/examples/custom_web_serving/fastapi_example/bentofile.yaml +++ /dev/null @@ -1,9 +0,0 @@ -service: "service.py:svc" -include: -- "service.py" -python: - packages: - - scikit-learn - - pandas - - fastapi - - pydantic diff --git a/examples/custom_web_serving/fastapi_example/requirements.txt b/examples/custom_web_serving/fastapi_example/requirements.txt deleted file mode 100644 index dff3629010a..00000000000 --- a/examples/custom_web_serving/fastapi_example/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -scikit-learn -fastapi -pydantic -pandas -bentoml>=1.0.0 diff --git a/examples/custom_web_serving/fastapi_example/service.py b/examples/custom_web_serving/fastapi_example/service.py deleted file mode 100644 index 0c106df093c..00000000000 --- a/examples/custom_web_serving/fastapi_example/service.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np -import pandas as pd -from fastapi import FastAPI -from pydantic import BaseModel - -import bentoml -from bentoml.io import JSON -from bentoml.io import NumpyNdarray - - -class IrisFeatures(BaseModel): - sepal_len: float - sepal_width: float - petal_len: float - petal_width: float - - -bento_model = bentoml.sklearn.get("iris_clf_with_feature_names:latest") -iris_clf_runner = bento_model.to_runner() - -svc = bentoml.Service("iris_fastapi_demo", runners=[iris_clf_runner]) - - -@svc.api(input=JSON(pydantic_model=IrisFeatures), output=NumpyNdarray()) -async def predict_bentoml(input_data: IrisFeatures) -> np.ndarray: - input_df = pd.DataFrame([input_data.dict()]) - return await iris_clf_runner.predict.async_run(input_df) - - -fastapi_app = FastAPI() -svc.mount_asgi_app(fastapi_app) - - -@fastapi_app.get("/metadata") -def metadata(): - return {"name": bento_model.tag.name, "version": bento_model.tag.version} - - -# For demo purpose, here's an identical inference endpoint implemented via FastAPI -@fastapi_app.post("/predict_fastapi") -def predict(features: IrisFeatures): - input_df = pd.DataFrame([features.dict()]) - results = iris_clf_runner.predict.run(input_df) - return {"prediction": results.tolist()[0]} - - -# For demo purpose, here's an identical inference endpoint implemented via FastAPI -@fastapi_app.post("/predict_fastapi_async") -async def predict_async(features: IrisFeatures): - input_df = pd.DataFrame([features.dict()]) - results = await iris_clf_runner.predict.async_run(input_df) - return {"prediction": results.tolist()[0]} diff --git a/examples/custom_web_serving/fastapi_example/train.py b/examples/custom_web_serving/fastapi_example/train.py deleted file mode 100644 index 5af911d0b70..00000000000 --- a/examples/custom_web_serving/fastapi_example/train.py +++ /dev/null @@ -1,25 +0,0 @@ -import logging - -import pandas as pd -from sklearn import datasets -from sklearn import svm - -import bentoml - -logging.basicConfig(level=logging.WARN) - -if __name__ == "__main__": - # Load training data - iris = datasets.load_iris() - X = pd.DataFrame( - data=iris.data, columns=["sepal_len", "sepal_width", "petal_len", "petal_width"] - ) - y = iris.target - - # Model Training - clf = svm.SVC() - clf.fit(X, y) - - # Save model to BentoML local model store - saved_model = bentoml.sklearn.save_model("iris_clf_with_feature_names", clf) - print(f"Model saved: {saved_model}") diff --git a/examples/custom_web_serving/flask_example/README.md b/examples/custom_web_serving/flask_example/README.md deleted file mode 100644 index dd50118d1ec..00000000000 --- a/examples/custom_web_serving/flask_example/README.md +++ /dev/null @@ -1,59 +0,0 @@ -# BentoML 🤝 Flask Demo Project - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train an Iris classifier model, similiar to the quickstart guide: - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -Test the `/predict_bentoml` endpoint: - -```bash -$ curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/predict_bentoml - -[2]% -``` - -Now for Flask integration endpoints: - -Test the custom `/metadata` endpoint: - -```bash -$ curl http://127.0.0.1:3000/metadata - -{"name":"iris_clf","version":"3vl5n7qkcwqe5uqj"} -``` - -Test the custom `/predict_flask` endpoint: - -```bash -$ curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/predict_flask - -[2] -``` - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize iris_flask_demo:latest -``` diff --git a/examples/custom_web_serving/flask_example/bentofile.yaml b/examples/custom_web_serving/flask_example/bentofile.yaml deleted file mode 100644 index 5c22af9cdb5..00000000000 --- a/examples/custom_web_serving/flask_example/bentofile.yaml +++ /dev/null @@ -1,8 +0,0 @@ -service: "service.py:svc" -include: -- "service.py" -python: - packages: - - scikit-learn - - pandas - - flask diff --git a/examples/custom_web_serving/flask_example/requirements.txt b/examples/custom_web_serving/flask_example/requirements.txt deleted file mode 100644 index dff3629010a..00000000000 --- a/examples/custom_web_serving/flask_example/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -scikit-learn -fastapi -pydantic -pandas -bentoml>=1.0.0 diff --git a/examples/custom_web_serving/flask_example/service.py b/examples/custom_web_serving/flask_example/service.py deleted file mode 100644 index 04e3ee39288..00000000000 --- a/examples/custom_web_serving/flask_example/service.py +++ /dev/null @@ -1,37 +0,0 @@ -import numpy as np -from flask import Flask -from flask import jsonify -from flask import request - -import bentoml -from bentoml.io import NumpyNdarray - -bento_model = bentoml.sklearn.get("iris_clf:latest") -iris_clf_runner = bento_model.to_runner() - -svc = bentoml.Service("iris_flask_demo", runners=[iris_clf_runner]) - - -@svc.api(input=NumpyNdarray(), output=NumpyNdarray()) -async def predict_bentoml(input_series: np.ndarray) -> np.ndarray: - return await iris_clf_runner.predict.async_run(input_series) - - -flask_app = Flask(__name__) -svc.mount_wsgi_app(flask_app) - - -@flask_app.route("/metadata") -def metadata(): - return {"name": bento_model.tag.name, "version": bento_model.tag.version} - - -# For demo purpose, here's an identical inference endpoint implemented via Flask -@flask_app.route("/predict_flask", methods=["POST"]) -def predict(): - content_type = request.headers.get("Content-Type") - if content_type == "application/json": - input_arr = np.array(request.json, dtype=float) - return jsonify(iris_clf_runner.predict.run(input_arr).tolist()) - else: - return "Content-Type not supported!" diff --git a/examples/custom_web_serving/flask_example/train.py b/examples/custom_web_serving/flask_example/train.py deleted file mode 100644 index 122186740f4..00000000000 --- a/examples/custom_web_serving/flask_example/train.py +++ /dev/null @@ -1,21 +0,0 @@ -import logging - -from sklearn import datasets -from sklearn import svm - -import bentoml - -logging.basicConfig(level=logging.WARN) - -if __name__ == "__main__": - # Load training data - iris = datasets.load_iris() - X, y = iris.data, iris.target - - # Model Training - clf = svm.SVC() - clf.fit(X, y) - - # Save model to BentoML local model store - saved_model = bentoml.sklearn.save_model("iris_clf", clf) - print(f"Model saved: {saved_model}") diff --git a/examples/flax/MNIST/.gitignore b/examples/flax/MNIST/.gitignore deleted file mode 100644 index e6d016a24d7..00000000000 --- a/examples/flax/MNIST/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -events.out* -*.msgpack diff --git a/examples/flax/MNIST/BUILD b/examples/flax/MNIST/BUILD deleted file mode 100644 index 5164c8d8b71..00000000000 --- a/examples/flax/MNIST/BUILD +++ /dev/null @@ -1,18 +0,0 @@ -load("@bazel_skylib//rules:write_file.bzl", "write_file") - -write_file( - name = "_train_sh", - out = "_train.sh", - content = [ - "#!/usr/bin/env bash\n", - "cd $BUILD_WORKING_DIRECTORY\n", - "python -m pip install -r requirements.txt\n", - "python train.py $@", - ], -) - -sh_binary( - name = "train", - srcs = ["_train.sh"], - data = ["train.py"], -) diff --git a/examples/flax/MNIST/README.md b/examples/flax/MNIST/README.md deleted file mode 100644 index 70036db0eb2..00000000000 --- a/examples/flax/MNIST/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# MNIST classifier - -This project demonstrates a simple CNN for MNIST classifier served with BentoML. - -### Instruction - -Run training scripts: - -```bash -# run with python3 -pip install -r requirements.txt -python3 train.py --num-epochs 2 - -# run with bazel -bazel run :train -- --num-epochs 2 -``` - -Serve with either gRPC or HTTP: - -```bash -bentoml serve-grpc --enable-reflection -``` - -Run the test suite: - -```bash -pytest tests -``` - -To run containerize do: - -```bash -bentoml containerize mnist_flax --opt platform=linux/amd64 -``` diff --git a/examples/flax/MNIST/bentofile.yaml b/examples/flax/MNIST/bentofile.yaml deleted file mode 100644 index 87c2bc8b2b1..00000000000 --- a/examples/flax/MNIST/bentofile.yaml +++ /dev/null @@ -1,12 +0,0 @@ -service: "service.py:svc" -labels: - owner: bentoml-team - project: mnist-flax - experiemental: true -include: - - "*.py" -python: - lock_packages: false - extra_index_url: - - https://storage.googleapis.com/jax-releases/jax_releases.html - requirements_txt: ./requirements.txt diff --git a/examples/flax/MNIST/requirements-gpu.txt b/examples/flax/MNIST/requirements-gpu.txt deleted file mode 100644 index ef84171052f..00000000000 --- a/examples/flax/MNIST/requirements-gpu.txt +++ /dev/null @@ -1,10 +0,0 @@ --f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html -jax[cuda]==0.4.4 -flax>=0.6.1 -optax>=0.1.3 -bentoml[grpc,grpc-reflection] -tensorflow -tensorflow-datasets -Pillow -pytest -pytest-asyncio diff --git a/examples/flax/MNIST/requirements.txt b/examples/flax/MNIST/requirements.txt deleted file mode 100644 index c58a04b0ab3..00000000000 --- a/examples/flax/MNIST/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -jax[cpu]==0.4.4 -flax>=0.6.1 -optax>=0.1.3 -bentoml[grpc,grpc-reflection] -tensorflow;platform_system!="Darwin" -tensorflow-macos;platform_system=="Darwin" -tensorflow-datasets -Pillow -pytest -pytest-asyncio diff --git a/examples/flax/MNIST/samples/0.png b/examples/flax/MNIST/samples/0.png deleted file mode 100644 index 7a93ea81868246ab670b0998b1f919c6becdb1b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 290 zcmV+-0p0$IP)C?}1GH8<5{%+pM2`k&OErf#59o8;c?- zdE|{BjLm=Ys~3tSgVfD8J`k40VYGk;0O^TWT0NuJ>4Ozh@fB*mh07*qoM6N<$g5cAGv;Y7A diff --git a/examples/flax/MNIST/samples/1.png b/examples/flax/MNIST/samples/1.png deleted file mode 100644 index 4629a3b16940439e66f1c992caa16eed1fe5c514..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119 zcmeAS@N?(olHy`uVBq!ia0vp^G9b(WBpAZe8a#lMou`XqNX4Aw1c@sNyykh9)9%ae zP`&&A-;v}u>nAGx0F6*2UngC7tDjNU* diff --git a/examples/flax/MNIST/samples/2.png b/examples/flax/MNIST/samples/2.png deleted file mode 100644 index e56adf47bdddc8ab1b02ff8004db08c0db8d8ef4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 285 zcmV+&0pk9NP)7GZwp z<#U~#qyPNDB5Aty4>W{iG2}Sk|M~i`@*+sGv!$iQ4#mu^e{KXaFiZtW{`qk9Xfd*+ z^PjJl3@kbC{(OosH`hip)%i~&184c4Ki{3PIMw;j%vsO>{JB$x&3k(9K~6oZhfR)w j!R^PNYg)Y6<>(0jw0UquY^9}n00000NkvXXu0mjf_3(`M diff --git a/examples/flax/MNIST/samples/3.png b/examples/flax/MNIST/samples/3.png deleted file mode 100644 index d441e3d6dd019458953cda989bb2f4eae15f605e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 218 zcmV<0044v4P)>l;K;YT9lLNzK21PlFjbfB$Ykmt6Me z&vY=O`Olwe=#qc`JP-ymR{nw6jVxIPrhPtRO7i|sZ_t1Q1WOLze>rcE|3X?u~yw>SK|EfY85-amoS1w+pA7@by2t zaZ1kp^T!^ia9g?ZPdAy8<=13zOOgTr9C}v)DAySIu3dZf{qNtu=#uOI{{06O1`(G!(d25r|9yMy>Q(pV zjVr=A(d2BO{(WnO%Ps%^y*9<=(7u0v8*$0Wy#DuIf>#K;r1{@}zhAw5_xjbc5Ol{d zFm%9u@izfOQsdwMe^)b;+PA#<_fH&Ml1*J*od?8_e)~5Gm%|tsmi=9d!WTg1O#izD zMRJQAf-V2?uMLXip?Pp-`oDkAQLUNt$%qNU3V8ANR~D*dz=NQ<$9XXu-CX=khd|1P(bF7wQuu#(f_&u$itav%0~l@pG}g(qJA{r~&B z7N2FiwZq)d8&A*VGyJ{TaCl|i0*4O=^^O|~YE*{F?_R>8k#oDi!d-Iy6VsYWI~`8$ lF0)XH^sfJZOtdJ1fnoWFs%ar#Z5D(4;pyt?jFW%2+3 diff --git a/examples/flax/MNIST/samples/8.png b/examples/flax/MNIST/samples/8.png deleted file mode 100644 index f5e6652c8c1d3b220e2ea8682e89bb0c1890205e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 304 zcmV-00nh%4P)+{~`c}lUX|8 za>p<|#N2tS{S{c!15=WLiG@MG(Y3LWn-Pm7JpceFxVv5wzv{mL0000k8tHIkL>z1Ox>W z6Mp<=pIxk|z`J^`fu~HF(6!8Ne|6@425fC2awjG0|F1u&sad{sCU<|_BL;@G{{jlz S^4~89x!Tj!&t;ucLK6Upep#mg diff --git a/examples/flax/MNIST/service.py b/examples/flax/MNIST/service.py deleted file mode 100644 index 669924bbe8f..00000000000 --- a/examples/flax/MNIST/service.py +++ /dev/null @@ -1,24 +0,0 @@ -from __future__ import annotations - -import typing as t -from typing import TYPE_CHECKING - -import jax.numpy as jnp -from PIL.Image import Image as PILImage - -import bentoml - -if TYPE_CHECKING: - from numpy.typing import NDArray - -mnist_runner = bentoml.flax.get("mnist_flax").to_runner() - -svc = bentoml.Service(name="mnist_flax", runners=[mnist_runner]) - - -@svc.api(input=bentoml.io.Image(), output=bentoml.io.NumpyNdarray()) -async def predict(f: PILImage) -> NDArray[t.Any]: - arr = jnp.array(f) / 255.0 - arr = jnp.expand_dims(arr, (0, 3)) - res = await mnist_runner.async_run(arr) - return res.argmax() diff --git a/examples/flax/MNIST/tests/conftest.py b/examples/flax/MNIST/tests/conftest.py deleted file mode 100644 index 4123305a82e..00000000000 --- a/examples/flax/MNIST/tests/conftest.py +++ /dev/null @@ -1,91 +0,0 @@ -from __future__ import annotations - -import contextlib -import os -import subprocess -import sys -import typing as t -from typing import TYPE_CHECKING - -import psutil -import pytest - -import bentoml -from bentoml._internal.configuration.containers import BentoMLContainer -from bentoml.testing.server import host_bento - -if TYPE_CHECKING: - from contextlib import ExitStack - - from _pytest.config import Config - from _pytest.fixtures import FixtureRequest as _PytestFixtureRequest - from _pytest.main import Session - from _pytest.nodes import Item - - class FixtureRequest(_PytestFixtureRequest): - param: str - - -PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -def pytest_collection_modifyitems( - session: Session, config: Config, items: list[Item] -) -> None: - try: - m = bentoml.models.get("mnist_flax") - print(f"Model exists: {m}") - except bentoml.exceptions.NotFound: - subprocess.check_call( - [ - sys.executable, - f"{os.path.join(PROJECT_DIR, 'train.py')}", - "--num-epochs", - "2", # 2 epochs for faster testing - "--lr", - "0.22", # speed up training time - "--enable-tensorboard", - ] - ) - - -@pytest.fixture(name="enable_grpc", params=[True, False], scope="session") -def fixture_enable_grpc(request: FixtureRequest) -> str: - return request.param - - -@pytest.fixture(scope="session", autouse=True) -def clean_context() -> t.Generator[contextlib.ExitStack, None, None]: - stack = contextlib.ExitStack() - yield stack - stack.close() - - -@pytest.fixture( - name="deployment_mode", - params=["container", "distributed", "standalone"], - scope="session", -) -def fixture_deployment_mode(request: FixtureRequest) -> str: - return request.param - - -@pytest.mark.usefixtures("change_test_dir") -@pytest.fixture(scope="module") -def host( - deployment_mode: t.Literal["container", "distributed", "standalone"], - clean_context: ExitStack, - enable_grpc: bool, -) -> t.Generator[str, None, None]: - if enable_grpc and psutil.WINDOWS: - pytest.skip("gRPC is not supported on Windows.") - - with host_bento( - "service:svc", - deployment_mode=deployment_mode, - project_path=PROJECT_DIR, - bentoml_home=BentoMLContainer.bentoml_home.get(), - clean_context=clean_context, - use_grpc=enable_grpc, - ) as _host: - yield _host diff --git a/examples/flax/MNIST/tests/test_endpoints.py b/examples/flax/MNIST/tests/test_endpoints.py deleted file mode 100644 index 9e9a8e42dc8..00000000000 --- a/examples/flax/MNIST/tests/test_endpoints.py +++ /dev/null @@ -1,71 +0,0 @@ -from __future__ import annotations - -import io -import typing as t - -import numpy as np -import pytest - -import bentoml -from bentoml.testing.grpc import async_client_call -from bentoml.testing.grpc import create_channel - -if t.TYPE_CHECKING: - import jax.numpy as jnp - - -@pytest.fixture() -def img(): - import PIL.Image - - images = {} - digits = list(range(10)) - for digit in digits: - img_path = f"samples/{digit}.png" - with open(img_path, "rb") as f: - img_bytes = f.read() - im = PIL.Image.open(io.BytesIO(img_bytes)) - arr = np.array(im) - images[digit] = { - "bytes": img_bytes, - "pil": im, - "array": arr, - } - - return images - - -@pytest.fixture(name="client") -@pytest.mark.asyncio -def fixture_client(host: str, enable_grpc: bool): - if not enable_grpc: - return bentoml.client.Client.from_url(host) - - -@pytest.mark.asyncio -async def test_image_grpc( - host: str, img: dict[int, dict[str, bytes | jnp.ndarray]], enable_grpc: bool -): - if not enable_grpc: - pytest.skip("Skipping gRPC test when testing on HTTP.") - async with create_channel(host) as channel: - for digit, d in img.items(): - img_bytes = d["bytes"] - await async_client_call( - "predict", - channel=channel, - data={"serialized_bytes": img_bytes}, - assert_data=lambda resp: resp.ndarray.int32_values == [digit], - ) - - -@pytest.mark.asyncio -async def test_image_http( - client: bentoml.client.Client, - img: dict[int, dict[str, bytes | jnp.ndarray]], - enable_grpc: bool, -): - if enable_grpc: - pytest.skip("Skipping HTTP test when testing on gRPC.") - for digit, d in img.items(): - assert await client.async_predict(d["pil"]).item() == digit diff --git a/examples/flax/MNIST/train.py b/examples/flax/MNIST/train.py deleted file mode 100644 index 67e7314b8c1..00000000000 --- a/examples/flax/MNIST/train.py +++ /dev/null @@ -1,243 +0,0 @@ -# modified from https://github.com/google/flax/blob/main/examples/mnist/README.md -from __future__ import annotations - -import argparse -import os -import typing as t -from typing import TYPE_CHECKING - -import attrs -import cattrs -import jax -import jax.numpy as jnp -import numpy as np -import optax -import tensorflow_datasets as tfds -from flax import linen as nn -from flax import serialization -from flax.metrics import tensorboard -from flax.training import train_state - -import bentoml -from bentoml._internal.utils.pkg import pkg_version_info - -if TYPE_CHECKING: - import tensorflow as tf - from flax import core - from jax._src.random import KeyArray - from tensorflow_datasets.core.utils.type_utils import Tree - - from bentoml._internal import external_typing as ext - - NumpyElem = ext.NpNDArray | tf.RaggedTensor - - -@attrs.define -class ConfigDict: - learning_rate: float = 0.1 - batch_size: int = 128 - num_epochs: int = 10 - momentum: float = 0.9 - enable_tensorboard: bool = True - hermetic: bool = True - - def to_dict(self) -> dict[str, t.Any]: - return cattrs.unstructure(self) - - def with_options(self, **kwargs: float | int | bool) -> ConfigDict: - return attrs.evolve(self, **kwargs) - - -_DefaultConfig = ConfigDict() - - -class CNN(nn.Module): - @nn.compact - def __call__(self, x: jnp.ndarray) -> jnp.ndarray: # pylint: disable=W0221 - x = nn.Conv(features=32, kernel_size=(3, 3))(x) - x = nn.relu(x) - x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2)) - x = nn.Conv(features=64, kernel_size=(3, 3))(x) - x = nn.relu(x) - x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2)) - x = x.reshape((x.shape[0], -1)) - x = nn.Dense(features=256)(x) - x = nn.relu(x) - x = nn.Dense(features=10)(x) - return x - - -@jax.jit -def apply_model( - state: train_state.TrainState, image: jnp.DeviceArray, labels: jnp.DeviceArray -) -> tuple[t.Callable[..., core.FrozenDict[str, t.Any]], jnp.ndarray, jnp.ndarray]: - """Compute gradients, loss, and accuracy for a single batch.""" - - def loss_fn(params: core.FrozenDict[str, t.Any]) -> tuple[jnp.ndarray, jnp.ndarray]: - logits: jnp.ndarray = state.apply_fn({"params": params}, image) - one_hot = jax.nn.one_hot(labels, 10) # 0 to 9 - loss: jnp.ndarray = jnp.mean( - optax.softmax_cross_entropy(logits=logits, labels=one_hot) - ) - return loss, logits - - grad = jax.value_and_grad(loss_fn, has_aux=True) - (loss, logits), grad = grad(state.params) - accuracy: jnp.ndarray = jnp.mean(jnp.argmax(logits, -1) == labels) - return grad, loss, accuracy - - -@jax.jit -def update_model(state: train_state.TrainState, grads: core.FrozenDict[str, t.Any]): - return state.apply_gradients(grads=grads) - - -def train_epoch( - state: train_state.TrainState, - train_ds: Tree[NumpyElem], - batch_size: int, - rng: KeyArray, -): - """Train for a single epoch.""" - train_ds_size = len(train_ds["image"]) - steps_per_epoch = train_ds_size // batch_size - - perms = jax.random.permutation(rng, len(train_ds["image"])) - perms = perms[: steps_per_epoch * batch_size] # skip incomplete batch - perms = perms.reshape((steps_per_epoch, batch_size)) - - epoch_loss = [] - epoch_accuracy = [] - - for perm in perms: - batch_images = train_ds["image"][perm, ...] - batch_labels = train_ds["label"][perm, ...] - grads, loss, accuracy = apply_model(state, batch_images, batch_labels) - state = update_model(state, grads) - epoch_loss.append(loss) - epoch_accuracy.append(accuracy) - train_loss = np.mean(epoch_loss) - train_accuracy = np.mean(epoch_accuracy) - return state, train_loss, train_accuracy - - -def get_datasets() -> tuple[Tree[NumpyElem], Tree[NumpyElem]]: - """Load MNIST train and test datasets into memory.""" - ds_builder = tfds.builder("mnist") - ds_builder.download_and_prepare() - train_ds = tfds.as_numpy(ds_builder.as_dataset(split="train", batch_size=-1)) - test_ds = tfds.as_numpy(ds_builder.as_dataset(split="test", batch_size=-1)) - train_ds["image"] = jnp.float32(train_ds["image"]) / 255.0 - test_ds["image"] = jnp.float32(test_ds["image"]) / 255.0 - return train_ds, test_ds - - -def create_train_state(rng: KeyArray, config: ConfigDict) -> train_state.TrainState: - """Creates initial `TrainState`.""" - cnn = CNN() - params = cnn.init(rng, jnp.ones([1, 28, 28, 1]))["params"] - tx = optax.sgd(config.learning_rate, config.momentum) - return train_state.TrainState.create(apply_fn=cnn.apply, params=params, tx=tx) - - -def train_and_evaluate( - config: ConfigDict = _DefaultConfig, workdir: str = "." -) -> train_state.TrainState: - """Execute model training and evaluation loop. - Args: - config: Hyperparameter configuration for training and evaluation. - workdir: Directory where the tensorboard summaries are written to. - Returns: - The train state (which includes the `.params`). - """ - train_ds, test_ds = get_datasets() - rng = jax.random.PRNGKey(0) - summary_writer: tensorboard.SummaryWriter | None = None - - if config.enable_tensorboard: - summary_writer = tensorboard.SummaryWriter(workdir) - summary_writer.hparams(config.to_dict()) - - rng, init_rng = jax.random.split(rng) - state = create_train_state(init_rng, config) - - for epoch in range(1, config.num_epochs + 1): - if not config.hermetic: - rng, input_rng = jax.random.split(rng) - state, train_loss, train_accuracy = train_epoch( - state, train_ds, config.batch_size, input_rng - ) - _, test_loss, test_accuracy = apply_model( - state, test_ds["image"], test_ds["label"] - ) - - print( - "epoch:% 3d, train_loss: %.4f, train_accuracy: %.2f, test_loss: %.4f, test_accuracy: %.2f" - % (epoch, train_loss, train_accuracy * 100, test_loss, test_accuracy * 100) - ) - - if config.enable_tensorboard: - assert summary_writer is not None - summary_writer.scalar("train_loss", train_loss, epoch) - summary_writer.scalar("train_accuracy", train_accuracy, epoch) - summary_writer.scalar("test_loss", test_loss, epoch) - summary_writer.scalar("test_accuracy", test_accuracy, epoch) - - if config.enable_tensorboard: - assert summary_writer is not None - summary_writer.flush() - - return state - - -def load_and_predict(path: str, idx: int = 0): - """ - Load the saved msgpack model and make predictions. - We will run prediction on test MNIST dataset - """ - if not os.path.exists(path): - raise FileNotFoundError(f"File not found at {path}") - with open(path, "rb") as f: - state_dict = serialization.from_bytes(CNN, f.read()) - cnn = CNN() - _, test_ds = get_datasets() - # ensure that all arrays are restored as jnp.ndarray - # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4: - # https://github.com/google/flax/issues/1261 - if pkg_version_info("flax") < (0, 3, 4): - state_dict = jax.tree_util.tree_map(jnp.ndarray, state_dict) - # jit it ! - logits = jax.jit(lambda x: cnn.apply({"params": state_dict["params"]}, x))( - test_ds["image"] - ) - return logits[idx].argmax() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--lr", type=float, default=0.1) - parser.add_argument("--momentum", type=float, default=0.94) - parser.add_argument("--batch-size", type=int, default=64) - parser.add_argument("--num-epochs", type=int, default=10) - parser.add_argument("--enable-tensorboard", action="store_true") - parser.add_argument( - "--hermetic", - action="store_true", - default=False, - help="Whether to use random key", - ) - args = parser.parse_args() - - training_state = train_and_evaluate( - config=_DefaultConfig.with_options( - learning_rate=args.lr, - momentum=args.momentum, - batch_size=args.batch_size, - num_epochs=args.num_epochs, - enable_tensorboard=args.enable_tensorboard, - hermetic=args.hermetic, - ), - ) - - model = bentoml.flax.save_model("mnist_flax", CNN(), training_state) - print(f"Saved model: {model}") diff --git a/examples/fraud_detection/IEEE-CIS-Fraud-Detection.ipynb b/examples/fraud_detection/IEEE-CIS-Fraud-Detection.ipynb deleted file mode 100644 index fb900242a76..00000000000 --- a/examples/fraud_detection/IEEE-CIS-Fraud-Detection.ipynb +++ /dev/null @@ -1,420 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "34ea4354", - "metadata": {}, - "source": [ - "# BentoML Demo - IEEE-CIS Fraud Detection" - ] - }, - { - "cell_type": "markdown", - "id": "b73a62eb", - "metadata": {}, - "source": [ - "Accept dataset rules on Kaggle before downloading: https://www.kaggle.com/competitions/ieee-fraud-detection/data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f82d57c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:50:58.429263Z", - "iopub.status.busy": "2023-03-09T06:50:58.428906Z", - "iopub.status.idle": "2023-03-09T06:50:58.442175Z", - "shell.execute_reply": "2023-03-09T06:50:58.441777Z" - } - }, - "outputs": [], - "source": [ - "# Set Kaggle Credentials for downloading dataset\n", - "%env KAGGLE_USERNAME=\n", - "%env KAGGLE_KEY=" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "945f2734", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:50:58.444818Z", - "iopub.status.busy": "2023-03-09T06:50:58.444485Z", - "iopub.status.idle": "2023-03-09T06:50:58.986259Z", - "shell.execute_reply": "2023-03-09T06:50:58.985255Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ieee-fraud-detection.zip: Skipping, found more recently modified local copy (use --force to force download)\n", - "Archive: ieee-fraud-detection.zip\n", - " inflating: ./data/sample_submission.csv \n", - " inflating: ./data/test_identity.csv \n", - " inflating: ./data/test_transaction.csv \n", - " inflating: ./data/train_identity.csv \n", - " inflating: ./data/train_transaction.csv \n" - ] - } - ], - "source": [ - "!kaggle competitions download -c ieee-fraud-detection\n", - "!rm -rf ./data/\n", - "!unzip -d ./data/ ieee-fraud-detection.zip && rm ieee-fraud-detection.zip" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c49fd861", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:50:58.998981Z", - "iopub.status.busy": "2023-03-09T06:50:58.995168Z", - "iopub.status.idle": "2023-03-09T06:51:07.380130Z", - "shell.execute_reply": "2023-03-09T06:51:07.379697Z" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "df_transactions = pd.read_csv(\"./data/train_transaction.csv\")\n", - "\n", - "X = df_transactions.drop(columns=[\"isFraud\"])\n", - "y = df_transactions.isFraud" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75e1312a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:51:07.383150Z", - "iopub.status.busy": "2023-03-09T06:51:07.382994Z", - "iopub.status.idle": "2023-03-09T06:51:08.059790Z", - "shell.execute_reply": "2023-03-09T06:51:08.058540Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.impute import SimpleImputer\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import (\n", - " StandardScaler,\n", - " OneHotEncoder,\n", - " LabelEncoder,\n", - " OrdinalEncoder,\n", - ")\n", - "from sklearn.feature_selection import SelectPercentile, chi2\n", - "\n", - "numeric_features = df_transactions.select_dtypes(include=\"float64\").columns\n", - "categorical_features = df_transactions.select_dtypes(include=\"object\").columns\n", - "\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", SimpleImputer(strategy=\"median\"), numeric_features),\n", - " (\n", - " \"cat\",\n", - " OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1),\n", - " categorical_features,\n", - " ),\n", - " ],\n", - " verbose_feature_names_out=False,\n", - " remainder=\"passthrough\",\n", - ")\n", - "preprocessor.set_output(transform=\"pandas\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a0d3d70", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:51:08.063015Z", - "iopub.status.busy": "2023-03-09T06:51:08.062784Z", - "iopub.status.idle": "2023-03-09T06:51:24.798036Z", - "shell.execute_reply": "2023-03-09T06:51:24.797677Z" - } - }, - "outputs": [], - "source": [ - "X = preprocessor.fit_transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3efa93f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:51:24.800370Z", - "iopub.status.busy": "2023-03-09T06:51:24.800220Z", - "iopub.status.idle": "2023-03-09T06:51:25.734700Z", - "shell.execute_reply": "2023-03-09T06:51:25.734322Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6e4b919", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:51:25.736827Z", - "iopub.status.busy": "2023-03-09T06:51:25.736679Z", - "iopub.status.idle": "2023-03-09T06:51:25.759545Z", - "shell.execute_reply": "2023-03-09T06:51:25.759232Z" - } - }, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "\n", - "def train(n_estimators, max_depth):\n", - " return xgb.XGBClassifier(\n", - " tree_method=\"hist\",\n", - " n_estimators=n_estimators,\n", - " max_depth=max_depth,\n", - " eval_metric=\"aucpr\",\n", - " objective=\"binary:logistic\",\n", - " enable_categorical=True,\n", - " ).fit(X_train, y_train, eval_set=[(X_test, y_test)])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8944cd0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:51:25.761463Z", - "iopub.status.busy": "2023-03-09T06:51:25.761324Z", - "iopub.status.idle": "2023-03-09T06:52:16.649884Z", - "shell.execute_reply": "2023-03-09T06:52:16.649519Z" - } - }, - "outputs": [], - "source": [ - "# small model with 300 gradient boosted trees and a maximum tree depth of 5\n", - "model_sm = train(300, 5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c549dd2b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:52:16.652081Z", - "iopub.status.busy": "2023-03-09T06:52:16.651916Z", - "iopub.status.idle": "2023-03-09T06:52:17.047092Z", - "shell.execute_reply": "2023-03-09T06:52:17.046693Z" - } - }, - "outputs": [], - "source": [ - "import bentoml\n", - "\n", - "bentoml.xgboost.save_model(\n", - " \"ieee-fraud-detection-sm\",\n", - " model_sm,\n", - " signatures={\n", - " \"predict_proba\": {\"batchable\": True},\n", - " },\n", - " custom_objects={\"preprocessor\": preprocessor},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f22add54", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:52:17.049300Z", - "iopub.status.busy": "2023-03-09T06:52:17.049115Z", - "iopub.status.idle": "2023-03-09T06:52:17.053783Z", - "shell.execute_reply": "2023-03-09T06:52:17.053478Z" - } - }, - "outputs": [], - "source": [ - "model_ref = bentoml.xgboost.get(\"ieee-fraud-detection-sm:latest\")\n", - "model_ref" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efcf15eb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:52:17.055505Z", - "iopub.status.busy": "2023-03-09T06:52:17.055384Z", - "iopub.status.idle": "2023-03-09T06:52:23.709177Z", - "shell.execute_reply": "2023-03-09T06:52:23.708889Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "import bentoml\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "model_ref = bentoml.xgboost.get(\"ieee-fraud-detection-sm:latest\")\n", - "model_runner = model_ref.to_runner()\n", - "model_runner.init_local()\n", - "model_preprocessor = model_ref.custom_objects[\"preprocessor\"]\n", - "\n", - "test_transactions = pd.read_csv(\"./data/test_transaction.csv\")[0:500]\n", - "test_transactions = model_preprocessor.transform(test_transactions)\n", - "result = model_runner.predict_proba.run(test_transactions)\n", - "np.argmax(result, axis=1)" - ] - }, - { - "cell_type": "markdown", - "id": "0e3ac781", - "metadata": {}, - "source": [ - "For the Inference Graph demo, let's train two additional models by tweaking the parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ba5780a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T06:52:23.711339Z", - "iopub.status.busy": "2023-03-09T06:52:23.711233Z", - "iopub.status.idle": "2023-03-09T07:03:17.724144Z", - "shell.execute_reply": "2023-03-09T07:03:17.723796Z" - } - }, - "outputs": [], - "source": [ - "# large model with 3000 gradient boosted trees and a maximum tree depth of 15\n", - "model_lg = train(3000, 15)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "318d54cb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T07:03:17.726234Z", - "iopub.status.busy": "2023-03-09T07:03:17.726122Z", - "iopub.status.idle": "2023-03-09T07:03:17.958983Z", - "shell.execute_reply": "2023-03-09T07:03:17.958688Z" - } - }, - "outputs": [], - "source": [ - "import bentoml\n", - "\n", - "bentoml.xgboost.save_model(\n", - " \"ieee-fraud-detection-lg\",\n", - " model_lg,\n", - " signatures={\n", - " \"predict_proba\": {\"batchable\": True},\n", - " },\n", - " custom_objects={\"preprocessor\": preprocessor},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "959d20ed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T07:03:17.960692Z", - "iopub.status.busy": "2023-03-09T07:03:17.960576Z", - "iopub.status.idle": "2023-03-09T07:03:36.003487Z", - "shell.execute_reply": "2023-03-09T07:03:36.003154Z" - } - }, - "outputs": [], - "source": [ - "# tiny model with 300 gradient boosted trees and a maximum tree depth of 5\n", - "model_tiny = train(100, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e86102e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-03-09T07:03:36.005582Z", - "iopub.status.busy": "2023-03-09T07:03:36.005448Z", - "iopub.status.idle": "2023-03-09T07:03:36.150045Z", - "shell.execute_reply": "2023-03-09T07:03:36.149710Z" - } - }, - "outputs": [], - "source": [ - "import bentoml\n", - "\n", - "bentoml.xgboost.save_model(\n", - " \"ieee-fraud-detection-tiny\",\n", - " model_tiny,\n", - " signatures={\n", - " \"predict_proba\": {\"batchable\": True},\n", - " },\n", - " custom_objects={\"preprocessor\": preprocessor},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "664f1b4e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/fraud_detection/README.md b/examples/fraud_detection/README.md deleted file mode 100644 index 4a719d00785..00000000000 --- a/examples/fraud_detection/README.md +++ /dev/null @@ -1,108 +0,0 @@ -# Fraud Detection Service Example - -1. Install dependencies: -```bash -pip install -r ./dev-requirements.txt -``` - -2. Download dataset from Kaggle - -Before downloading, set up Kaggle API Credentials https://github.com/Kaggle/kaggle-api#api-credentials -and accept dataset rules: https://www.kaggle.com/competitions/ieee-fraud-detection/data - -```bash -./download_data.sh -``` - -3. Train the fraud detection xgboost model. For details, see the ./IEEE-CIS-Fraud-Detection.ipynb - notebook: -```bash -./train.sh -``` - -This creates 3 variations of the model: - -```bash -$ bentoml models list - -Tag Module Size Creation Time -ieee-fraud-detection-tiny:qli6n3f6jcta3uqj bentoml.xgboost 141.40 KiB 2023-03-08 23:03:36 -ieee-fraud-detection-lg:o7wqb5f6jcta3uqj bentoml.xgboost 18.07 MiB 2023-03-08 23:03:17 -ieee-fraud-detection-sm:5yblgmf6i2ta3uqj bentoml.xgboost 723.00 KiB 2023-03-08 22:52:16 -``` - - -4. Run the ML Service locally: -```bash -bentoml serve -``` - -5. Send test requests: - -Visit http://localhost:3000/ in a browser and send test requests via the UI. - -Alternatively, send test payloads via CLI: - -```bash -head --lines=200 ./data/test_transaction.csv | curl -X POST -H 'Content-Type: text/csv' --data-binary @- http://0.0.0.0:3000/is_fraud -``` - -6. Build a docker image for deployment - -Build a Bento to lock the model version and dependency tree: -```bash -bentoml build -``` - -Ensure docker is installed and running, build a docker image with `bentoml containerize` -```bash -bentoml containerize fraud_detection:latest -``` - -Test out the docker image built: - -```bash -docker run -it --rm -p 3000:3000 fraud_detection:{YOUR BENTO VERSION} -``` - -7. Optional: use GPU for model inference - -Use `bentofile-gpu.yaml` to build a new Bento, which adds the following two lines to the YAML. -This ensures the docker image comes with GPU libraries installed and BentoML will automatically -load models on GPU when running the docker image with GPU devices available. - -```yaml -docker: - cuda_version: "11.6.2" -``` - -Build Bento with GPU support: -```bash -bentoml build -f ./bentofile-gpu.yaml -``` - -Build docker image with GPU support: -```bash -bentoml containerize fraud_detection:latest - -docker run --gpus all --device /dev/nvidia0 \ - --device /dev/nvidia-uvm --device /dev/nvidia-uvm-tools \ - --device /dev/nvidia-modeset --device /dev/nvidiactl \ - fraud_detection:{YOUR BENTO VERSION} -``` - -8. Optional: multi-model inference graph - -BentoML makes it efficient to create ML service with multiple ML models. Users can choose to run -models sequentially or in parallel using the Python AsyncIO APIs along with Runners APIs. This makes -it possible create inference graphes or multi-stage inference pipeline all from Python APIs. - -Here's an example that runs all three models simutaneously and aggregate their results: - -```bash -bentoml serve inference_graph_service:svc - -bentoml build -f ./inference_graph_bentofile.yaml -``` - -Sample code available in the `./inference_graph_service.py` file. diff --git a/examples/fraud_detection/benchmark/README.md b/examples/fraud_detection/benchmark/README.md deleted file mode 100644 index 75e5837b8b2..00000000000 --- a/examples/fraud_detection/benchmark/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# Benchmark - -1. Install dependencies: -```bash -pip install -r ./benchmark-requirements.txt -``` - -2. Benchmark with Locust -```bash -bentoml serve service:svc -``` -```bash -locust -H http://0.0.0.0:3000 -u 200 -r 10 -``` - -Visit http://0.0.0.0:8089/ and start the test. - -3. Testing other serving methods - -* BentoML with distributed Runner architecture (default, recommended for most use cases) - ```bash - bentoml serve service:svc - ``` - -* BentoML with embedded local Runner (recommended for light-weight models) - ```bash - bentoml serve service_local_runner:svc - ``` - -* A typical FastAPI implementation with XGBoost syncrounous API for inference: - - ```bash - uvicorn fastapi_main_load_model:app --workers=10 --port=3000 - ``` - -* FastAPI endpoint with BentoML runner async API for inference: - - ```bash - uvicorn fastapi_main_local_runner:app --workers=10 --port=3000 - ``` - -* BentoML deployed on Ray - - serve run ray_deploy:deploy -p 3000 diff --git a/examples/fraud_detection/benchmark/benchmark-requirements.txt b/examples/fraud_detection/benchmark/benchmark-requirements.txt deleted file mode 100644 index 1ee5ff75788..00000000000 --- a/examples/fraud_detection/benchmark/benchmark-requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -numpy -pandas -xgboost -scikit-learn -fastapi -uvicorn[standard] -locust diff --git a/examples/fraud_detection/benchmark/fastapi_main_load_model.py b/examples/fraud_detection/benchmark/fastapi_main_load_model.py deleted file mode 100644 index 5bfe1312d47..00000000000 --- a/examples/fraud_detection/benchmark/fastapi_main_load_model.py +++ /dev/null @@ -1,29 +0,0 @@ -import io - -import numpy as np -import pandas as pd -from fastapi import FastAPI -from fastapi import Request -from sample import sample_input - -import bentoml - -model_tag = "ieee-fraud-detection-lg:latest" -preprocessor = bentoml.xgboost.get(model_tag).custom_objects["preprocessor"] -fraud_model = bentoml.xgboost.load_model(model_tag) - -app = FastAPI() - - -@app.post("/is_fraud") -async def is_fraud(request: Request): - body = await request.body() - input_df = pd.read_json(io.BytesIO(body), dtype=True, orient="records") - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = fraud_model.predict_proba(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return { - "is_fraud": list(map(bool, predictions)), - "is_fraud_prob": results[:, 1].tolist(), - } diff --git a/examples/fraud_detection/benchmark/fastapi_main_local_runner.py b/examples/fraud_detection/benchmark/fastapi_main_local_runner.py deleted file mode 100644 index 0f11113aa9f..00000000000 --- a/examples/fraud_detection/benchmark/fastapi_main_local_runner.py +++ /dev/null @@ -1,30 +0,0 @@ -import io - -import numpy as np -import pandas as pd -from fastapi import FastAPI -from fastapi import Request -from sample import sample_input - -import bentoml - -model_ref = bentoml.xgboost.get("ieee-fraud-detection-lg:latest") -preprocessor = model_ref.custom_objects["preprocessor"] -fraud_model_runner = model_ref.to_runner() -fraud_model_runner.init_local() - -app = FastAPI() - - -@app.post("/is_fraud") -async def is_fraud(request: Request): - body = await request.body() - input_df = pd.read_json(io.BytesIO(body), dtype=True, orient="records") - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = await fraud_model_runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return { - "is_fraud": list(map(bool, predictions)), - "is_fraud_prob": results[:, 1].tolist(), - } diff --git a/examples/fraud_detection/benchmark/locustfile.py b/examples/fraud_detection/benchmark/locustfile.py deleted file mode 100644 index b85efab8951..00000000000 --- a/examples/fraud_detection/benchmark/locustfile.py +++ /dev/null @@ -1,21 +0,0 @@ -import numpy as np -import pandas as pd -from locust import HttpUser -from locust import between -from locust import task - -NUM_OF_ROWS = 500 -test_transactions = pd.read_csv("../data/test_transaction.csv")[0:NUM_OF_ROWS] - -endpoint = "/is_fraud" -# endpoint = "/is_fraud_async" - - -class FraudDetectionUser(HttpUser): - @task - def is_fraud(self): - index = np.random.choice(NUM_OF_ROWS) - input_data = test_transactions[index : index + 1] - self.client.post(endpoint, data=input_data.to_json()) - - wait_time = between(0.01, 2) diff --git a/examples/fraud_detection/benchmark/ray_deploy.py b/examples/fraud_detection/benchmark/ray_deploy.py deleted file mode 100644 index 5fb83d40f2f..00000000000 --- a/examples/fraud_detection/benchmark/ray_deploy.py +++ /dev/null @@ -1,18 +0,0 @@ -import bentoml - -deploy = bentoml.ray.deployment( - "fraud_detection:latest", - {"num_replicas": 5, "ray_actor_options": {"num_cpus": 1}}, - { - "ieee-fraud-detection-sm": { - "num_replicas": 1, - "ray_actor_options": {"num_cpus": 5}, - } - }, - enable_batching=True, - batching_config={ - "ieee-fraud-detection-sm": { - "predict_proba": {"max_batch_size": 5, "batch_wait_timeout_s": 0.2} - } - }, -) diff --git a/examples/fraud_detection/benchmark/sample.py b/examples/fraud_detection/benchmark/sample.py deleted file mode 100644 index be08f55910e..00000000000 --- a/examples/fraud_detection/benchmark/sample.py +++ /dev/null @@ -1,401 +0,0 @@ -import pandas as pd - -sample_json = [ - { - "TransactionID": 2987000, - "TransactionDT": 86400, - "TransactionAmt": 68.5, - "ProductCD": "W", - "card1": 13926, - "card2": None, - "card3": 150.0, - "card4": "discover", - "card5": 142.0, - "card6": "credit", - "addr1": 315.0, - "addr2": 87.0, - "dist1": 19.0, - "dist2": None, - "P_emaildomain": None, - "R_emaildomain": None, - "C1": 1.0, - "C2": 1.0, - "C3": 0.0, - "C4": 0.0, - "C5": 0.0, - "C6": 1.0, - "C7": 0.0, - "C8": 0.0, - "C9": 1.0, - "C10": 0.0, - "C11": 2.0, - "C12": 0.0, - "C13": 1.0, - "C14": 1.0, - "D1": 14.0, - "D2": None, - "D3": 13.0, - "D4": None, - "D5": None, - "D6": None, - "D7": None, - "D8": None, - "D9": None, - "D10": 13.0, - "D11": 13.0, - "D12": None, - "D13": None, - 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- -@svc.api(input=input_spec, output=JSON()) -def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = fraud_model_runner.predict_proba.run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} - - -@svc.api(input=input_spec, output=JSON()) -async def is_fraud_async(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = await fraud_model_runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} diff --git a/examples/fraud_detection/benchmark/service_local_runner.py b/examples/fraud_detection/benchmark/service_local_runner.py deleted file mode 100644 index 2e630e8e76a..00000000000 --- a/examples/fraud_detection/benchmark/service_local_runner.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np -import pandas as pd -from sample import sample_input - -import bentoml -from bentoml.io import JSON -from bentoml.io import PandasDataFrame - -model_ref = bentoml.xgboost.get("ieee-fraud-detection-lg:latest") -preprocessor = model_ref.custom_objects["preprocessor"] -fraud_model_runner = model_ref.to_runner() -fraud_model_runner.init_local() - -svc = bentoml.Service("fraud_detection") - -input_spec = PandasDataFrame.from_sample(sample_input) - - -@svc.api(input=input_spec, output=JSON()) -def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = fraud_model_runner.predict_proba.run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} - - -@svc.api(input=input_spec, output=JSON()) -async def is_fraud_async(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = await fraud_model_runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} diff --git a/examples/fraud_detection/bentofile-gpu.yaml b/examples/fraud_detection/bentofile-gpu.yaml deleted file mode 100644 index 9d3b67c0c08..00000000000 --- a/examples/fraud_detection/bentofile-gpu.yaml +++ /dev/null @@ -1,12 +0,0 @@ -service: "service:svc" -include: -- "service.py" -- "sample.py" -docker: - cuda_version: "11.6.2" -python: - packages: - - numpy - - pandas - - xgboost - - scikit-learn diff --git a/examples/fraud_detection/bentofile.yaml b/examples/fraud_detection/bentofile.yaml deleted file mode 100644 index a6535fa6cb7..00000000000 --- a/examples/fraud_detection/bentofile.yaml +++ /dev/null @@ -1,10 +0,0 @@ -service: "service:svc" -include: -- "service.py" -- "sample.py" -python: - packages: - - numpy - - pandas - - xgboost - - scikit-learn diff --git a/examples/fraud_detection/dev-requirements.txt b/examples/fraud_detection/dev-requirements.txt deleted file mode 100644 index da63880f5b7..00000000000 --- a/examples/fraud_detection/dev-requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -notebook -kaggle -jupyter -numpy -pandas -xgboost -scikit-learn -nbconvert diff --git a/examples/fraud_detection/download_data.sh b/examples/fraud_detection/download_data.sh deleted file mode 100755 index 5303fbc4277..00000000000 --- a/examples/fraud_detection/download_data.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -set -ex - -kaggle competitions download -c ieee-fraud-detection -rm -rf ./data/ -unzip -o -d ./data/ ieee-fraud-detection.zip && rm ieee-fraud-detection.zip diff --git a/examples/fraud_detection/inference_graph_bentofile.yaml b/examples/fraud_detection/inference_graph_bentofile.yaml deleted file mode 100644 index 41d95d26151..00000000000 --- a/examples/fraud_detection/inference_graph_bentofile.yaml +++ /dev/null @@ -1,9 +0,0 @@ -service: "inference_graph_service:svc" -include: -- "*.py" -python: - packages: - - numpy - - pandas - - xgboost - - scikit-learn diff --git a/examples/fraud_detection/inference_graph_service.py b/examples/fraud_detection/inference_graph_service.py deleted file mode 100644 index bc2ed37df3b..00000000000 --- a/examples/fraud_detection/inference_graph_service.py +++ /dev/null @@ -1,53 +0,0 @@ -import asyncio - -import numpy as np -import pandas as pd -from sample import sample_input - -import bentoml -from bentoml.io import JSON -from bentoml.io import PandasDataFrame - -model_ref = bentoml.xgboost.get("ieee-fraud-detection-lg:latest") -preprocessor = model_ref.custom_objects["preprocessor"] - -fraud_model_tiny_runner = bentoml.xgboost.get( - "ieee-fraud-detection-tiny:latest" -).to_runner() -fraud_model_small_runner = bentoml.xgboost.get( - "ieee-fraud-detection-sm:latest" -).to_runner() -fraud_model_large_runner = bentoml.xgboost.get( - "ieee-fraud-detection-lg:latest" -).to_runner() - -svc = bentoml.Service( - "fraud_detection_inference_graph", - runners=[ - fraud_model_tiny_runner, - fraud_model_small_runner, - fraud_model_large_runner, - ], -) - -input_spec = PandasDataFrame.from_sample(sample_input) - - -async def _is_fraud_async( - runner: bentoml.Runner, - input_df: pd.DataFrame, -): - results = await runner.predict_proba.async_run(input_df) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} - - -@svc.api(input=input_spec, output=JSON()) -async def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_df = preprocessor.transform(input_df) - return await asyncio.gather( - _is_fraud_async(fraud_model_tiny_runner, input_df), - _is_fraud_async(fraud_model_small_runner, input_df), - _is_fraud_async(fraud_model_large_runner, input_df), - ) diff --git a/examples/fraud_detection/sample.py b/examples/fraud_detection/sample.py deleted file mode 100644 index be08f55910e..00000000000 --- a/examples/fraud_detection/sample.py +++ /dev/null @@ -1,401 +0,0 @@ -import pandas as pd - -sample_json = [ - { - "TransactionID": 2987000, - "TransactionDT": 86400, - "TransactionAmt": 68.5, - "ProductCD": "W", - "card1": 13926, - "card2": None, - "card3": 150.0, - "card4": "discover", - "card5": 142.0, - "card6": "credit", - "addr1": 315.0, - "addr2": 87.0, - "dist1": 19.0, - "dist2": None, - "P_emaildomain": None, - "R_emaildomain": None, - "C1": 1.0, - "C2": 1.0, - "C3": 0.0, - "C4": 0.0, - "C5": 0.0, - "C6": 1.0, - "C7": 0.0, - "C8": 0.0, - "C9": 1.0, - 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-svc = bentoml.Service("fraud_detection", runners=[fraud_model_runner]) - -input_spec = PandasDataFrame.from_sample(sample_input) - - -@svc.api(input=input_spec, output=JSON()) -async def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - input_features = preprocessor.transform(input_df) - results = await fraud_model_runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return {"is_fraud": list(map(bool, predictions)), "is_fraud_prob": results[:, 1]} diff --git a/examples/fraud_detection/submit_test_requests.sh b/examples/fraud_detection/submit_test_requests.sh deleted file mode 100644 index 316940f9f23..00000000000 --- a/examples/fraud_detection/submit_test_requests.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -head --lines=200 ./data/test_transaction.csv | curl -X POST -H 'Content-Type: text/csv' --data-binary @- http://0.0.0.0:3000/is_fraud diff --git a/examples/fraud_detection/train.sh b/examples/fraud_detection/train.sh deleted file mode 100755 index de8aaef797d..00000000000 --- a/examples/fraud_detection/train.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -jupyter nbconvert --to notebook --inplace --execute ./IEEE-CIS-Fraud-Detection.ipynb --debug 2>&1 | grep -v '^\[NbConvertApp\]' diff --git a/examples/flax/MNIST/.bentoignore b/examples/inference-graph/.bentoignore similarity index 100% rename from examples/flax/MNIST/.bentoignore rename to examples/inference-graph/.bentoignore diff --git a/examples/inference_graph/README.md b/examples/inference-graph/README.md similarity index 100% rename from examples/inference_graph/README.md rename to examples/inference-graph/README.md diff --git a/examples/inference_graph/bentofile.yaml b/examples/inference-graph/bentofile.yaml similarity index 100% rename from examples/inference_graph/bentofile.yaml rename to examples/inference-graph/bentofile.yaml diff --git a/examples/inference_graph/requirements.txt b/examples/inference-graph/requirements.txt similarity index 100% rename from examples/inference_graph/requirements.txt rename to examples/inference-graph/requirements.txt diff --git a/examples/inference_graph/service.py b/examples/inference-graph/service.py similarity index 100% rename from examples/inference_graph/service.py rename to examples/inference-graph/service.py diff --git a/examples/inference_graph/.bentoignore b/examples/inference_graph/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/inference_graph/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/kfserving/.bentoignore b/examples/kfserving/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/kfserving/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/kfserving/bentofile.yaml b/examples/kfserving/bentofile.yaml deleted file mode 100644 index 2dd801b72bf..00000000000 --- a/examples/kfserving/bentofile.yaml +++ /dev/null @@ -1,11 +0,0 @@ -service: "service.py:svc" -labels: - owner: bentoml-team - project: gallery -include: -- "*.py" -python: - packages: - - scikit-learn - - pandas - - pydantic diff --git a/examples/kfserving/custom_kfserving_deployment.yaml b/examples/kfserving/custom_kfserving_deployment.yaml deleted file mode 100644 index 72a666c6279..00000000000 --- a/examples/kfserving/custom_kfserving_deployment.yaml +++ /dev/null @@ -1,9 +0,0 @@ -apiVersion: serving.kserve.io/v1beta1 -kind: InferenceService -metadata: - name: bentoml-custom-model -spec: - predictor: - containers: - - name: bentoml-kserve-container - image: {username}/iris_classifier:latest diff --git a/examples/kfserving/requirements.txt b/examples/kfserving/requirements.txt deleted file mode 100644 index 14c29cdbe36..00000000000 --- a/examples/kfserving/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -scikit-learn -pandas -bentoml>=1.0.0 -pydantic diff --git a/examples/kfserving/service.py b/examples/kfserving/service.py deleted file mode 100644 index 2d132f442de..00000000000 --- a/examples/kfserving/service.py +++ /dev/null @@ -1,29 +0,0 @@ -from typing import List - -import numpy as np -import pydantic - -import bentoml -from bentoml.io import JSON -from bentoml.io import NumpyNdarray - -iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner() - -svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner]) - - -class KFServingInputSchema(pydantic.BaseModel): - instances: List[List[float]] - - -kfserving_input = JSON(pydantic_model=KFServingInputSchema) - - -@svc.api( - input=kfserving_input, - output=NumpyNdarray(), - route="v1/models/iris_classifier", -) -async def classify(kf_input: KFServingInputSchema) -> np.ndarray: - instances = np.array(kf_input.instances) - return await iris_clf_runner.predict.async_run(instances) diff --git a/examples/kfserving/train.py b/examples/kfserving/train.py deleted file mode 100644 index 122186740f4..00000000000 --- a/examples/kfserving/train.py +++ /dev/null @@ -1,21 +0,0 @@ -import logging - -from sklearn import datasets -from sklearn import svm - -import bentoml - -logging.basicConfig(level=logging.WARN) - -if __name__ == "__main__": - # Load training data - iris = datasets.load_iris() - X, y = iris.data, iris.target - - # Model Training - clf = svm.SVC() - clf.fit(X, y) - - # Save model to BentoML local model store - saved_model = bentoml.sklearn.save_model("iris_clf", clf) - print(f"Model saved: {saved_model}") diff --git a/examples/kubeflow/README.md b/examples/kubeflow/README.md deleted file mode 100644 index 6168f0e4d13..00000000000 --- a/examples/kubeflow/README.md +++ /dev/null @@ -1,312 +0,0 @@ -# Developing BentoML Applications on Kubeflow - -Starting with the release of Kubeflow 1.7, BentoML provides a native integration with Kubeflow. This integration allows you to package models trained in Kubeflow Notebooks or Pipelines as [Bentos](https://docs.bentoml.com/en/latest/concepts/bento.html), and deploy them as microservices in the Kubernetes cluster through BentoML's cloud native components and custom resource definitions (CRDs). This documentation provides a comprehensive guide on how to use BentoML and Kubeflow together to streamline the process of deploying models at scale. - -In this example, we will train three fraud detection models using the Kubeflow notebook and the [Kaggle IEEE-CIS Fraud Detection dataset](https://www.kaggle.com/c/ieee-fraud-detection). We will then create a BentoML service that can simultaneously invoke all three models and return a decision on whether a transaction is fraudulent and build it into a Bento. We will showcase two deployment workflows using BentoML's Kubernetes operators: deploying directly from the Bento, and deploying from an OCI image built from the Bento. - -## Prerequisites - -Install Kubeflow and BentoML resources to the Kubernetes cluster. See [Kubeflow](https://github.com/kubeflow/manifests) and [BentoML](https://github.com/kubeflow/manifests/tree/master/contrib/bentoml) manifests installation guides for details. - -After BentoML Kubernetes resources are installed successfully, you should have the following CRDs in the namespace. - -```bash -> kubectl -n kubeflow get crds | grep bento -bentodeployments.serving.yatai.ai 2022-12-22T18:46:46Z -bentoes.resources.yatai.ai 2022-12-22T18:46:47Z -bentorequests.resources.yatai.ai 2022-12-22T18:46:47Z -``` - -Install the required packages to run this example. - -```bash -git clone --depth 1 https://github.com/bentoml/BentoML -cd BentoML/examples/kubeflow -pip install -r requirements.txt -``` - -## Download Kaggle Dataset - -Set Kaggle [user credentials](https://github.com/Kaggle/kaggle-api#api-credentials) for API access. Accepting the [rules of the competition](https://www.kaggle.com/competitions/ieee-fraud-detection/rules) is required for downloading the dataset. - -```bash -export KAGGLE_USERNAME= -export KAGGLE_KEY= -``` - -Download Kaggle dataset. - -```bash -kaggle competitions download -c ieee-fraud-detection -rm -rf ./data/ -unzip -d ./data/ ieee-fraud-detection.zip && rm ieee-fraud-detection.zip -``` - -## Train Models - -In this demonstration, we'll train three fraud detection models using the Kaggle IEEE-CIS Fraud Detection dataset. To showcase saving and serving multiple models with Kubeflow and BentoML, we'll split the dataset into three equal-sized chunks and use each chunk to train a separate model. While this approach has no practical benefits, it will help illustrate how to save and serve multiple models with Kubeflow and BentoML. This step can also be run from the `notebook.ipynb` included in this directory. - -```python -import pandas as pd - -df_transactions = pd.read_csv("./data/train_transaction.csv") - -X = df_transactions.drop(columns=["isFraud"]) -y = df_transactions.isFraud -``` - -Define the preprocessor. - -```python -from sklearn.impute import SimpleImputer -from sklearn.compose import ColumnTransformer -from sklearn.impute import SimpleImputer -from sklearn.preprocessing import OrdinalEncoder - -numeric_features = df_transactions.select_dtypes(include="float64").columns -categorical_features = df_transactions.select_dtypes(include="object").columns - -preprocessor = ColumnTransformer( - transformers=[ - ("num", SimpleImputer(strategy="median"), numeric_features), - ( - "cat", - OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1), - categorical_features, - ), - ], - verbose_feature_names_out=False, - remainder="passthrough", -) - -X = preprocessor.fit_transform(X) -``` - -Define our training function with the number of boosting rounds and maximum depths. - -```python -import xgboost as xgb - -def train(n_estimators, max_depth): - return xgb.XGBClassifier( - tree_method="hist", - n_estimators=n_estimators, - max_depth=max_depth, - eval_metric="aucpr", - objective="binary:logistic", - enable_categorical=True, - ).fit(X_train, y_train, eval_set=[(X_test, y_test)]) -``` - -We will divide the training data into three equal-sized chunks and treat them as independent data sets. Based on these data sets, we will train three separate fraud detection models. The trained model will be saved to the local model store using BentoML model saving API. - -```python -import bentoml - -from sklearn.model_selection import train_test_split - -CHUNKS = 3 -CHUNK_SIZE = len(X) // CHUNKS - -for i in range(CHUNKS): - START = i * CHUNK_SIZE - END = (i + 1) * CHUNK_SIZE - X_train, X_test, y_train, y_test = train_test_split(X[START:END], y[START:END]) - - name = f"ieee-fraud-detection-{i}" - model = train(10, 5) - score = model.score(X_test, y_test) - print(f"Successfully trained model {name} with score {score}.") - - bentoml.xgboost.save_model( - name, - model, - signatures={ - "predict_proba": {"batchable": True}, - }, - custom_objects={"preprocessor": preprocessor}, - ) - print(f"Successfully saved model {name} to the local model store.") -``` - -Saved models can be loaded back into the memory and debugged in the notebook. - -```python -import bentoml -import pandas as pd -import numpy as np - -model_ref = bentoml.xgboost.get("ieee-fraud-detection-0:latest") -model_runner = model_ref.to_runner() -model_runner.init_local() -model_preprocessor = model_ref.custom_objects["preprocessor"] - -test_transactions = pd.read_csv("./data/test_transaction.csv")[0:500] -test_transactions = model_preprocessor.transform(test_transactions) -result = model_runner.predict_proba.run(test_transactions) -np.argmax(result, axis=1) -``` - -## Define Service API - -After the models are built and scored, let's create the service definition. You can find the service definition in the `service.py` module in this example. Let's breakdown the `service.py` module and explain what each section does. - -First, we will create a list of preprocessors and runners from the three models we saved earlier. Runners are abstractions of the model inferences that can be scaled independently. See [Using Runners](https://docs.bentoml.com/en/latest/concepts/runner.html) for more details. - -```python -fraud_detection_preprocessors = [] -fraud_detection_runners = [] - -for model_name in ["ieee-fraud-detection-0", "ieee-fraud-detection-1", "ieee-fraud-detection-2"]: - model_ref = bentoml.xgboost.get(model_name) - fraud_detection_preprocessors.append(model_ref.custom_objects["preprocessor"]) - fraud_detection_runners.append(model_ref.to_runner()) -``` - -Next, we will create a service with the list of runners passed in. - -```python -svc = bentoml.Service("fraud_detection", runners=fraud_detection_runners) -``` - -Finally, we will create the API function `is_fraud`. We'll use the `@api` decorator to declare that the function is an API and specify the input and output types as pandas.DataFrame and JSON, respectively. The function is defined as `async` so that the inference calls to the runners can happen simultaneously without waiting for the results to return before calling the next runner. The inner function `_is_fraud` defines the model inference logic for each runner. All runners are called simultaneously through the `asyncio.gather` function and the results are aggregated into a list. The function will return True if any of the models return True. - -```python -@svc.api(input=PandasDataFrame.from_sample(sample_input), outp1t=JSON()) -async def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - - async def _is_fraud(preprocessor, runner, input_df): - input_features = preprocessor.transform(input_df) - results = await runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return bool(predictions[0]) - - # Simultaeously run all models - results = await asyncio.gather( - *[ - _is_fraud(p, r, input_df) - for p, r in zip(fraud_detection_preprocessors, fraud_detection_runners) - ] - ) - - # Return fraud if at least one model returns fraud - return any(results) -``` - -For more about service definitions, please see [Service and APIs](https://docs.bentoml.com/en/latest/concepts/service.html). - -## Build Service - -Building the service and models into a bento allows it to be distributed among collaborators, containerized into a OCI image, and deployed in the Kubernetes cluster. To build a service into a bento, we first need to define the `bentofile.yaml` file. See [Building Bentos](https://docs.bentoml.com/en/latest/concepts/bento.html) for more options. - -```yaml -service: "service:svc" -include: -- "service.py" -- "sample.py" -python: - requirements_txt: ./requirements.txt -``` - -Running the following command will build the service into a bento and store it to the local bento store. - -```bash -bentoml build -``` - -## Serve Bento - -Serving the bento will bring up a service endpoint in HTTP or gRPC for the service API we defined. Use `--help` to see more serving options. - -```bash -bentoml serve-http -``` - -## Deploy to Kubernetes Cluster - -BentoML offers three custom resource definitions (CRDs) in the Kubernetes cluster. - -- [BentoRequest](https://docs.bentoml.com/projects/yatai/en/latest/concepts/bentorequest_crd.html) - Describes the metadata needed for building the container image of the Bento, such as the download URL. Created by the user. -- [Bento](https://docs.bentoml.com/projects/yatai/en/latest/concepts/bento_crd.html) - Describes the metadata for the Bento such as the address of the image and the runners. Created by users or by the `yatai-image-builder` operator for reconsiliating `BentoRequest` resources. -- [BentoDeployment](https://docs.bentoml.com/projects/yatai/en/latest/concepts/bentodeployment_crd.html) - Describes the metadata of the deployment such as resources and autoscaling behaviors. Reconciled by the `yatai-deployment` operator to create Kubernetes deployments of API Servers and Runners. - -![image](https://raw.githubusercontent.com/bentoml/BentoML/main/docs/source/_static/img/kubeflow-crds.png) - -Next, we will demonstrate two ways of deployment. - -1. Deploying using a `BentoRequest` resource by providing a Bento -2. Deploying Using a `Bento` resource by providing a pre-built container image from a Bento - -### Deploy with BentoRequest CRD - -In this workflow, we will export the Bento to a remote storage, and then use the `yatai-image-builder` operator to containerize it. Finally, we'll deploy the containerized Bento image using the `yatai-deployment` operator. Using the `yatai-image-builder` operator to download Bentos from AWS and push the containerized OCI image will require setting up the credentials of S3 bucket and container repository. See the [manifests installation guide](https://github.com/kubeflow/manifests/tree/master/contrib/bentoml) for detailed instructions. - -Push the Bento built and saved in the local Bento store to a cloud storage such as AWS S3. - -```bash -bentoml export fraud_detection:o5smnagbncigycvj s3://your_bucket/fraud_detection.bento -``` - -Apply the `BentoRequest` and `BentoDeployment` resources as defined in `deployment_from_bentorequest.yaml` included in this example. - -```bash -kubectl apply -f deployment_from_bentorequest.yaml -``` - -Once the resources are created, the `yatai-image-builder` operator will reconcile the `BentoRequest` resource and spawn a pod to download and build the container image from the provided Bento defined in the resource. The `yatai-image-builder` operator will push the built image to the container registry specified during the installation and create a `Bento` resource with the same name. At the same time, the `yatai-deployment` operator will reconcile the `BentoDeployment` resource with the provided name and create Kubernetes deployments of API Servers and Runners from the container image specified in the `Bento` resource. - -### Deploym with Bento CRD - -In this workflow, we'll create an OCI image from a Bento and upload the image to a container repository. After that, we'll use the `yatai-deployment` operator to deploy the Bento OCI image. - -Containerize the image through `containerize` sub-command. - -```bash -bentoml containerize fraud_detection:o5smnagbncigycvj -t your-username/fraud_detection:o5smnagbncigycvj -``` - -Push the containerized Bento image to a remote repository of your choice. - -```bash -docker push your-username/fraud_detection:o5smnagbncigycvj -``` - -Apply the `Bento` and `BentoDeployment` resources as defined in `deployment_from_bento.yaml` file included in this example. - -```bash -kubectl apply -f deployment_from_bento.yaml -``` - -Once the resources are created, the `yatai-deployment` operator will reconcile the `BentoDeployment` resource with the provided name and create Kubernetes deployments of API Servers and Runners from the container image specified in the `Bento` resource. - -## Verify Deployment - -Verify the deployment of API Servers and Runners. Note that API server and runners are run in separate pods and created in separate deployments that can be scaled independently. - -```bash -kubectl -n kubeflow get pods -l yatai.ai/bento-deployment=fraud-detection - -NAME READY STATUS RESTARTS AGE -fraud-detection-67f84686c4-9zzdz 4/4 Running 0 10s -fraud-detection-runner-0-86dc8b5c57-q4c9f 3/3 Running 0 10s -fraud-detection-runner-1-846bdfcf56-c5g6m 3/3 Running 0 10s -fraud-detection-runner-2-6d48794b7-xws4j 3/3 Running 0 10s -``` - -![image](https://raw.githubusercontent.com/bentoml/BentoML/main/docs/source/_static/img/kubeflow-fraud-detection.png) - -Port forward the Fraud Detection service to test locally. You should be able to visit the Swagger page of the service by requesting http://0.0.0.0:8080 while port forwarding. - -```bash -kubectl -n kubeflow port-forward svc/fraud-detection 8080:3000 --address 0.0.0.0 -``` - -## Conclusion - -Congratulations! You completed the example. Let's recap what we have learned. - -- Trained three fraud detection models and saved them to the BentoML model store. -- Created a BentoML service that runs inferences on all three models simultaneously, combines and returns the results. -- Containerized the BentoML service into an OCI image and pushed the image to a remote repository. -- Created BentoML CRDs on Kubernetes to deploy the Bento in a microservice architecture. diff --git a/examples/kubeflow/bentofile.yaml b/examples/kubeflow/bentofile.yaml deleted file mode 100644 index fe1ea327ee9..00000000000 --- a/examples/kubeflow/bentofile.yaml +++ /dev/null @@ -1,6 +0,0 @@ -service: "service:svc" -include: -- "service.py" -- "sample.py" -python: - requirements_txt: ./requirements.txt diff --git a/examples/kubeflow/deployment_from_bento.yaml b/examples/kubeflow/deployment_from_bento.yaml deleted file mode 100644 index 8ddb69772a4..00000000000 --- a/examples/kubeflow/deployment_from_bento.yaml +++ /dev/null @@ -1,97 +0,0 @@ -apiVersion: resources.yatai.ai/v1alpha1 -kind: Bento -metadata: - name: fraud-detection - namespace: kubeflow -spec: - image: docker.io/bentoml/fraud_detection:o5smnagbncigycvj - runners: - - name: ieee-fraud-detection-0 - runnableType: XGBoost - - name: ieee-fraud-detection-1 - runnableType: XGBoost - - name: ieee-fraud-detection-2 - runnableType: XGBoost - tag: fraud_detection:o5smnagbncigycvj ---- -apiVersion: serving.yatai.ai/v2alpha1 -kind: BentoDeployment -metadata: - name: fraud-detection - namespace: kubeflow -spec: - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - bento: fraud-detection - ingress: - enabled: false - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - runners: - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-0 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-1 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-2 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi diff --git a/examples/kubeflow/deployment_from_bentorequest.yaml b/examples/kubeflow/deployment_from_bentorequest.yaml deleted file mode 100644 index a3a92efe878..00000000000 --- a/examples/kubeflow/deployment_from_bentorequest.yaml +++ /dev/null @@ -1,94 +0,0 @@ -apiVersion: resources.yatai.ai/v1alpha1 -kind: BentoRequest -metadata: - name: fraud-detection - namespace: kubeflow -spec: - bentoTag: fraud_detection:o5smnagbncigycvj - downloadUrl: s3://bentoml.com/kubeflow/fraud_detection.bento - runners: - - name: ieee-fraud-detection-0 - - name: ieee-fraud-detection-1 - - name: ieee-fraud-detection-2 ---- -apiVersion: serving.yatai.ai/v2alpha1 -kind: BentoDeployment -metadata: - name: fraud-detection - namespace: kubeflow -spec: - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - bento: fraud-detection - ingress: - enabled: false - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - runners: - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-0 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-1 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi - - autoscaling: - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 80 - type: Utilization - type: Resource - minReplicas: 1 - name: ieee-fraud-detection-2 - resources: - limits: - cpu: 1000m - memory: 1024Mi - requests: - cpu: 100m - memory: 200Mi diff --git a/examples/kubeflow/notebook.ipynb b/examples/kubeflow/notebook.ipynb deleted file mode 100644 index 5ec3c47f0f1..00000000000 --- a/examples/kubeflow/notebook.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# BentoML Kubeflow Notebook Example\n", - "\n", - "In this example, we will train three fraud detection models using the [Kaggle IEEE-CIS Fraud Detection dataset](https://www.kaggle.com/c/ieee-fraud-detection) using the Kubeflow notebook and create a BentoML service that simultaneously invoke all three models and returns the decision if any one of the models predicts that a transactin is a fraud. We will build and push the BentoML service to an S3 bucket. Next we will containerize BentoML service from the S3 bucket and deploy the service to Kubeflow cluster using using BentoML custom resource definitions on Kubernetes. The service will be deployed in a microservice architecture with each model running in a separate pod, deployed on hardware that is the most ideal for running the model, and scale independently." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "This guide assume that Kubeflow is already installed in Kubernetes cluster. See [Kubeflow Manifests](https://github.com/kubeflow/manifests) for installation instructions.\n", - "\n", - "Install BentoML cloud native components and custom resource definitions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! kustomize build bentoml-yatai-stack/default | kubectl apply -n kubeflow --server-side -f -" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Install the required packages to run this example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! pip install -r requirements.txt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Kaggle Dataset\n", - "\n", - "Set Kaggle username and key as environment variables. Accepting the [rules of the competition](https://www.kaggle.com/competitions/ieee-fraud-detection/rules) is required for downloading the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set Kaggle Credentials for downloading dataset\n", - "%env KAGGLE_USERNAME=\n", - "%env KAGGLE_KEY=" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!kaggle competitions download -c ieee-fraud-detection\n", - "!rm -rf ./data/\n", - "!unzip -d ./data/ ieee-fraud-detection.zip && rm ieee-fraud-detection.zip" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Models\n", - "\n", - "In this demonstration, we'll train three fraud detection models using the Kaggle IEEE-CIS Fraud Detection dataset. To showcase saving and serving multiple models with Kubeflow and BentoML, we'll split the dataset into three equal-sized chunks and use each chunk to train a separate model. While this approach has no practical benefits, it will help illustrate how to save and serve multiple models with Kubeflow and BentoML." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "df_transactions = pd.read_csv(\"./data/train_transaction.csv\")\n", - "\n", - "X = df_transactions.drop(columns=[\"isFraud\"])\n", - "y = df_transactions.isFraud" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.impute import SimpleImputer\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import OrdinalEncoder\n", - "\n", - "numeric_features = df_transactions.select_dtypes(include=\"float64\").columns\n", - "categorical_features = df_transactions.select_dtypes(include=\"object\").columns\n", - "\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", SimpleImputer(strategy=\"median\"), numeric_features),\n", - " (\n", - " \"cat\",\n", - " OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1),\n", - " categorical_features,\n", - " ),\n", - " ],\n", - " verbose_feature_names_out=False,\n", - " remainder=\"passthrough\",\n", - ")\n", - "\n", - "X = preprocessor.fit_transform(X)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define our training function with the number of boosting rounds and maximum depths." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "\n", - "\n", - "def train(n_estimators, max_depth, X_train, y_train, X_test, y_test):\n", - " return xgb.XGBClassifier(\n", - " tree_method=\"hist\",\n", - " n_estimators=n_estimators,\n", - " max_depth=max_depth,\n", - " eval_metric=\"aucpr\",\n", - " objective=\"binary:logistic\",\n", - " enable_categorical=True,\n", - " ).fit(X_train, y_train, eval_set=[(X_test, y_test)])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will divide the training data into three equal-sized chunks and treat them as independent data sets. Based on these data sets, we will train three separate fraud detection models. The trained model will be saved to the local model store using BentoML model saving API." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import bentoml\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "CHUNKS = 3\n", - "CHUNK_SIZE = len(X) // CHUNKS\n", - "\n", - "for i in range(CHUNKS):\n", - " START = i * CHUNK_SIZE\n", - " END = (i + 1) * CHUNK_SIZE\n", - " X_train, X_test, y_train, y_test = train_test_split(X[START:END], y[START:END])\n", - "\n", - " name = f\"ieee-fraud-detection-{i}\"\n", - " model = train(100, 5, X_train, y_train, X_test, y_test)\n", - " score = model.score(X_test, y_test)\n", - " print(f\"Successfully trained model {name} with score {score}.\")\n", - "\n", - " bentoml.xgboost.save_model(\n", - " name,\n", - " model,\n", - " signatures={\n", - " \"predict_proba\": {\"batchable\": True},\n", - " },\n", - " custom_objects={\"preprocessor\": preprocessor},\n", - " )\n", - " print(f\"Successfully saved model {name} to the local model store.\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Saved models can be loaded back into the memory and debugged in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import bentoml\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "model_ref = bentoml.xgboost.get(\"ieee-fraud-detection-0:latest\")\n", - "model_runner = model_ref.to_runner()\n", - "model_runner.init_local()\n", - "model_preprocessor = model_ref.custom_objects[\"preprocessor\"]\n", - "\n", - "test_transactions = pd.read_csv(\"./data/test_transaction.csv\")[0:500]\n", - "test_transactions = model_preprocessor.transform(test_transactions)\n", - "result = model_runner.predict_proba.run(test_transactions)\n", - "np.argmax(result, axis=1)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Service API\n", - "\n", - "After the models are built and scored, let's create the service definition. You can find the service definition in the `service.py` module in this example. Let's breakdown the `service.py` module and explain what each section does.\n", - "\n", - "First, we will create a list of preprocessors and runners from the three models we saved earlier. Runners are abstractions of the model inferences that can be scaled independently. See [Using Runners](https://docs.bentoml.com/en/latest/concepts/runner.html) for more details.\n", - "\n", - "```python\n", - "fraud_detection_preprocessors = []\n", - "fraud_detection_runners = []\n", - "\n", - "for model_name in [\"ieee-fraud-detection-0\", \"ieee-fraud-detection-1\", \"ieee-fraud-detection-2\"]:\n", - " model_ref = bentoml.xgboost.get(model_name)\n", - " fraud_detection_preprocessors.append(model_ref.custom_objects[\"preprocessor\"])\n", - " fraud_detection_runners.append(model_ref.to_runner())\n", - "```\n", - "\n", - "Next, we will create a service with the list of runners passed in.\n", - "\n", - "```python\n", - "svc = bentoml.Service(\"fraud_detection\", runners=fraud_detection_runners)\n", - "```\n", - "\n", - "Finally, we will create the API function `is_fraud`. We'll use the `@api` decorator to declare that the function is an API and specify the input and output types as pandas.DataFrame and JSON, respectively. The function is defined as `async` so that the inference calls to the runners can happen simultaneously without waiting for the results to return before calling the next runner. The inner function `_is_fraud` defines the model inference logic for each runner. All runners are called simultaneously through the `asyncio.gather` function and the results are aggregated into a list. The function will return True if any of the models return True.\n", - "\n", - "For more about service definitinos, please see [Service and APIs](https://docs.bentoml.com/en/latest/concepts/service.html)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build Service\n", - "\n", - "Building the service and models into a bento allows it to be distributed among collaborators, containerized into a OCI image, and deployed in the Kubernetes cluster. To build a service into a bento, we first need to define the `bentofile.yaml` file. See [Building Bentos](https://docs.bentoml.com/en/latest/concepts/bento.html) for more options.\n", - "\n", - "```yaml\n", - "service: \"service:svc\"\n", - "include:\n", - "- \"service.py\"\n", - "- \"sample.py\"\n", - "python:\n", - " requirements_txt: ./requirements.txt\n", - "```\n", - "\n", - "Running the following command will build the service into a bento and store it to the local bento store." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! bentoml build" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Serve Bento\n", - "\n", - "Serving the bento will bring up a service endpoint in HTTP or gRPC for the service API we defined. Use `--help` to see more serving options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! bentoml serve-http --production" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy to Kubernetes Cluster\n", - "\n", - "Great work! You have successfully built and tested the Fraud Detection Bento. Next, we will deploy the bento to the Kubernetes cluster. Proceed to the README of the example for the next steps." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "bentoml", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "c7496e3357ac7d0feefccc058dccef5f5223d152b10dff998de4314227246f61" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/kubeflow/requirements.txt b/examples/kubeflow/requirements.txt deleted file mode 100644 index 554e7078591..00000000000 --- a/examples/kubeflow/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -bentoml -numpy==1.23.5 -pandas==1.5.2 -xgboost==1.7.4 -scikit-learn==1.1.3 diff --git a/examples/kubeflow/sample.py b/examples/kubeflow/sample.py deleted file mode 100644 index be08f55910e..00000000000 --- a/examples/kubeflow/sample.py +++ /dev/null @@ -1,401 +0,0 @@ -import pandas as pd - -sample_json = [ - { - "TransactionID": 2987000, - "TransactionDT": 86400, - "TransactionAmt": 68.5, - "ProductCD": "W", - "card1": 13926, - "card2": None, - "card3": 150.0, - "card4": "discover", - "card5": 142.0, - "card6": "credit", - "addr1": 315.0, - "addr2": 87.0, - "dist1": 19.0, - "dist2": None, - "P_emaildomain": None, - "R_emaildomain": None, - "C1": 1.0, - "C2": 1.0, - "C3": 0.0, - "C4": 0.0, - "C5": 0.0, - "C6": 1.0, - "C7": 0.0, - "C8": 0.0, - "C9": 1.0, - "C10": 0.0, - "C11": 2.0, - "C12": 0.0, - "C13": 1.0, - "C14": 1.0, - "D1": 14.0, - "D2": None, - "D3": 13.0, - "D4": None, - "D5": None, - "D6": None, - "D7": None, - "D8": None, - "D9": None, - "D10": 13.0, - "D11": 13.0, - "D12": None, - "D13": None, - "D14": None, - "D15": 0.0, - "M1": "T", - "M2": "T", - "M3": "T", - "M4": "M2", - "M5": "F", - "M6": "T", - "M7": None, - "M8": None, - "M9": None, - "V1": 1.0, - "V2": 1.0, - "V3": 1.0, - "V4": 1.0, - "V5": 1.0, - "V6": 1.0, - "V7": 1.0, - "V8": 1.0, - "V9": 1.0, - "V10": 0.0, - 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} -] - -sample_input = pd.DataFrame(sample_json) diff --git a/examples/kubeflow/service.py b/examples/kubeflow/service.py deleted file mode 100644 index 8914e7e75c5..00000000000 --- a/examples/kubeflow/service.py +++ /dev/null @@ -1,45 +0,0 @@ -import asyncio - -import numpy as np -import pandas as pd -from sample import sample_input - -import bentoml -from bentoml.io import JSON -from bentoml.io import PandasDataFrame - -fraud_detection_preprocessors = [] -fraud_detection_runners = [] - -for model_name in [ - "ieee-fraud-detection-0", - "ieee-fraud-detection-1", - "ieee-fraud-detection-2", -]: - model_ref = bentoml.xgboost.get(model_name) - fraud_detection_preprocessors.append(model_ref.custom_objects["preprocessor"]) - fraud_detection_runners.append(model_ref.to_runner()) - -svc = bentoml.Service("fraud_detection", runners=fraud_detection_runners) - - -@svc.api(input=PandasDataFrame.from_sample(sample_input), output=JSON()) -async def is_fraud(input_df: pd.DataFrame): - input_df = input_df.astype(sample_input.dtypes) - - async def _is_fraud(preprocessor, runner, input_df): - input_features = preprocessor.transform(input_df) - results = await runner.predict_proba.async_run(input_features) - predictions = np.argmax(results, axis=1) # 0 is not fraud, 1 is fraud - return bool(predictions[0]) - - # Simultaeously run all models - results = await asyncio.gather( - *[ - _is_fraud(p, r, input_df) - for p, r in zip(fraud_detection_preprocessors, fraud_detection_runners) - ] - ) - - # Return fraud if at least one model returns fraud - return any(results) diff --git a/examples/mlflow/.gitignore b/examples/mlflow/.gitignore deleted file mode 100644 index c0e11754a0b..00000000000 --- a/examples/mlflow/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -**/mlruns/ -**/mlartifacts/ -**/outputs/ -**/mlruns.db -**/data/ -**/0/ diff --git a/examples/mlflow/keras/train.py b/examples/mlflow/keras/train.py deleted file mode 100644 index c31fa96e68f..00000000000 --- a/examples/mlflow/keras/train.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Trains and evaluate a simple MLP -on the Reuters newswire topic classification task. -""" - -# pylint: disable=no-name-in-module -# The following import and function call are the only additions to code required -# to automatically log metrics and parameters to MLflow. -import mlflow.keras -import numpy as np -from tensorflow import keras -from tensorflow.keras.datasets import reuters -from tensorflow.keras.layers import Activation -from tensorflow.keras.layers import Dense -from tensorflow.keras.layers import Dropout -from tensorflow.keras.models import Sequential -from tensorflow.keras.preprocessing.text import Tokenizer - -import bentoml - -mlflow.keras.autolog() - -max_words = 1000 -batch_size = 32 -epochs = 5 - -print("Loading data...") -(x_train, y_train), (x_test, y_test) = reuters.load_data( - num_words=max_words, test_split=0.2 -) - -print(len(x_train), "train sequences") -print(len(x_test), "test sequences") - -num_classes = np.max(y_train) + 1 -print(num_classes, "classes") - -print("Vectorizing sequence data...") -tokenizer = Tokenizer(num_words=max_words) -x_train = tokenizer.sequences_to_matrix(x_train, mode="binary") -x_test = tokenizer.sequences_to_matrix(x_test, mode="binary") -print("x_train shape:", x_train.shape) -print("x_test shape:", x_test.shape) - -print( - "Convert class vector to binary class matrix (for use with categorical_crossentropy)" -) -y_train = keras.utils.to_categorical(y_train, num_classes) -y_test = keras.utils.to_categorical(y_test, num_classes) -print("y_train shape:", y_train.shape) -print("y_test shape:", y_test.shape) - -print("Building model...") -model = Sequential() -model.add(Dense(512, input_shape=(max_words,))) -model.add(Activation("relu")) -model.add(Dropout(0.5)) -model.add(Dense(num_classes)) -model.add(Activation("softmax")) - -model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) - -history = model.fit( - x_train, - y_train, - batch_size=batch_size, - epochs=epochs, - verbose=1, - validation_split=0.1, -) -score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1) -print("Test score:", score[0]) -print("Test accuracy:", score[1]) - -# Find URI to the logged MLFlow model -run_id = mlflow.last_active_run().info.run_id -artifact_path = "model" -model_uri = f"runs:/{run_id}/{artifact_path}" - -# Option 1: directly save trained model to BentoML -bento_model_1 = bentoml.keras.save_model("keras_native", model) -print("\nBentoML: model imported as native keras model: %s" % bento_model_1) - -# Option 2: Import logged MLFlow model to BentoML -bento_model_2 = bentoml.mlflow.import_model("mlflow_keras", model_uri) -print("\nBentoML: model imported as MLFlow pyfunc model: %s" % bento_model_2) - -# Option 3: loaded keras model from MLFlow artifact and save with bentoml.keras natively -loaded_keras_model = mlflow.keras.load_model(model_uri) -bento_model_3 = bentoml.keras.save_model("keras_native", loaded_keras_model) -print( - "\nBentoML: import native keras model loaded from MLflow artifact: %s" - % bento_model_3 -) - -# Test loading model from BentoML model store: -for bento_model in [ - bentoml.keras.get(bento_model_1.tag), - bentoml.mlflow.get(bento_model_2.tag), - bentoml.keras.get(bento_model_3.tag), -]: - test_runner = bento_model.to_runner() - test_runner.init_local() - assert np.allclose(test_runner.predict.run(x_test), model.predict(x_test)) diff --git a/examples/mlflow/lightgbm/MLproject b/examples/mlflow/lightgbm/MLproject deleted file mode 100644 index d3f535b7ace..00000000000 --- a/examples/mlflow/lightgbm/MLproject +++ /dev/null @@ -1,13 +0,0 @@ -name: lightgbm-example -conda_env: conda.yaml -entry_points: - main: - parameters: - learning_rate: {type: float, default: 0.1} - colsample_bytree: {type: float, default: 1.0} - subsample: {type: float, default: 1.0} - command: | - python train.py \ - --learning-rate={learning_rate} \ - --colsample-bytree={colsample_bytree} \ - --subsample={subsample} diff --git a/examples/mlflow/lightgbm/README.md b/examples/mlflow/lightgbm/README.md deleted file mode 100644 index bd8e54d263c..00000000000 --- a/examples/mlflow/lightgbm/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# LightGBM Example - -This example trains a LightGBM classifier with the iris dataset and logs hyperparameters, metrics, and trained model. - -It is modified from MLFlow LightGBM native example: https://github.com/mlflow/mlflow/tree/master/examples/lightgbm/ - - -## Running the code - -``` -python train.py --colsample-bytree 0.8 --subsample 0.9 -``` -You can try experimenting with different parameter values like: -``` -python train.py --learning-rate 0.4 --colsample-bytree 0.7 --subsample 0.8 -``` - -Then you can open the MLflow UI to track the experiments and compare your runs via: -``` -mlflow ui -``` - -## Running the code as a project - -``` -mlflow run . -P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9 -``` - -## Serve the model with BentoML - -``` -bentoml serve -``` - -``` -curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/classify -``` - -## Build Bento for production deployment - -``` -bentoml build -``` - -Generate docker image from Bento: -``` -bentoml containerize lgb_iris_service:latest -``` diff --git a/examples/mlflow/lightgbm/bentofile.yaml b/examples/mlflow/lightgbm/bentofile.yaml deleted file mode 100644 index b256a5f087b..00000000000 --- a/examples/mlflow/lightgbm/bentofile.yaml +++ /dev/null @@ -1,5 +0,0 @@ -service: "service.py:svc" -include: -- "service.py" -conda: - environment_yml: ./conda.yaml diff --git a/examples/mlflow/lightgbm/conda.yaml b/examples/mlflow/lightgbm/conda.yaml deleted file mode 100644 index 0f117de9ae7..00000000000 --- a/examples/mlflow/lightgbm/conda.yaml +++ /dev/null @@ -1,11 +0,0 @@ -name: lightgbm-example -channels: - - conda-forge -dependencies: - - python=3.7 - - pip - - pip: - - mlflow>=1.6.0 - - matplotlib - - lightgbm - - bentoml>=1.0.0rc3 diff --git a/examples/mlflow/lightgbm/service.py b/examples/mlflow/lightgbm/service.py deleted file mode 100644 index 5f90d8a1f29..00000000000 --- a/examples/mlflow/lightgbm/service.py +++ /dev/null @@ -1,20 +0,0 @@ -import numpy as np - -import bentoml -from bentoml.io import NumpyNdarray - -lgb_iris_runner = bentoml.mlflow.get("lgb_iris:latest").to_runner() - -svc = bentoml.Service("lgb_iris_service", runners=[lgb_iris_runner]) - -input_spec = NumpyNdarray( - dtype="float64", - enforce_dtype=True, - shape=(-1, 4), - enforce_shape=True, -) - - -@svc.api(input=input_spec, output=NumpyNdarray()) -async def classify(input_series: np.ndarray) -> np.ndarray: - return await lgb_iris_runner.predict.async_run(input_series) diff --git a/examples/mlflow/lightgbm/train.py b/examples/mlflow/lightgbm/train.py deleted file mode 100644 index 4ccdbd8dd58..00000000000 --- a/examples/mlflow/lightgbm/train.py +++ /dev/null @@ -1,94 +0,0 @@ -import argparse - -import lightgbm as lgb -import matplotlib as mpl -import mlflow -import mlflow.lightgbm -from sklearn import datasets -from sklearn.metrics import accuracy_score -from sklearn.metrics import log_loss -from sklearn.model_selection import train_test_split - -import bentoml - -mpl.use("Agg") - - -def parse_args(): - parser = argparse.ArgumentParser(description="LightGBM example") - parser.add_argument( - "--learning-rate", - type=float, - default=0.1, - help="learning rate to update step size at each boosting step (default: 0.3)", - ) - parser.add_argument( - "--colsample-bytree", - type=float, - default=1.0, - help="subsample ratio of columns when constructing each tree (default: 1.0)", - ) - parser.add_argument( - "--subsample", - type=float, - default=1.0, - help="subsample ratio of the training instances (default: 1.0)", - ) - return parser.parse_args() - - -def main(): - # parse command-line arguments - args = parse_args() - - # prepare train and test data - iris = datasets.load_iris() - X = iris.data - y = iris.target - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # enable auto logging - mlflow.lightgbm.autolog() - - train_set = lgb.Dataset(X_train, label=y_train) - - with mlflow.start_run(): - # train model - params = { - "objective": "multiclass", - "num_class": 3, - "learning_rate": args.learning_rate, - "metric": "multi_logloss", - "colsample_bytree": args.colsample_bytree, - "subsample": args.subsample, - "seed": 42, - } - model = lgb.train( - params, - train_set, - num_boost_round=10, - valid_sets=[train_set], - valid_names=["train"], - ) - - # evaluate model - y_proba = model.predict(X_test) - y_pred = y_proba.argmax(axis=1) - loss = log_loss(y_test, y_proba) - acc = accuracy_score(y_test, y_pred) - - # log metrics - mlflow.log_metrics({"log_loss": loss, "accuracy": acc}) - - # Import logged mlflow model to BentoML model store for serving: - model_uri = mlflow.get_artifact_uri("model") - bento_model = bentoml.mlflow.import_model( - "lgb_iris", model_uri, signatures={"predict": {"batchable": True}} - ) - print("Model imported to BentoML: %s" % bento_model) - - -if __name__ == "__main__": - main() diff --git a/examples/mlflow/pytorch/MLproject b/examples/mlflow/pytorch/MLproject deleted file mode 100644 index edf796d7523..00000000000 --- a/examples/mlflow/pytorch/MLproject +++ /dev/null @@ -1,25 +0,0 @@ -name: pytorch_tutorial - -conda_env: conda.yaml - -entry_points: - main: - parameters: - batch-size: {type: int, default: 64} - test-batch-size: {type: int, default: 1000} - epochs: {type: int, default: 10} - lr: {type: float, default: 0.01} - momentum: {type: float, default: 0.5} - enable-cuda: {type: string, default: 'True'} - seed: {type: int, default: 5} - log-interval: {type: int, default: 100} - command: | - python mnist_tensorboard_artifact.py \ - --batch-size {batch-size} \ - --test-batch-size {test-batch-size} \ - --epochs {epochs} \ - --lr {lr} \ - --momentum {momentum} \ - --enable-cuda {enable-cuda} \ - --seed {seed} \ - --log-interval {log-interval} diff --git a/examples/mlflow/pytorch/README.md b/examples/mlflow/pytorch/README.md deleted file mode 100644 index 2e8cdf4f35a..00000000000 --- a/examples/mlflow/pytorch/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# MLFlow + PyTorch + BentoML Example - -This example is built on https://github.com/mlflow/mlflow/tree/master/examples/pytorch - -Train a PyTorch mnist example model, log the model to mlflow and import MLFlow model to BentoML for serving. - -```bash -python mnist.py -``` - -Start the prediction service defined with BentoML in `service.py`, using the imported MLflow model: - -```bash -bentoml serve service.py:svc -``` - -Test out the serving endpoint: - -```bash -curl -X POST -H "Content-Type:application/json" \ - -d @test_input.json \ - http://localhost:3000/predict -``` - -Build Bento and containerize BentoServier for deployment: - -```bash -bentoml build - -bentoml containerize mlflow_pytorch_mnist_demo:latest -``` diff --git a/examples/mlflow/pytorch/bentofile.yaml b/examples/mlflow/pytorch/bentofile.yaml deleted file mode 100644 index 3e8bdc4168f..00000000000 --- a/examples/mlflow/pytorch/bentofile.yaml +++ /dev/null @@ -1,5 +0,0 @@ -service: "service:svc" -include: - - "service.py" -conda: - environment_yml: "./conda.yaml" diff --git a/examples/mlflow/pytorch/conda.yaml b/examples/mlflow/pytorch/conda.yaml deleted file mode 100644 index 0350289807c..00000000000 --- a/examples/mlflow/pytorch/conda.yaml +++ /dev/null @@ -1,12 +0,0 @@ -name: pytorch_example -channels: - - conda-forge -dependencies: - - python=3.7 - - pip - - pip: - - torch==1.8.1 - - torchvision==0.9.1 - - mlflow>=1.0 - - protobuf<4.0.0 - - bentoml>=1.0.0rc3 diff --git a/examples/mlflow/pytorch/mnist.py b/examples/mlflow/pytorch/mnist.py deleted file mode 100644 index 317bf085d21..00000000000 --- a/examples/mlflow/pytorch/mnist.py +++ /dev/null @@ -1,261 +0,0 @@ -# pylint: disable=redefined-outer-name -# -# Trains an MNIST digit recognizer using PyTorch, and uses tensorboardX to log training metrics -# and weights in TensorBoard event format to the MLflow run's artifact directory. This stores the -# TensorBoard events in MLflow for later access using the TensorBoard command line tool. -# -# NOTE: This example requires you to first install PyTorch (using the instructions at pytorch.org) -# and tensorboardX (using pip install tensorboardX). -# -# Code based on https://github.com/lanpa/tensorboard-pytorch-examples/blob/master/mnist/main.py. -# -# BentoML example is based on https://github.com/mlflow/mlflow/blob/master/examples/pytorch/mnist_tensorboard_artifact.py -# -import argparse -import os - -import mlflow -import mlflow.pytorch -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from torchvision import datasets -from torchvision import transforms - -import bentoml - -# Command-line arguments -parser = argparse.ArgumentParser(description="PyTorch MNIST Example") -parser.add_argument( - "--batch-size", - type=int, - default=64, - metavar="N", - help="input batch size for training (default: 64)", -) -parser.add_argument( - "--test-batch-size", - type=int, - default=1000, - metavar="N", - help="input batch size for testing (default: 1000)", -) -parser.add_argument( - "--epochs", - type=int, - default=10, - metavar="N", - help="number of epochs to train (default: 10)", -) -parser.add_argument( - "--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)" -) -parser.add_argument( - "--momentum", - type=float, - default=0.5, - metavar="M", - help="SGD momentum (default: 0.5)", -) -parser.add_argument( - "--enable-cuda", - type=str, - choices=["True", "False"], - default="True", - help="enables or disables CUDA training", -) -parser.add_argument( - "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)" -) -parser.add_argument( - "--log-interval", - type=int, - default=100, - metavar="N", - help="how many batches to wait before logging training status", -) -args = parser.parse_args() - -enable_cuda_flag = True if args.enable_cuda == "True" else False - -args.cuda = enable_cuda_flag and torch.cuda.is_available() - -torch.manual_seed(args.seed) -if args.cuda: - torch.cuda.manual_seed(args.seed) - -kwargs = {"num_workers": 1, "pin_memory": True} if args.cuda else {} -train_loader = torch.utils.data.DataLoader( - datasets.MNIST( - "../data", - train=True, - download=True, - transform=transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] - ), - ), - batch_size=args.batch_size, - shuffle=True, - **kwargs, -) -test_loader = torch.utils.data.DataLoader( - datasets.MNIST( - "../data", - train=False, - transform=transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] - ), - ), - batch_size=args.test_batch_size, - shuffle=True, - **kwargs, -) - - -class Net(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 10, kernel_size=5) - self.conv2 = nn.Conv2d(10, 20, kernel_size=5) - self.conv2_drop = nn.Dropout2d() - self.fc1 = nn.Linear(320, 50) - self.fc2 = nn.Linear(50, 10) - - def forward(self, x): - x = F.relu(F.max_pool2d(self.conv1(x), 2)) - x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) - x = x.view(-1, 320) - x = F.relu(self.fc1(x)) - x = F.dropout(x, training=self.training) - x = self.fc2(x) - return F.log_softmax(x, dim=0) - - -model = Net() -if args.cuda: - model.cuda() - -optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) - - -def train(epoch): - model.train() - for batch_idx, (data, target) in enumerate(train_loader): - if args.cuda: - data, target = data.cuda(), target.cuda() - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print( - "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( - epoch, - batch_idx * len(data), - len(train_loader.dataset), - 100.0 * batch_idx / len(train_loader), - loss.data.item(), - ) - ) - step = epoch * len(train_loader) + batch_idx - log_scalar("train_loss", loss.data.item(), step) - - -def test(epoch): - model.eval() - test_loss = 0 - correct = 0 - with torch.no_grad(): - for data, target in test_loader: - if args.cuda: - data, target = data.cuda(), target.cuda() - output = model(data) - test_loss += F.nll_loss( - output, target, reduction="sum" - ).data.item() # sum up batch loss - # get the index of the max log-probability - pred = output.data.max(1)[1] - correct += pred.eq(target.data).cpu().sum().item() - - test_loss /= len(test_loader.dataset) - test_accuracy = 100.0 * correct / len(test_loader.dataset) - print( - "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - test_loss, correct, len(test_loader.dataset), test_accuracy - ) - ) - step = (epoch + 1) * len(train_loader) - log_scalar("test_loss", test_loss, step) - log_scalar("test_accuracy", test_accuracy, step) - - -def log_scalar(name, value, _): - """Log a scalar value to both MLflow and TensorBoard""" - mlflow.log_metric(name, value) - - -if __name__ == "__main__": - with mlflow.start_run(): - # Log our parameters into mlflow - for key, value in vars(args).items(): - mlflow.log_param(key, value) - - # Perform the training - for epoch in range(1, args.epochs + 1): - train(epoch) - test(epoch) - - # Log the model as an artifact of the MLflow run. - print("\nLogging the trained model as a run artifact...") - mlflow.pytorch.log_model(model, artifact_path="pytorch-model") - print( - "\nThe model is logged at:\n%s" - % os.path.join(mlflow.get_artifact_uri(), "pytorch-model") - ) - - # Option 1: Save pytorch model directly with BentoML - bento_model_1 = bentoml.pytorch.save_model( - "pytorch-mnist", model, signatures={"__call__": {"batchable": True}} - ) - print("Pytorch Model saved with BentoML: %s" % bento_model_1) - - # make predictions with BentoML runner - model_runner_1 = bentoml.pytorch.get("pytorch-mnist:latest").to_runner() - model_runner_1.init_local() - - # Extract a few examples from the test dataset to evaluate on - eval_data, eval_labels = next(iter(test_loader)) - - predictions = model_runner_1.run(eval_data.numpy()) - template = 'Sample {} : Ground truth is "{}", model prediction is "{}"' - print("\nSample predictions") - for index in range(5): - print( - template.format(index, eval_labels[index], predictions.argmax(1)[index]) - ) - - # Option 2: Import logged mlflow model to BentoML for serving: - model_uri = mlflow.get_artifact_uri("pytorch-model") - bento_model_2 = bentoml.mlflow.import_model( - "mlflow_pytorch_mnist", - model_uri, - signatures={"predict": {"batchable": True}}, - ) - print("Model imported to BentoML: %s" % bento_model_2) - - # make predictions with BentoML runner - model_runner_2 = bentoml.mlflow.get("mlflow_pytorch_mnist:latest").to_runner() - model_runner_2.init_local() - - # Extract a few examples from the test dataset to evaluate on - eval_data, eval_labels = next(iter(test_loader)) - - predictions = model_runner_2.predict.run(eval_data.numpy()) - template = 'Sample {} : Ground truth is "{}", model prediction is "{}"' - print("\nSample predictions") - for index in range(5): - print( - template.format(index, eval_labels[index], predictions.argmax(1)[index]) - ) diff --git a/examples/mlflow/pytorch/service.py b/examples/mlflow/pytorch/service.py deleted file mode 100644 index 15a5f7e1844..00000000000 --- a/examples/mlflow/pytorch/service.py +++ /dev/null @@ -1,16 +0,0 @@ -import bentoml - -mnist_runner = bentoml.mlflow.get("mlflow_pytorch_mnist:latest").to_runner() - -svc = bentoml.Service("mlflow_pytorch_mnist_demo", runners=[mnist_runner]) - -input_spec = bentoml.io.NumpyNdarray( - dtype="float32", - shape=[-1, 1, 28, 28], - enforce_dtype=True, -) - - -@svc.api(input=input_spec, output=bentoml.io.NumpyNdarray()) -async def predict(input_arr): - return await mnist_runner.predict.async_run(input_arr) diff --git a/examples/mlflow/pytorch/test_input.json b/examples/mlflow/pytorch/test_input.json deleted file mode 100644 index 90ae733ef1d..00000000000 --- a/examples/mlflow/pytorch/test_input.json +++ /dev/null @@ -1,2360 +0,0 @@ -[ - [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 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-[lr]: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html -[pipe]: https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html -[gs]: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html diff --git a/examples/mlflow/sklearn_autolog/grid_search_cv.py b/examples/mlflow/sklearn_autolog/grid_search_cv.py deleted file mode 100644 index 10693792002..00000000000 --- a/examples/mlflow/sklearn_autolog/grid_search_cv.py +++ /dev/null @@ -1,48 +0,0 @@ -from pprint import pprint - -import mlflow -import pandas as pd -from sklearn import datasets -from sklearn import svm -from sklearn.model_selection import GridSearchCV -from utils import fetch_logged_data - -import bentoml - - -def main(): - mlflow.sklearn.autolog() - - iris = datasets.load_iris() - parameters = {"kernel": ("linear", "rbf"), "C": [1, 10]} - svc = svm.SVC() - clf = GridSearchCV(svc, parameters) - - clf.fit(iris.data, iris.target) - run_id = mlflow.last_active_run().info.run_id - - # show data logged in the parent run - print("========== parent run ==========") - for key, data in fetch_logged_data(run_id).items(): - print("\n---------- logged {} ----------".format(key)) - pprint(data) - - # show data logged in the child runs - filter_child_runs = "tags.mlflow.parentRunId = '{}'".format(run_id) - runs = mlflow.search_runs(filter_string=filter_child_runs) - param_cols = ["params.{}".format(p) for p in parameters.keys()] - metric_cols = ["metrics.mean_test_score"] - - print("\n========== child runs ==========\n") - pd.set_option("display.max_columns", None) # prevent truncating columns - print(runs[["run_id", *param_cols, *metric_cols]]) - - # import only the best_estimator artifact to BentoML - artifact_path = "best_estimator" - model_uri = f"runs:/{run_id}/{artifact_path}" - bento_model = bentoml.mlflow.import_model("sklearn_gs_iris", model_uri) - print("\nModel imported to BentoML: %s" % bento_model) - - -if __name__ == "__main__": - main() diff --git a/examples/mlflow/sklearn_autolog/linear_regression.py b/examples/mlflow/sklearn_autolog/linear_regression.py deleted file mode 100644 index 052680fc1fb..00000000000 --- a/examples/mlflow/sklearn_autolog/linear_regression.py +++ /dev/null @@ -1,38 +0,0 @@ -from pprint import pprint - -import mlflow -import numpy as np -from sklearn.linear_model import LinearRegression -from utils import fetch_logged_data - -import bentoml - - -def main(): - # enable autologging - mlflow.sklearn.autolog() - - # prepare training data - X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) - y = np.dot(X, np.array([1, 2])) + 3 - - # train a model - model = LinearRegression() - model.fit(X, y) - run_id = mlflow.last_active_run().info.run_id - print("Logged data and model in run {}".format(run_id)) - - # show logged data - for key, data in fetch_logged_data(run_id).items(): - print("\n---------- logged {} ----------".format(key)) - pprint(data) - - # import logged MLFlow model to BentoML - artifact_path = "model" - model_uri = f"runs:/{run_id}/{artifact_path}" - bento_model = bentoml.mlflow.import_model("logistic_regression_model", model_uri) - print("\nModel imported to BentoML: %s" % bento_model) - - -if __name__ == "__main__": - main() diff --git a/examples/mlflow/sklearn_autolog/pipeline.py b/examples/mlflow/sklearn_autolog/pipeline.py deleted file mode 100644 index d18c6e9f250..00000000000 --- a/examples/mlflow/sklearn_autolog/pipeline.py +++ /dev/null @@ -1,40 +0,0 @@ -from pprint import pprint - -import mlflow -import numpy as np -from sklearn.linear_model import LinearRegression -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from utils import fetch_logged_data - -import bentoml - - -def main(): - # enable autologging - mlflow.sklearn.autolog() - - # prepare training data - X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) - y = np.dot(X, np.array([1, 2])) + 3 - - # train a model - pipe = Pipeline([("scaler", StandardScaler()), ("lr", LinearRegression())]) - pipe.fit(X, y) - run_id = mlflow.last_active_run().info.run_id - print("Logged data and model in run: {}".format(run_id)) - - # show logged data - for key, data in fetch_logged_data(run_id).items(): - print("\n---------- logged {} ----------".format(key)) - pprint(data) - - # import logged MLFlow model to BentoML - artifact_path = "model" - model_uri = f"runs:/{run_id}/{artifact_path}" - bento_model = bentoml.mlflow.import_model("pipeline_model", model_uri) - print("\nModel imported to BentoML: %s" % bento_model) - - -if __name__ == "__main__": - main() diff --git a/examples/mlflow/sklearn_autolog/utils.py b/examples/mlflow/sklearn_autolog/utils.py deleted file mode 100644 index 95d8e46721f..00000000000 --- a/examples/mlflow/sklearn_autolog/utils.py +++ /dev/null @@ -1,26 +0,0 @@ -from mlflow.tracking import MlflowClient - - -def yield_artifacts(run_id, path=None): - """Yield all artifacts in the specified run""" - client = MlflowClient() - for item in client.list_artifacts(run_id, path): - if item.is_dir: - yield from yield_artifacts(run_id, item.path) - else: - yield item.path - - -def fetch_logged_data(run_id): - """Fetch params, metrics, tags, and artifacts in the specified run""" - client = MlflowClient() - data = client.get_run(run_id).data - # Exclude system tags: https://www.mlflow.org/docs/latest/tracking.html#system-tags - tags = {k: v for k, v in data.tags.items() if not k.startswith("mlflow.")} - artifacts = list(yield_artifacts(run_id)) - return { - "params": data.params, - "metrics": data.metrics, - "tags": tags, - "artifacts": artifacts, - } diff --git a/examples/mlflow/sklearn_logistic_regression/MLproject b/examples/mlflow/sklearn_logistic_regression/MLproject deleted file mode 100644 index f7ebb4ea326..00000000000 --- a/examples/mlflow/sklearn_logistic_regression/MLproject +++ /dev/null @@ -1,7 +0,0 @@ -name: sklearn_logistic_example - -conda_env: conda.yaml - -entry_points: - main: - command: "python train.py" diff --git a/examples/mlflow/sklearn_logistic_regression/conda.yaml b/examples/mlflow/sklearn_logistic_regression/conda.yaml deleted file mode 100644 index 9b876c82fb3..00000000000 --- a/examples/mlflow/sklearn_logistic_regression/conda.yaml +++ /dev/null @@ -1,11 +0,0 @@ -name: sklearn-example -channels: - - conda-forge -dependencies: - - python=3.8 - - pip - - pip: - - mlflow>=1.0 - - scipy - - scikit-learn - - bentoml>=1.0.0rc3 diff --git a/examples/mlflow/sklearn_logistic_regression/train.py b/examples/mlflow/sklearn_logistic_regression/train.py deleted file mode 100644 index 13df8ee6f30..00000000000 --- a/examples/mlflow/sklearn_logistic_regression/train.py +++ /dev/null @@ -1,23 +0,0 @@ -import mlflow -import mlflow.sklearn -import numpy as np -from sklearn.linear_model import LogisticRegression - -import bentoml - -if __name__ == "__main__": - X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1) - y = np.array([0, 0, 1, 1, 1, 0]) - lr = LogisticRegression() - lr.fit(X, y) - score = lr.score(X, y) - print("Score: %s" % score) - mlflow.log_metric("score", score) - logged_model = mlflow.sklearn.log_model(lr, "model") - print("Model saved in run %s" % mlflow.active_run().info.run_uuid) - - # Import logged mlflow model to BentoML model store for serving: - bento_model = bentoml.mlflow.import_model( - "logistic_regression_model", logged_model.model_uri - ) - print("Model imported to BentoML: %s" % bento_model) diff --git a/examples/mlflow/torchscript/IrisClassification/MLproject b/examples/mlflow/torchscript/IrisClassification/MLproject deleted file mode 100644 index 2141a4d166b..00000000000 --- a/examples/mlflow/torchscript/IrisClassification/MLproject +++ /dev/null @@ -1,12 +0,0 @@ -name: iris-classification - -conda_env: conda.yaml - -entry_points: - main: - parameters: - epochs: {type: int, default: 50} - - command: | - python iris_classification.py \ - --epochs {epochs} diff --git a/examples/mlflow/torchscript/IrisClassification/README.md b/examples/mlflow/torchscript/IrisClassification/README.md deleted file mode 100644 index c9cd4ee8c02..00000000000 --- a/examples/mlflow/torchscript/IrisClassification/README.md +++ /dev/null @@ -1,41 +0,0 @@ -## Iris classification example with MLflow -This example demonstrates training a classification model on the Iris dataset, scripting the model with TorchScript, logging the -scripted model to MLflow using -[``mlflow.pytorch.log_model``](https://mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.log_model), and -loading it back for inference using -[``mlflow.pytorch.load_model``](https://mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.load_model) - - -### Running the code -To run the example via MLflow, navigate to the `mlflow/examples/pytorch/torchscript/IrisClassification` directory and run the command - -``` -mlflow run . -``` - -This will run `iris_classification.py` with the default set of parameters such as `--max_epochs=5`. You can see the default value in the `MLproject` file. - -In order to run the file with custom parameters, run the command - -``` -mlflow run . -P epochs=X -``` - -where `X` is your desired value for `epochs`. - -If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument `--env-manager=local`. - -``` -mlflow run . --env-manager=local -``` - -Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command - -``` -mlflow ui -``` - -and navigating to [http://localhost:5000](http://localhost:5000). - -## Running against a custom tracking server -To configure MLflow to log to a custom (non-default) tracking location, set the ``MLFLOW_TRACKING_URI`` environment variable, e.g. via ``export MLFLOW_TRACKING_URI=http://localhost:5000/``. For more details, see [the docs](https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded) diff --git a/examples/mlflow/torchscript/IrisClassification/conda.yaml b/examples/mlflow/torchscript/IrisClassification/conda.yaml deleted file mode 100644 index 57dfa6e7d6e..00000000000 --- a/examples/mlflow/torchscript/IrisClassification/conda.yaml +++ /dev/null @@ -1,12 +0,0 @@ -channels: -- conda-forge -dependencies: -- python=3.8.2 -- pip -- pip: - - sklearn - - cloudpickle==1.6.0 - - boto3 - - torchvision>=0.9.1 - - torch>=1.9.0 - - bentoml>=1.0.0rc3 diff --git a/examples/mlflow/torchscript/IrisClassification/iris_classification.py b/examples/mlflow/torchscript/IrisClassification/iris_classification.py deleted file mode 100644 index 2ad22486009..00000000000 --- a/examples/mlflow/torchscript/IrisClassification/iris_classification.py +++ /dev/null @@ -1,135 +0,0 @@ -# pylint: disable=abstract-method,redefined-outer-name -import argparse - -import mlflow.pytorch -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from sklearn.datasets import load_iris -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - -import bentoml - - -class IrisClassifier(nn.Module): - def __init__(self): - super(IrisClassifier, self).__init__() - self.fc1 = nn.Linear(4, 10) - self.fc2 = nn.Linear(10, 10) - self.fc3 = nn.Linear(10, 3) - - def forward(self, x): - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = F.dropout(x, 0.2) - x = self.fc3(x) - return x - - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def prepare_data(): - iris = load_iris() - data = iris.data - labels = iris.target - target_names = iris.target_names - - X_train, X_test, y_train, y_test = train_test_split( - data, labels, test_size=0.2, random_state=42, shuffle=True, stratify=labels - ) - - X_train = torch.FloatTensor(X_train).to(device) - X_test = torch.FloatTensor(X_test).to(device) - y_train = torch.LongTensor(y_train).to(device) - y_test = torch.LongTensor(y_test).to(device) - - return X_train, X_test, y_train, y_test, target_names - - -def train_model(model, epochs, X_train, y_train): - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - - for epoch in range(epochs): - out = model(X_train) - loss = criterion(out, y_train).to(device) - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if epoch % 10 == 0: - print("number of epoch", epoch, "loss", float(loss)) - - return model - - -def test_model(model, X_test, y_test): - model.eval() - with torch.no_grad(): - predict_out = model(X_test) - _, predict_y = torch.max(predict_out, 1) - - print( - "\nprediction accuracy", - float(accuracy_score(y_test.cpu(), predict_y.cpu())), - ) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Iris Classification Torchscripted model" - ) - - parser.add_argument( - "--epochs", type=int, default=100, help="number of epochs to run (default: 100)" - ) - - args = parser.parse_args() - - model = IrisClassifier() - model = model.to(device) - X_train, X_test, y_train, y_test, target_names = prepare_data() - scripted_model = torch.jit.script(model) # scripting the model - scripted_model = train_model(scripted_model, args.epochs, X_train, y_train) - test_model(scripted_model, X_test, y_test) - - # Saving model and running inference with BentoML: - - # Option1: save natively with bentoml.torchscript_iris - bentoml.torchscript.save_model( - "torchscript_iris", scripted_model, signatures={"__call__": {"batchable": True}} - ) - model_runner = bentoml.torchscript.get("torchscript_iris").to_runner() - model_runner.init_local() - - test_input = np.array([4.4000, 3.0000, 1.3000, 0.2000], dtype="float32") - actual = "setosa" - prediction = model_runner.run(test_input) - predicted = target_names[np.argmax(prediction)] - print("\nPREDICTION RESULT: ACTUAL: {}, PREDICTED: {}".format(actual, predicted)) - - # Option2: save MLflow model and import MLflow pyfunc model to BentoML - with mlflow.start_run() as run: - # logging scripted model - mlflow.pytorch.log_model(scripted_model, "model") - - # Import logged mlflow model to BentoML model store for serving: - model_uri = mlflow.get_artifact_uri("model") - bento_model = bentoml.mlflow.import_model( - "mlflow_torch_iris", model_uri, signatures={"predict": {"batchable": True}} - ) - print(f"Model imported to BentoML: {bento_model}") - - model_runner = bentoml.mlflow.get("mlflow_torch_iris").to_runner() - model_runner.init_local() - - test_input = np.array([4.4000, 3.0000, 1.3000, 0.2000], dtype="float32") - actual = "setosa" - prediction = model_runner.predict.run(test_input) - predicted = target_names[np.argmax(prediction)] - print( - "\nPREDICTION RESULT: ACTUAL: {}, PREDICTED: {}".format(actual, predicted) - ) diff --git a/examples/mlflow/torchscript/MNIST/MLproject b/examples/mlflow/torchscript/MNIST/MLproject deleted file mode 100644 index 0c62493773c..00000000000 --- a/examples/mlflow/torchscript/MNIST/MLproject +++ /dev/null @@ -1,16 +0,0 @@ -name: mnist-torchscript - -conda_env: conda.yaml - -entry_points: - main: - parameters: - epochs: {type: int, default: 5} - batch_size: {type: int, default: 64} - learning_rate: {type: float, default: 1e-3} - - command: | - python mnist_torchscript.py \ - --epochs {epochs} \ - --batch-size {batch_size} \ - --lr {learning_rate} diff --git a/examples/mlflow/torchscript/MNIST/README.md b/examples/mlflow/torchscript/MNIST/README.md deleted file mode 100644 index b77fc226b79..00000000000 --- a/examples/mlflow/torchscript/MNIST/README.md +++ /dev/null @@ -1,48 +0,0 @@ -## MNIST example with MLflow - -This example demonstrates training of MNIST handwritten recognition model and logging it as torch scripted model. -`mlflow.pytorch.log_model()` is used to log the scripted model to MLflow and `mlflow.pytorch.load_model()` to load it from MLflow - -### Code related to MLflow: -This will log the TorchScripted model into MLflow and load the logged model. - -## Setting Tracking URI - -MLflow tracking URI can be set using the environment variable `MLFLOW_TRACKING_URI` - -Example: `export MLFLOW_TRACKING_URI=http://localhost:5000/` - -For more details - https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded - -### Running the code -To run the example via MLflow, navigate to the `mlflow/examples/pytorch/torchscript/MNIST` directory and run the command - -``` -mlflow run . -``` - -This will run `mnist_torchscript.py` with the default set of parameters such as `--max_epochs=5`. You can see the default value in the `MLproject` file. - -In order to run the file with custom parameters, run the command - -``` -mlflow run . -P epochs=X -``` - -where `X` is your desired value for `epochs`. - -If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument `--env-manager=local`. - -``` -mlflow run . --env-manager=local -``` - -Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command - -``` -mlflow ui -``` - -and navigating to [http://localhost:5000](http://localhost:5000). - -For more information on MLflow tracking, click [here](https://www.mlflow.org/docs/latest/tracking.html#mlflow-tracking) to view documentation. diff --git a/examples/mlflow/torchscript/MNIST/conda.yaml b/examples/mlflow/torchscript/MNIST/conda.yaml deleted file mode 100644 index e34b44613bb..00000000000 --- a/examples/mlflow/torchscript/MNIST/conda.yaml +++ /dev/null @@ -1,12 +0,0 @@ -channels: -- conda-forge -dependencies: -- python=3.8.2 -- pip -- pip: - - mlflow - - cloudpickle==1.6.0 - - boto3 - - torchvision>=0.9.1 - - torch>=1.9.0 - - bentoml>=1.0.0rc3 diff --git a/examples/mlflow/torchscript/MNIST/mnist_torchscript.py b/examples/mlflow/torchscript/MNIST/mnist_torchscript.py deleted file mode 100644 index e1f4a79a171..00000000000 --- a/examples/mlflow/torchscript/MNIST/mnist_torchscript.py +++ /dev/null @@ -1,238 +0,0 @@ -# pylint: disable=abstract-method - -import argparse - -import mlflow -import mlflow.pytorch -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from torch.optim.lr_scheduler import StepLR -from torchvision import datasets -from torchvision import transforms - -import bentoml - - -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(1, 32, 3, 1) - self.conv2 = nn.Conv2d(32, 64, 3, 1) - self.dropout1 = nn.Dropout(0.25) - self.dropout2 = nn.Dropout(0.5) - self.fc1 = nn.Linear(9216, 128) - self.fc2 = nn.Linear(128, 10) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.conv2(x) - x = F.relu(x) - x = F.max_pool2d(x, 2) - x = self.dropout1(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = F.relu(x) - x = self.dropout2(x) - x = self.fc2(x) - output = F.log_softmax(x, dim=1) - return output - - -def train(args, model, device, train_loader, optimizer, epoch): - model.train() - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(device), target.to(device) - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print( - "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( - epoch, - batch_idx * len(data), - len(train_loader.dataset), - 100.0 * batch_idx / len(train_loader), - loss.item(), - ) - ) - if args.dry_run: - break - - -def test(model, device, test_loader): - model.eval() - test_loss = 0 - correct = 0 - with torch.no_grad(): - for data, target in test_loader: - data, target = data.to(device), target.to(device) - output = model(data) - # sum up batch loss - test_loss += F.nll_loss(output, target, reduction="sum").item() - # get the index of the max log-probability - pred = output.argmax(dim=1, keepdim=True) - correct += pred.eq(target.view_as(pred)).sum().item() - - test_loss /= len(test_loader.dataset) - - print( - "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - test_loss, - correct, - len(test_loader.dataset), - 100.0 * correct / len(test_loader.dataset), - ) - ) - - -def main(): - # Training settings - parser = argparse.ArgumentParser(description="PyTorch MNIST Example") - parser.add_argument( - "--batch-size", - type=int, - default=64, - metavar="N", - help="input batch size for training (default: 64)", - ) - parser.add_argument( - "--test-batch-size", - type=int, - default=1000, - metavar="N", - help="input batch size for testing (default: 1000)", - ) - parser.add_argument( - "--epochs", - type=int, - default=14, - metavar="N", - help="number of epochs to train (default: 14)", - ) - parser.add_argument( - "--lr", - type=float, - default=1.0, - metavar="LR", - help="learning rate (default: 1.0)", - ) - parser.add_argument( - "--gamma", - type=float, - default=0.7, - metavar="M", - help="Learning rate step gamma (default: 0.7)", - ) - parser.add_argument( - "--no-cuda", action="store_true", default=False, help="disables CUDA training" - ) - parser.add_argument( - "--dry-run", - action="store_true", - default=False, - help="quickly check a single pass", - ) - parser.add_argument( - "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)" - ) - parser.add_argument( - "--log-interval", - type=int, - default=10, - metavar="N", - help="how many batches to wait before logging training status", - ) - parser.add_argument( - "--save-model", - action="store_true", - default=False, - help="For Saving the current model", - ) - - args = parser.parse_args() - use_cuda = not args.no_cuda and torch.cuda.is_available() - - torch.manual_seed(args.seed) - - device = torch.device("cuda" if use_cuda else "cpu") - - train_kwargs = {"batch_size": args.batch_size} - test_kwargs = {"batch_size": args.test_batch_size} - if use_cuda: - cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True} - train_kwargs.update(cuda_kwargs) - test_kwargs.update(cuda_kwargs) - - transform = transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] - ) - dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform) - dataset2 = datasets.MNIST("../data", train=False, transform=transform) - train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs) - test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs) - - model = Net().to(device) - scripted_model = torch.jit.script(model) # scripting the model - optimizer = optim.Adadelta(model.parameters(), lr=args.lr) - - scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) - for epoch in range(1, args.epochs + 1): - train(args, scripted_model, device, train_loader, optimizer, epoch) - scheduler.step() - test(scripted_model, device, test_loader) - - # Saving model and running inference with BentoML: - - # Option1: save natively with bentoml.torchscript_iris - bentoml.torchscript.save_model( - "torchscript_mnist", - scripted_model, - signatures={"__call__": {"batchable": True}}, - ) - model_runner = bentoml.torchscript.get("torchscript_mnist").to_runner() - model_runner.init_local() - - test_datapoint, test_target = next(iter(test_loader)) - prediction = model_runner.run(test_datapoint[0].unsqueeze(0).numpy()) - actual = test_target[0].item() - predicted = np.argmax(prediction).item() - print( - "\nPREDICTION RESULT: ACTUAL: {}, PREDICTED: {}".format( - str(actual), str(predicted) - ) - ) - - # Option2: save MLflow model and import MLflow pyfunc model to BentoML - with mlflow.start_run(): - # logging scripted model - mlflow.pytorch.log_model(scripted_model, "model") - - # Import logged mlflow model to BentoML model store for serving: - model_uri = mlflow.get_artifact_uri("model") - bento_model = bentoml.mlflow.import_model( - "mlflow_torch_mnist", model_uri, signatures={"predict": {"batchable": True}} - ) - print(f"Model imported to BentoML: {bento_model}") - - model_runner = bentoml.mlflow.get("mlflow_torch_mnist").to_runner() - model_runner.init_local() - - test_datapoint, test_target = next(iter(test_loader)) - prediction = model_runner.predict.run(test_datapoint[0].unsqueeze(0).numpy()) - actual = test_target[0].item() - predicted = np.argmax(prediction).item() - print( - "\nPREDICTION RESULT: ACTUAL: {}, PREDICTED: {}".format( - str(actual), str(predicted) - ) - ) - - -if __name__ == "__main__": - main() diff --git a/examples/model_load_store_lora/README.md b/examples/model-loading-saving/README.md similarity index 100% rename from examples/model_load_store_lora/README.md rename to examples/model-loading-saving/README.md diff --git a/examples/model_load_store_lora/bentofile.yaml b/examples/model-loading-saving/bentofile.yaml similarity index 100% rename from examples/model_load_store_lora/bentofile.yaml rename to examples/model-loading-saving/bentofile.yaml diff --git a/examples/model_load_store_lora/import_model.py b/examples/model-loading-saving/import_model.py similarity index 100% rename from examples/model_load_store_lora/import_model.py rename to examples/model-loading-saving/import_model.py diff --git a/examples/model_load_store_lora/requirements.txt b/examples/model-loading-saving/requirements.txt similarity index 100% rename from examples/model_load_store_lora/requirements.txt rename to examples/model-loading-saving/requirements.txt diff --git a/examples/model_load_store_lora/service.py b/examples/model-loading-saving/service.py similarity index 100% rename from examples/model_load_store_lora/service.py rename to examples/model-loading-saving/service.py diff --git a/examples/monitoring/task_classification/.bentoignore b/examples/monitoring/task_classification/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/monitoring/task_classification/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/monitoring/task_classification/.gitignore b/examples/monitoring/task_classification/.gitignore deleted file mode 100644 index 05e584b7434..00000000000 --- a/examples/monitoring/task_classification/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/monitoring diff --git a/examples/monitoring/task_classification/README.md b/examples/monitoring/task_classification/README.md deleted file mode 100644 index 31582674136..00000000000 --- a/examples/monitoring/task_classification/README.md +++ /dev/null @@ -1,189 +0,0 @@ -# BentoML monitoring example for classification tasks - -This is a sample project demonstrating basic monitoring usage of [BentoML](https://github.com/bentoml). - -In this project, we will train a classifier model using Scikit-learn and the Iris dataset, build -an prediction service for serving the trained model with monitoring enabled, and deploy the -model server as a docker image for production deployment. - -### Install Dependencies - -Install python packages required for running this project: -```bash -pip install -r ./requirements.txt -``` - -### Model Training - -Create an Iris classifier and save it with `bentoml.sklearn`: - -```bash -import bentoml -from sklearn import svm, datasets - -# Load training data -iris = datasets.load_iris() -X, y = iris.data, iris.target - -# Model Training -clf = svm.SVC() -clf.fit(X, y) - -# Save model to BentoML local model store -bentoml.sklearn.save_model("iris_clf", clf) -``` - -### Serving the model -Draft a `service.py` file with monitoring data collection lines, and run your service with Bento Server locally: - -```python -import numpy as np - -import bentoml -from bentoml.io import Text -from bentoml.io import NumpyNdarray - -CLASS_NAMES = ["setosa", "versicolor", "virginica"] - -iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner() -svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner]) - - -@svc.api( - input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)), - output=Text(), -) -async def classify(features: np.ndarray) -> str: - with bentoml.monitor("iris_classifier_prediction") as mon: - mon.log(features[0], name="sepal length", role="feature", data_type="numerical") - mon.log(features[1], name="sepal width", role="feature", data_type="numerical") - mon.log(features[2], name="petal length", role="feature", data_type="numerical") - mon.log(features[3], name="petal width", role="feature", data_type="numerical") - - results = await iris_clf_runner.predict.async_run([features]) - result = results[0] - category = CLASS_NAMES[result] - - mon.log(category, name="pred", role="prediction", data_type="categorical") - return category -``` - -```bash -bentoml serve service.py:svc --reload -``` - -Open your web browser at http://127.0.0.1:3000 to view the Bento UI for sending test requests. - -You may also send request with `curl` command or any HTTP client, e.g.: - -```bash -curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/classify -``` - - -Then you can find the exported data under the `./monitoring//data` directory. -Here's the example output: - -```json -{"timestamp": "2022-11-02T12:38:38.701396", "request_id": 8781503815303167270, "sepal length": 5.9, "sepal width": 3.0, "petal length": 1.4, "petal width": 0.2, "pred": "0"} -{"timestamp": "2022-11-02T12:38:48.345552", "request_id": 14419506828678509143, "sepal length": 4.9, "sepal width": 3.0, "petal length": 1.4, "petal width": 0.2, "pred": "0"} -``` - - -### Customizing the monitoring - -You can customize the monitoring by modifying the config file of bentoml. The default is: - -```yaml -monitoring: - enabled: true - type: default - options: - output_dir: ./monitoring -``` - -You can draft your own bentoml config file `deployment.yaml` and change the `output_dir` to any directory you want. You can also use other monitoring solutions by changing the `type` to your desired handler. For example, if you want to use the `arize` handler, you can change the config to: - -```yaml -monitoring: - enabled: true - type: bentoml_plugins.arize.ArizeMonitor - options: - api_key: - space_key: -``` - -Then you can specify the config file through environment variable `BENTOML_CONFIG`: -```bash -BENTOML_CONFIG=deployment.yaml bentoml serve service.py:svc -``` - - -### Containerized Serving with monitoring - -Bento is the distribution format in BentoML which captures all the source code, model files, config -files and dependency specifications required for running the service for production deployment. Think -of it as Docker/Container designed for machine learning models. - -To begin with building Bento, create a `bentofile.yaml` under your project directory: - -```yaml -service: "service.py:svc" -labels: - owner: bentoml-team - project: gallery -include: -- "*.py" -python: - packages: - - scikit-learn - - pandas -``` - -Next, run `bentoml build` from current directory to start the Bento build: - -``` -> bentoml build - -05/05/2022 19:19:16 INFO [cli] Building BentoML service "iris_classifier:5wtigdwm4kwzduqj" from build context "/Users/bentoml/workspace/gallery/quickstart" -05/05/2022 19:19:16 INFO [cli] Packing model "iris_clf:4i7wbngm4crhpuqj" from "/Users/bentoml/bentoml/models/iris_clf/4i7wbngm4crhpuqj" -05/05/2022 19:19:16 INFO [cli] Successfully saved Model(tag="iris_clf:4i7wbngm4crhpuqj", - path="/var/folders/bq/gdsf0kmn2k1bf880r_l238600000gn/T/tmp26dx354ubentoml_bento_iris_classifier/models/iris_clf/4i7wbngm4crhpuqj/") -05/05/2022 19:19:16 INFO [cli] Locking PyPI package versions.. -05/05/2022 19:19:17 INFO [cli] - ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ - ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ - ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ - ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ - ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ - ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ - -05/05/2022 19:19:17 INFO [cli] Successfully built Bento(tag="iris_classifier:5wtigdwm4kwzduqj") at "/Users/bentoml/bentoml/bentos/iris_classifier/5wtigdwm4kwzduqj/" -``` - -A new Bento is now built and saved to local Bento store. You can view and manage it via -`bentoml list`,`bentoml get` and `bentoml delete` CLI command. - -Then we will convert a Bento into a Docker image containing the HTTP model server. - -Make sure you have docker installed and docker deamon running, and run the following commnand: - -```bash -bentoml containerize iris_classifier:latest -``` - -This will build a new docker image with all source code, model files and dependencies in place, -and ready for production deployment. To start a container with this docker image locally, run: - -```bash -docker run -p 3000:3000 iris_classifier:invwzzsw7li6zckb2ie5eubhd --mount type=bind,source=,target=/bento/monitoring -``` - -## What's Next? - -- 👉 [Pop into our Slack community!](https://l.bentoml.com/join-slack) We're happy to help with any issue you face or even just to meet you and hear what you're working on. -- Dive deeper into the [Core Concepts](https://docs.bentoml.com/en/latest/concepts/index.html) in BentoML -- Learn how to use BentoML with other ML Frameworks at [Frameworks Guide](https://docs.bentoml.com/en/latest/frameworks/index.html) or check out other [gallery projects](https://github.com/bentoml/BentoML/tree/main/examples) -- Learn more about model deployment options for Bento: - - [🦄️ Yatai](https://github.com/bentoml/Yatai): Model Deployment at scale on Kubernetes - - [🚀 bentoctl](https://github.com/bentoml/bentoctl): Fast model deployment on any cloud platform diff --git a/examples/monitoring/task_classification/bentofile.yaml b/examples/monitoring/task_classification/bentofile.yaml deleted file mode 100644 index 749288a228a..00000000000 --- a/examples/monitoring/task_classification/bentofile.yaml +++ /dev/null @@ -1,10 +0,0 @@ -service: "service.py:svc" -labels: - owner: bentoml-team - project: gallery -include: -- "*.py" -python: - packages: - - scikit-learn - - pandas diff --git a/examples/monitoring/task_classification/deployment.yaml b/examples/monitoring/task_classification/deployment.yaml deleted file mode 100644 index 5302969a146..00000000000 --- a/examples/monitoring/task_classification/deployment.yaml +++ /dev/null @@ -1,2 +0,0 @@ -monitoring: - enabled: true diff --git a/examples/monitoring/task_classification/locustfile.py b/examples/monitoring/task_classification/locustfile.py deleted file mode 100644 index 8021cf373c0..00000000000 --- a/examples/monitoring/task_classification/locustfile.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np -from locust import HttpUser -from locust import between -from locust import task -from sklearn import datasets - -test_data = datasets.load_iris().data -num_of_rows = test_data.shape[0] - - -class IrisHttpUser(HttpUser): - """ - Usage: - Run the iris_classifier service in production mode: - - bentoml serve-http iris_classifier:latest - - Start locust load testing client with: - - locust --class-picker -H http://localhost:3000 - - Open browser at http://0.0.0.0:8089, adjust desired number of users and spawn - rate for the load test from the Web UI and start swarming. - """ - - @task - def classify(self): - index = np.random.choice(num_of_rows - 1) - - input_data = test_data[index] - self.client.post("/classify", json=input_data.tolist()) - - wait_time = between(0.01, 2) diff --git a/examples/monitoring/task_classification/requirements.txt b/examples/monitoring/task_classification/requirements.txt deleted file mode 100644 index 4e853a4faf0..00000000000 --- a/examples/monitoring/task_classification/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -scikit-learn -pandas -bentoml[monitor-otlp]>=1.0.19 diff --git a/examples/monitoring/task_classification/service.py b/examples/monitoring/task_classification/service.py deleted file mode 100644 index b368426992c..00000000000 --- a/examples/monitoring/task_classification/service.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import annotations - -import os - -import numpy as np - -import bentoml -from bentoml.io import NumpyNdarray -from bentoml.io import Text - -CLASS_NAMES = ["setosa", "versicolor", "virginica"] - -iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner() -svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner]) - -LOG_PATH = os.environ.get("MONITORING_LOG_PATH", "/tmp/iris_monitoring") - - -@svc.api( - input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)), - output=Text(), -) -async def classify(features: np.ndarray) -> str: - with bentoml.monitor( - "iris_classifier_prediction", monitor_options={"log_path": LOG_PATH} - ) as mon: - mon.log(features[0], name="sepal length", role="feature", data_type="numerical") - mon.log(features[1], name="sepal width", role="feature", data_type="numerical") - mon.log(features[2], name="petal length", role="feature", data_type="numerical") - mon.log(features[3], name="petal width", role="feature", data_type="numerical") - - results = await iris_clf_runner.predict.async_run([features]) - result = results[0] - category = CLASS_NAMES[result] - - mon.log(category, name="pred", role="prediction", data_type="categorical") - return category diff --git a/examples/monitoring/task_classification/tests/conftest.py b/examples/monitoring/task_classification/tests/conftest.py deleted file mode 100644 index d74464e3f11..00000000000 --- a/examples/monitoring/task_classification/tests/conftest.py +++ /dev/null @@ -1,66 +0,0 @@ -from __future__ import annotations - -import contextlib -import os -import subprocess -import sys -import typing as t -from pathlib import Path - -import numpy as np -import pytest - -import bentoml -from bentoml._internal.configuration.containers import BentoMLContainer - -if t.TYPE_CHECKING: - from _pytest.fixtures import FixtureRequest - from _pytest.tmpdir import TempPathFactory - -PROJECT_DIR = Path(__file__).parent.parent - - -@pytest.fixture(scope="session", autouse=True) -def prepare_model() -> None: - try: - print(f"Found {bentoml.models.get('iris_clf')}, skipping model saving.") - except bentoml.exceptions.NotFound: - subprocess.check_call( - [sys.executable, PROJECT_DIR.joinpath("train.py").__fspath__()] - ) - - -@pytest.fixture( - name="monitoring_type", params=["default", "otlp"], scope="session", autouse=True -) -def fixture_monitoring_type(request: FixtureRequest) -> str: - BentoMLContainer.config.monitoring.type.set(request.param) - return request.param - - -@pytest.fixture(name="monitoring_dir", scope="session") -def fixture_monitoring_dir(tmp_path_factory: TempPathFactory) -> Path: - d = tmp_path_factory.mktemp("monitoring") - os.environ["MONITORING_LOG_PATH"] = d.__fspath__() - return d - - -@pytest.fixture(scope="session") -def host( - bentoml_home: str, - deployment_mode: t.Literal["container", "distributed", "standalone"], - clean_context: contextlib.ExitStack, - monitoring_dir: Path, -): - from bentoml.testing.server import host_bento - - with host_bento( - "service:svc", - project_path=PROJECT_DIR.__fspath__(), - deployment_mode=deployment_mode, - bentoml_home=bentoml_home, - clean_context=clean_context, - ) as _host: - client = bentoml.client.Client.from_url(_host) - for _ in range(10): - client.classify(np.array([4.9, 3.0, 1.4, 0.2])) diff --git a/examples/monitoring/task_classification/tests/test_log_collection.py b/examples/monitoring/task_classification/tests/test_log_collection.py deleted file mode 100644 index 42b55cfa9c0..00000000000 --- a/examples/monitoring/task_classification/tests/test_log_collection.py +++ /dev/null @@ -1,22 +0,0 @@ -from __future__ import annotations - -import typing as t - -import pandas as pd -import pytest - -if t.TYPE_CHECKING: - from pathlib import Path - - -@pytest.mark.asyncio -async def test_log_collection(host: str, monitoring_dir: Path): - data_path = monitoring_dir.joinpath("iris_classifier_prediction", "data") - assert monitoring_dir.exists() - assert data_path.exists() - assert ( - pd.concat( - [pd.read_json(f.__fspath__(), lines=True) for f in data_path.glob("*")] - ) - is not None - ) diff --git a/examples/monitoring/task_classification/train.py b/examples/monitoring/task_classification/train.py deleted file mode 100644 index 122186740f4..00000000000 --- a/examples/monitoring/task_classification/train.py +++ /dev/null @@ -1,21 +0,0 @@ -import logging - -from sklearn import datasets -from sklearn import svm - -import bentoml - -logging.basicConfig(level=logging.WARN) - -if __name__ == "__main__": - # Load training data - iris = datasets.load_iris() - X, y = iris.data, iris.target - - # Model Training - clf = svm.SVC() - clf.fit(X, y) - - # Save model to BentoML local model store - saved_model = bentoml.sklearn.save_model("iris_clf", clf) - print(f"Model saved: {saved_model}") diff --git a/examples/pydantic_validation/.bentoignore b/examples/pydantic_validation/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/pydantic_validation/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/pydantic_validation/README.md b/examples/pydantic_validation/README.md deleted file mode 100644 index 28375a7b244..00000000000 --- a/examples/pydantic_validation/README.md +++ /dev/null @@ -1,61 +0,0 @@ -# BentoML Example: Using Pydantic for request validation - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train an Iris classifier model, similiar to the quickstart guide: - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -Test the `/predict` endpoint with expected input: - -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 7.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width": 2.2}' http://127.0.0.1:3000/classify - -[2]% -``` - -Test sending request with optional field: -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 7.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width": 2.2, "request_id": 123}' http://127.0.0.1:3000/classify - -[2]% -``` - -Test sending request with wrong field name: - -```bash -$ curl -X POST -H "content-type: application/json" --data '{"sepal_len": 6.2, "sepal_width": 3.2, "petal_len": 5.2, "petal_width_typo": 2.2}' http://127.0.0.1:3000/classify - -"BentoService error handling API request: Invalid JSON input received: 2 validation errors for IrisFeatures - petal_width - field required (type=value_error.missing) - petal_width_typo - extra fields not permitted (type=value_error.extra)"% -``` - - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize iris_classifier_pydantic:latest -``` diff --git a/examples/pydantic_validation/bentofile.yaml b/examples/pydantic_validation/bentofile.yaml deleted file mode 100644 index 29d67cdd2cb..00000000000 --- a/examples/pydantic_validation/bentofile.yaml +++ /dev/null @@ -1,8 +0,0 @@ -service: "service.py:svc" -include: -- "service.py" -python: - packages: - - scikit-learn - - pandas - - pydantic diff --git a/examples/pydantic_validation/requirements.txt b/examples/pydantic_validation/requirements.txt deleted file mode 100644 index f9e6859557f..00000000000 --- a/examples/pydantic_validation/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -scikit-learn -pydantic -pandas -bentoml>=1.0.0 diff --git a/examples/pydantic_validation/service.py b/examples/pydantic_validation/service.py deleted file mode 100644 index 3a3c4ed6658..00000000000 --- a/examples/pydantic_validation/service.py +++ /dev/null @@ -1,39 +0,0 @@ -import typing - -import numpy as np -import pandas as pd -from pydantic import BaseModel - -import bentoml -from bentoml.io import JSON -from bentoml.io import NumpyNdarray - -iris_clf_runner = bentoml.sklearn.get("iris_clf_with_feature_names:latest").to_runner() - -svc = bentoml.Service("iris_classifier_pydantic", runners=[iris_clf_runner]) - - -class IrisFeatures(BaseModel): - sepal_len: float - sepal_width: float - petal_len: float - petal_width: float - - # Optional field - request_id: typing.Optional[int] - - # Use custom Pydantic config for additional validation options - class Config: - extra = "forbid" - - -input_spec = JSON(pydantic_model=IrisFeatures) - - -@svc.api(input=input_spec, output=NumpyNdarray()) -async def classify(input_data: IrisFeatures) -> np.ndarray: - if input_data.request_id is not None: - print("Received request ID: ", input_data.request_id) - - input_df = pd.DataFrame([input_data.dict(exclude={"request_id"})]) - return await iris_clf_runner.predict.async_run(input_df) diff --git a/examples/pydantic_validation/train.py b/examples/pydantic_validation/train.py deleted file mode 100644 index 5af911d0b70..00000000000 --- a/examples/pydantic_validation/train.py +++ /dev/null @@ -1,25 +0,0 @@ -import logging - -import pandas as pd -from sklearn import datasets -from sklearn import svm - -import bentoml - -logging.basicConfig(level=logging.WARN) - -if __name__ == "__main__": - # Load training data - iris = datasets.load_iris() - X = pd.DataFrame( - data=iris.data, columns=["sepal_len", "sepal_width", "petal_len", "petal_width"] - ) - y = iris.target - - # Model Training - clf = svm.SVC() - clf.fit(X, y) - - # Save model to BentoML local model store - saved_model = bentoml.sklearn.save_model("iris_clf_with_feature_names", clf) - print(f"Model saved: {saved_model}") diff --git a/examples/pytorch_mnist/.bentoignore b/examples/pytorch_mnist/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/pytorch_mnist/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/pytorch_mnist/.gitignore b/examples/pytorch_mnist/.gitignore deleted file mode 100644 index 689af916ab0..00000000000 --- a/examples/pytorch_mnist/.gitignore +++ /dev/null @@ -1 +0,0 @@ -MNIST/ diff --git a/examples/pytorch_mnist/README.md b/examples/pytorch_mnist/README.md deleted file mode 100644 index 6c609491606..00000000000 --- a/examples/pytorch_mnist/README.md +++ /dev/null @@ -1,241 +0,0 @@ -# BentoML PyTorch MNIST Tutorial - -This is a sample project demonstrating basic usage of BentoML, the machine learning model serving library. - -In this project, we will train a digit recognition model using PyTorch on the MNIST dataset, build -an ML service for the model, serve the model behind an HTTP endpoint, and containerize the model -server as a docker image for production deployment. - -This project is also available to run from a notebook: https://github.com/bentoml/BentoML/blob/main/examples/pytorch_mnist/pytorch_mnist_demo.ipynb - -### Install Dependencies - -Install python packages required for running this project: - -```bash -pip install -r ./requirements.txt -``` - -### Model Training - -First step, train a digit recognition model with PyTorch using BentoML: - -```bash -python train.py -``` - -This should save a new model in the BentoML local model store: - -```bash -bentoml models list -``` - -Verify that the model can be loaded as runner from Python shell: - -```python -import numpy as np -import PIL.Image -import torch - -import bentoml - -runner = bentoml.pytorch.get("pytorch_mnist:latest").to_runner() -runner.init_local() - -img = PIL.Image.open("samples/0.png") -np_img = np.array(img) -tensor_img = torch.from_numpy(np_img).float() -tensor_img = tensor_img.unsqueeze(0).unsqueeze(0) -tensor_img = torch.nn.functional.interpolate(tensor_img, size=28, mode='bicubic', align_corners=False) - -result = runner.predict.run(tensor_img) # => tensor(0) -``` - -### Create ML Service - -The ML Service code is defined in the `service.py` file: - -```python -# service.py -import typing as t - -import numpy as np -import PIL.Image -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - - -mnist_runner = bentoml.pytorch.get( - "pytorch_mnist", - name="mnist_runner", - predict_fn_name="predict", -).to_runner() - -svc = bentoml.Service( - name="pytorch_mnist_demo", - runners=[ - mnist_runner, - ], -) - - -@svc.api( - input=NumpyNdarray(dtype="float32", enforce_dtype=True), - output=NumpyNdarray(dtype="int64"), -) -async def predict_ndarray( - inp: "np.ndarray[t.Any, np.dtype[t.Any]]", -) -> "np.ndarray[t.Any, np.dtype[t.Any]]": - assert inp.shape == (28, 28) - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension - inp = np.expand_dims(inp, 0) - output_tensor = await mnist_runner.async_run(inp) - return output_tensor.numpy() - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="int64")) -async def predict_image(f: PILImage) -> "np.ndarray[t.Any, np.dtype[t.Any]]": - assert isinstance(f, PILImage) - arr = np.array(f)/255.0 - assert arr.shape == (28, 28) - - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension - arr = np.expand_dims(arr, 0).astype("float32") - output_tensor = await mnist_runner.async_run(arr) - return output_tensor.numpy() -``` - -We defined two api endpoints `/predict_ndarray` and `/predict_image` with single runner. - -Start an API server locally to test the service code above: - -```bash -bentoml serve service:svc --development --reload -``` - -With the `--reload` flag, the API server will automatically restart when the source -file `service.py` is being edited, to boost your development productivity. - -Verify the endpoint can be accessed locally: - -```bash -curl -H "Content-Type: multipart/form-data" -F'fileobj=@samples/1.png;type=image/png' http://127.0.0.1:3000/predict_image -``` - -We can also do a simple local benchmark if [locust](https://locust.io) is installed: - -```bash -locust --headless -u 100 -r 1000 --run-time 10m --host http://127.0.0.1:3000 -``` - -### Build Bento for deployment - -A `bentofile` is already created in this directory for building a -Bento for the service: - -```yaml -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: - - "*.py" -exclude: - - "tests/" -python: - packages: - - scikit-learn - - torch - - Pillow -``` - -Note that we exclude `tests/` from the bento using `exclude`. - -Simply run `bentoml build` from current directory to build a Bento with the latest -version of the `pytorch_mnist` model. This may take a while when running for the first -time for BentoML to resolve all dependency versions: - -``` -> bentoml build - -[01:14:04 AM] INFO Building BentoML service "pytorch_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq" from build context - "/home/chef/workspace/gallery/pytorch" - INFO Packing model "pytorch_mnist_demo:xm6jsddtu3y6zluuvcqvsoywq" from - "/home/chef/bentoml/models/pytorch_mnist_demo/xm6jsddtu3y6zluuvcqvsoywq" - INFO Locking PyPI package versions.. -[01:14:05 AM] INFO - ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ - ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ - ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ - ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ - ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ - ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ - - INFO Successfully built Bento(tag="pytorch_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq") at - "/home/chef/bentoml/bentos/pytorch_mnist_demo/bmygukdtzpy6zlc5vcqvsoywq/" -``` - -This Bento can now be loaded for serving: - -```bash -bentoml serve pytorch_mnist_demo:latest -``` - -The Bento directory contains all code, files, models and configs required for running this service. -BentoML standarlizes this file structure which enables serving runtimes and deployment tools to be -built on top of it. By default, Bentos are managed under the `~/bentoml/bentos` directory: - -``` -> cd ~/bentoml/bentos/pytorch_mnist_demo && cd $(cat latest) - -> tree -. -├── apis -│   └── openapi.yaml -├── bento.yaml -├── env -│   ├── conda -│   ├── docker -│   │   ├── Dockerfile -│   │   ├── entrypoint.sh -│   │   └── init.sh -│   └── python -│   ├── requirements.lock.txt -│   ├── requirements.txt -│   └── version.txt -├── models -│   └── pytorch_mnist_demo -│   ├── eqxdigtybch6nkfb -│   │   ├── model.yaml -│   │   └── saved_model.pt -│   └── latest -├── README.md -└── src - ├── model.py - ├── service.py - └── train.py - -9 directories, 15 files -``` - -### Containerize Bento for deployment - -Make sure you have docker installed and docker deamon running, and the following command -will use your local docker environment to build a new docker image, containing the model -server configured from this Bento: - -```bash -bentoml containerize pytorch_mnist_demo:latest -``` - -Test out the docker image built: - -```bash -docker run -P pytorch_mnist_demo:invwzzsw7li6zckb2ie5eubhd -``` diff --git a/examples/pytorch_mnist/bentofile.yaml b/examples/pytorch_mnist/bentofile.yaml deleted file mode 100644 index b361891c3b2..00000000000 --- a/examples/pytorch_mnist/bentofile.yaml +++ /dev/null @@ -1,15 +0,0 @@ -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -exclude: -- "tests/" -- "locustfile.py" -python: - packages: - - scikit-learn - - torch - - Pillow diff --git a/examples/pytorch_mnist/locustfile.py b/examples/pytorch_mnist/locustfile.py deleted file mode 100644 index c9bdd3de8ac..00000000000 --- a/examples/pytorch_mnist/locustfile.py +++ /dev/null @@ -1,15 +0,0 @@ -from locust import HttpUser -from locust import between -from locust import task - -with open("samples/1.png", "rb") as f: - test_image_bytes = f.read() - - -class PyTorchMNISTLoadTestUser(HttpUser): - wait_time = between(0.01, 2) - - @task - def predict_image(self): - files = {"upload_files": ("1.png", test_image_bytes, "image/png")} - self.client.post("/predict_image", files=files) diff --git a/examples/pytorch_mnist/model.py b/examples/pytorch_mnist/model.py deleted file mode 100644 index 2344609fa2f..00000000000 --- a/examples/pytorch_mnist/model.py +++ /dev/null @@ -1,32 +0,0 @@ -import torch -import torch.nn as nn - - -class SimpleConvNet(nn.Module): - """ - Simple Convolutional Neural Network - """ - - def __init__(self): - super().__init__() - self.layers = nn.Sequential( - nn.Conv2d(1, 10, kernel_size=3), - nn.ReLU(), - nn.Flatten(), - nn.Linear(26 * 26 * 10, 50), - nn.ReLU(), - nn.Linear(50, 20), - nn.ReLU(), - nn.Linear(20, 10), - ) - - def forward(self, x): - return self.layers(x) - - def predict(self, inp): - """predict digit for input""" - self.eval() - with torch.no_grad(): - raw_output = self(inp) - _, pred = torch.max(raw_output, 1) - return pred diff --git a/examples/pytorch_mnist/pytorch_mnist_demo.ipynb b/examples/pytorch_mnist/pytorch_mnist_demo.ipynb deleted file mode 100644 index c2fede3d637..00000000000 --- a/examples/pytorch_mnist/pytorch_mnist_demo.ipynb +++ /dev/null @@ -1,529 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a682ea0b", - "metadata": {}, - "source": [ - "# BentoML PyTorch MNIST Tutorial\n", - "\n", - "Link to source code: https://github.com/bentoml/BentoML/tree/main/examples/pytorch_mnist/\n", - "\n", - "Install required dependencies:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ad00863", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -r requirements.txt" - ] - }, - { - "cell_type": "markdown", - "id": "45393b74", - "metadata": {}, - "source": [ - "## Define the model\n", - "\n", - "First let's define a simple PyTorch network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "caeff07d", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "\n", - "\n", - "class SimpleConvNet(nn.Module):\n", - " \"\"\"\n", - " Simple Convolutional Neural Network\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.layers = nn.Sequential(\n", - " nn.Conv2d(1, 10, kernel_size=3),\n", - " nn.ReLU(),\n", - " nn.Flatten(),\n", - " nn.Linear(26 * 26 * 10, 50),\n", - " nn.ReLU(),\n", - " nn.Linear(50, 20),\n", - " nn.ReLU(),\n", - " nn.Linear(20, 10),\n", - " )\n", - "\n", - " def forward(self, x):\n", - " return self.layers(x)\n", - "\n", - " def predict(self, inp):\n", - " \"\"\"predict digit for input\"\"\"\n", - " self.eval()\n", - " with torch.no_grad():\n", - " raw_output = self(inp)\n", - " _, pred = torch.max(raw_output, 1)\n", - " return pred" - ] - }, - { - "cell_type": "markdown", - "id": "38888f0a", - "metadata": {}, - "source": [ - "## Training and Saving the model\n", - "\n", - "Then we define a simple PyTorch network and some helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c62db15c", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import random\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn.functional as F\n", - "from torch import nn\n", - "from torchvision.datasets import MNIST\n", - "from torch.utils.data import DataLoader, ConcatDataset\n", - "from torchvision import transforms\n", - "from sklearn.model_selection import KFold\n", - "\n", - "import bentoml\n", - "\n", - "# reproducible setup for testing\n", - "seed = 42\n", - "random.seed(seed)\n", - "np.random.seed(seed)\n", - "torch.manual_seed(seed)\n", - "torch.cuda.manual_seed(seed)\n", - "torch.cuda.manual_seed_all(seed)\n", - "torch.backends.cudnn.benchmark = False\n", - "torch.backends.cudnn.deterministic = True\n", - "\n", - "\n", - "def _dataloader_init_fn(worker_id):\n", - " np.random.seed(seed)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "539b5097", - "metadata": {}, - "outputs": [], - "source": [ - "K_FOLDS = 5\n", - "NUM_EPOCHS = 5\n", - "LOSS_FUNCTION = nn.CrossEntropyLoss()\n", - "\n", - "\n", - "def get_dataset():\n", - " # Prepare MNIST dataset by concatenating Train/Test part; we split later.\n", - " train_set = MNIST(\n", - " os.getcwd(), download=True, transform=transforms.ToTensor(), train=True\n", - " )\n", - " test_set = MNIST(\n", - " os.getcwd(), download=True, transform=transforms.ToTensor(), train=False\n", - " )\n", - " return train_set, test_set\n", - "\n", - "\n", - "def train_epoch(model, optimizer, loss_function, train_loader, epoch, device=\"cpu\"):\n", - " # Mark training flag\n", - " model.train()\n", - " for batch_idx, (inputs, targets) in enumerate(train_loader):\n", - " inputs, targets = inputs.to(device), targets.to(device)\n", - " optimizer.zero_grad()\n", - " outputs = model(inputs)\n", - " loss = loss_function(outputs, targets)\n", - " loss.backward()\n", - " optimizer.step()\n", - " if batch_idx % 499 == 0:\n", - " print(\n", - " \"Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\".format(\n", - " epoch,\n", - " batch_idx * len(inputs),\n", - " len(train_loader.dataset),\n", - " 100.0 * batch_idx / len(train_loader),\n", - " loss.item(),\n", - " )\n", - " )\n", - "\n", - "\n", - "def test_model(model, test_loader, device=\"cpu\"):\n", - " correct, total = 0, 0\n", - " model.eval()\n", - " with torch.no_grad():\n", - " for batch_idx, (inputs, targets) in enumerate(test_loader):\n", - " inputs, targets = inputs.to(device), targets.to(device)\n", - " outputs = model(inputs)\n", - " _, predicted = torch.max(outputs.data, 1)\n", - " total += targets.size(0)\n", - " correct += (predicted == targets).sum().item()\n", - "\n", - " return correct, total" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d2db4a8", - "metadata": {}, - "outputs": [], - "source": [ - "# load data\n", - "train_set, test_set = get_dataset()\n", - "test_loader = torch.utils.data.DataLoader(\n", - " test_set,\n", - " batch_size=10,\n", - " sampler=torch.utils.data.RandomSampler(test_set),\n", - " worker_init_fn=_dataloader_init_fn,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "788c19a0", - "metadata": {}, - "source": [ - "### Cross Validation\n", - "\n", - "We can do some cross validation and the results can be saved with the model as metadata\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b2fdd72", - "metadata": {}, - "outputs": [], - "source": [ - "def cross_validate(dataset, epochs=NUM_EPOCHS, k_folds=K_FOLDS):\n", - " results = {}\n", - "\n", - " # Define the K-fold Cross Validator\n", - " kfold = KFold(n_splits=k_folds, shuffle=True)\n", - "\n", - " print(\"--------------------------------\")\n", - "\n", - " # K-fold Cross Validation model evaluation\n", - " for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset)):\n", - " print(f\"FOLD {fold}\")\n", - " print(\"--------------------------------\")\n", - "\n", - " # Sample elements randomly from a given list of ids, no replacement.\n", - " train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)\n", - " test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)\n", - "\n", - " # Define data loaders for training and testing data in this fold\n", - " train_loader = torch.utils.data.DataLoader(\n", - " dataset,\n", - " batch_size=10,\n", - " sampler=train_subsampler,\n", - " worker_init_fn=_dataloader_init_fn,\n", - " )\n", - " test_loader = torch.utils.data.DataLoader(\n", - " dataset,\n", - " batch_size=10,\n", - " sampler=test_subsampler,\n", - " worker_init_fn=_dataloader_init_fn,\n", - " )\n", - "\n", - " # Train this fold\n", - " model = SimpleConvNet()\n", - " optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n", - " loss_function = nn.CrossEntropyLoss()\n", - " for epoch in range(epochs):\n", - " train_epoch(model, optimizer, loss_function, train_loader, epoch)\n", - "\n", - " # Evaluation for this fold\n", - " correct, total = test_model(model, test_loader)\n", - " print(\"Accuracy for fold %d: %d %%\" % (fold, 100.0 * correct / total))\n", - " print(\"--------------------------------\")\n", - " results[fold] = 100.0 * (correct / total)\n", - "\n", - " # Print fold results\n", - " print(f\"K-FOLD CROSS VALIDATION RESULTS FOR {K_FOLDS} FOLDS\")\n", - " print(\"--------------------------------\")\n", - " sum = 0.0\n", - " for key, value in results.items():\n", - " print(f\"Fold {key}: {value} %\")\n", - " sum += value\n", - "\n", - " print(f\"Average: {sum/len(results.items())} %\")\n", - "\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd06de8f", - "metadata": {}, - "outputs": [], - "source": [ - "cv_results = cross_validate(train_set, epochs=1)" - ] - }, - { - "cell_type": "markdown", - "id": "ad2104a6", - "metadata": {}, - "source": [ - "### training the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3d311c5", - "metadata": {}, - "outputs": [], - "source": [ - "def train(dataset, epochs=NUM_EPOCHS, device=\"cpu\"):\n", - " train_sampler = torch.utils.data.RandomSampler(dataset)\n", - " train_loader = torch.utils.data.DataLoader(\n", - " dataset,\n", - " batch_size=10,\n", - " sampler=train_sampler,\n", - " worker_init_fn=_dataloader_init_fn,\n", - " )\n", - " model = SimpleConvNet()\n", - " optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n", - " loss_function = nn.CrossEntropyLoss()\n", - " for epoch in range(epochs):\n", - " train_epoch(model, optimizer, loss_function, train_loader, epoch, device)\n", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8df05c4", - "metadata": {}, - "outputs": [], - "source": [ - "trained_model = train(train_set)" - ] - }, - { - "cell_type": "markdown", - "id": "92d9b23c", - "metadata": {}, - "source": [ - "### saving the model with some metadata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fe9c4a7", - "metadata": {}, - "outputs": [], - "source": [ - "correct, total = test_model(trained_model, test_loader)\n", - "metadata = {\n", - " \"accuracy\": float(correct) / total,\n", - " \"cv_stats\": cv_results,\n", - "}\n", - "\n", - "tag = bentoml.pytorch.save(\n", - " \"pytorch_mnist\",\n", - " trained_model,\n", - " metadata=metadata,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bdf35e55", - "metadata": {}, - "source": [ - "## Create a BentoML Service for serving the model\n", - "\n", - "Note: using `%%writefile` here because `bentoml.Service` instance must be created in a separate `.py` file\n", - "\n", - "Even though we have only one model, we can create as many api endpoints as we want. Here we create two end points `predict_ndarray` and `predict_image`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3e2f590", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile service.py\n", - "\n", - "import typing as t\n", - "\n", - "import numpy as np\n", - "import PIL.Image\n", - "from PIL.Image import Image as PILImage\n", - "\n", - "import bentoml\n", - "from bentoml.io import Image\n", - "from bentoml.io import NumpyNdarray\n", - "\n", - "\n", - "mnist_runner = bentoml.pytorch.get(\n", - " \"pytorch_mnist\",\n", - " name=\"mnist_runner\",\n", - " predict_fn_name=\"predict\",\n", - ").to_runner()\n", - "\n", - "svc = bentoml.Service(\n", - " name=\"pytorch_mnist_demo\",\n", - " runners=[\n", - " mnist_runner,\n", - " ],\n", - ")\n", - "\n", - "\n", - "@svc.api(\n", - " input=NumpyNdarray(dtype=\"float32\", enforce_dtype=True),\n", - " output=NumpyNdarray(dtype=\"int64\"),\n", - ")\n", - "async def predict_ndarray(\n", - " inp: \"np.ndarray[t.Any, np.dtype[t.Any]]\",\n", - ") -> \"np.ndarray[t.Any, np.dtype[t.Any]]\":\n", - " assert inp.shape == (28, 28)\n", - " # We are using greyscale image and our PyTorch model expect one\n", - " # extra channel dimension\n", - " inp = np.expand_dims(inp, 0)\n", - " output_tensor = await mnist_runner.async_run(inp)\n", - " return output_tensor.numpy()\n", - "\n", - "\n", - "@svc.api(input=Image(), output=NumpyNdarray(dtype=\"int64\"))\n", - "async def predict_image(f: PILImage) -> \"np.ndarray[t.Any, np.dtype[t.Any]]\":\n", - " assert isinstance(f, PILImage)\n", - " arr = np.array(f)/255.0\n", - " assert arr.shape == (28, 28)\n", - "\n", - " # We are using greyscale image and our PyTorch model expect one\n", - " # extra channel dimension\n", - " arr = np.expand_dims(arr, 0).astype(\"float32\")\n", - " output_tensor = await mnist_runner.async_run(arr)\n", - " return output_tensor.numpy()\n" - ] - }, - { - "cell_type": "markdown", - "id": "590147aa", - "metadata": {}, - "source": [ - "Start a dev model server to test out the service defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29173871", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve service.py:svc" - ] - }, - { - "cell_type": "markdown", - "id": "606c1b36", - "metadata": {}, - "source": [ - "Now you can use something like:\n", - "\n", - "`curl -H \"Content-Type: multipart/form-data\" -F'fileobj=@samples/1.png;type=image/png' http://127.0.0.1:3000/predict_image`\n", - " \n", - "to send an image to the digit recognition service" - ] - }, - { - "cell_type": "markdown", - "id": "c7f03564", - "metadata": {}, - "source": [ - "## Build a Bento for distribution and deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "207561bc", - "metadata": {}, - "outputs": [], - "source": [ - "bentoml.build(\n", - " \"service.py:svc\",\n", - " include=[\"*.py\"],\n", - " exclude=[\"tests/\"],\n", - " description=\"file:./README.md\",\n", - " python=dict(\n", - " packages=[\"scikit-learn\", \"torch\", \"Pillow\"],\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "36306933", - "metadata": {}, - "source": [ - "Starting a dev server with the Bento build:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec4b9dff", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve pytorch_mnist_demo:latest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f05fae93", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - }, - "name": "pytorch_mnist.ipynb" - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/pytorch_mnist/requirements.txt b/examples/pytorch_mnist/requirements.txt deleted file mode 100644 index c234cd77496..00000000000 --- a/examples/pytorch_mnist/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -scikit-learn -torch -torchvision -bentoml>=1.0.0 diff --git a/examples/pytorch_mnist/samples/0.png b/examples/pytorch_mnist/samples/0.png deleted file mode 100644 index 7a93ea81868246ab670b0998b1f919c6becdb1b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 290 zcmV+-0p0$IP)C?}1GH8<5{%+pM2`k&OErf#59o8;c?- zdE|{BjLm=Ys~3tSgVfD8J`k40VYGk;0O^TWT0NuJ>4Ozh@fB*mh07*qoM6N<$g5cAGv;Y7A diff --git a/examples/pytorch_mnist/samples/1.png b/examples/pytorch_mnist/samples/1.png deleted file mode 100644 index 4629a3b16940439e66f1c992caa16eed1fe5c514..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119 zcmeAS@N?(olHy`uVBq!ia0vp^G9b(WBpAZe8a#lMou`XqNX4Aw1c@sNyykh9)9%ae zP`&&A-;v}u>nAGx0F6*2UngC7tDjNU* diff --git a/examples/pytorch_mnist/samples/2.png b/examples/pytorch_mnist/samples/2.png deleted file mode 100644 index e56adf47bdddc8ab1b02ff8004db08c0db8d8ef4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 285 zcmV+&0pk9NP)7GZwp z<#U~#qyPNDB5Aty4>W{iG2}Sk|M~i`@*+sGv!$iQ4#mu^e{KXaFiZtW{`qk9Xfd*+ z^PjJl3@kbC{(OosH`hip)%i~&184c4Ki{3PIMw;j%vsO>{JB$x&3k(9K~6oZhfR)w j!R^PNYg)Y6<>(0jw0UquY^9}n00000NkvXXu0mjf_3(`M diff --git a/examples/pytorch_mnist/samples/3.png b/examples/pytorch_mnist/samples/3.png deleted file mode 100644 index d441e3d6dd019458953cda989bb2f4eae15f605e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 218 zcmV<0044v4P)>l;K;YT9lLNzK21PlFjbfB$Ykmt6Me z&vY=O`Olwe=#qc`JP-ymR{nw6jVxIPrhPtRO7i|sZ_t1Q1WOLze>rcE|3X?u~yw>SK|EfY85-amoS1w+pA7@by2t zaZ1kp^T!^ia9g?ZPdAy8<=13zOOgTr9C}v)DAySIu3dZf{qNtu=#uOI{{06O1`(G!(d25r|9yMy>Q(pV zjVr=A(d2BO{(WnO%Ps%^y*9<=(7u0v8*$0Wy#DuIf>#K;r1{@}zhAw5_xjbc5Ol{d zFm%9u@izfOQsdwMe^)b;+PA#<_fH&Ml1*J*od?8_e)~5Gm%|tsmi=9d!WTg1O#izD zMRJQAf-V2?uMLXip?Pp-`oDkAQLUNt$%qNU3V8ANR~D*dz=NQ<$9XXu-CX=khd|1P(bF7wQuu#(f_&u$itav%0~l@pG}g(qJA{r~&B z7N2FiwZq)d8&A*VGyJ{TaCl|i0*4O=^^O|~YE*{F?_R>8k#oDi!d-Iy6VsYWI~`8$ lF0)XH^sfJZOtdJ1fnoWFs%ar#Z5D(4;pyt?jFW%2+3 diff --git a/examples/pytorch_mnist/samples/8.png b/examples/pytorch_mnist/samples/8.png deleted file mode 100644 index f5e6652c8c1d3b220e2ea8682e89bb0c1890205e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 304 zcmV-00nh%4P)+{~`c}lUX|8 za>p<|#N2tS{S{c!15=WLiG@MG(Y3LWn-Pm7JpceFxVv5wzv{mL0000k8tHIkL>z1Ox>W z6Mp<=pIxk|z`J^`fu~HF(6!8Ne|6@425fC2awjG0|F1u&sad{sCU<|_BL;@G{{jlz S^4~89x!Tj!&t;ucLK6Upep#mg diff --git a/examples/pytorch_mnist/service.py b/examples/pytorch_mnist/service.py deleted file mode 100644 index 51f012b528d..00000000000 --- a/examples/pytorch_mnist/service.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -import typing as t -from typing import TYPE_CHECKING - -import numpy as np -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - -if TYPE_CHECKING: - from numpy.typing import NDArray - -mnist_runner = bentoml.pytorch.get("pytorch_mnist").to_runner() - -svc = bentoml.Service(name="pytorch_mnist_demo", runners=[mnist_runner]) - - -def to_numpy(tensor): - return tensor.detach().cpu().numpy() - - -@svc.api( - input=NumpyNdarray(dtype="float32", enforce_dtype=True), - output=NumpyNdarray(dtype="int64"), -) -async def predict_ndarray(inp: NDArray[t.Any]) -> NDArray[t.Any]: - assert inp.shape == (28, 28) - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension. Then we will also add one batch - # dimension - inp = np.expand_dims(inp, (0, 1)) - output_tensor = await mnist_runner.async_run(inp) - return to_numpy(output_tensor) - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="int64")) -async def predict_image(f: PILImage) -> NDArray[t.Any]: - assert isinstance(f, PILImage) - arr = np.array(f) / 255.0 - assert arr.shape == (28, 28) - - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension. Then we will also add one batch - # dimension - arr = np.expand_dims(arr, (0, 1)).astype("float32") - output_tensor = await mnist_runner.async_run(arr) - return to_numpy(output_tensor) diff --git a/examples/pytorch_mnist/tests/conftest.py b/examples/pytorch_mnist/tests/conftest.py deleted file mode 100644 index 8043b60da4e..00000000000 --- a/examples/pytorch_mnist/tests/conftest.py +++ /dev/null @@ -1,26 +0,0 @@ -# pylint: disable=redefined-outer-name - -import typing as t - -import pytest - -from bentoml.testing.server import host_bento - - -def pytest_configure(config): # pylint: disable=unused-argument - import os - import subprocess - import sys - - cmd = f"{sys.executable} {os.path.join(os.getcwd(), 'train.py')} --k-folds=0" - subprocess.run(cmd, shell=True, check=True) - - -@pytest.fixture(scope="session") -def host() -> t.Generator[str, None, None]: - import bentoml - - bentoml.build("service:svc") - - with host_bento(bento="pytorch_mnist_demo:latest") as host: - yield host diff --git a/examples/pytorch_mnist/tests/test_prediction.py b/examples/pytorch_mnist/tests/test_prediction.py deleted file mode 100644 index d3fdc7f4dbf..00000000000 --- a/examples/pytorch_mnist/tests/test_prediction.py +++ /dev/null @@ -1,61 +0,0 @@ -# pylint: disable=redefined-outer-name - -import io -import json - -import numpy as np -import pytest - -from bentoml.testing.utils import async_request - - -@pytest.fixture() -def img_data(): - import PIL.Image - - images = {} - digits = list(range(10)) - for digit in digits: - img_path = f"samples/{digit}.png" - with open(img_path, "rb") as f: - img_bytes = f.read() - img_arr = np.array(PIL.Image.open(io.BytesIO(img_bytes))) - img_arr = img_arr / 255.0 - img_dic = { - "bytes": img_bytes, - "array": img_arr, - } - images[digit] = img_dic - - return images - - -@pytest.mark.asyncio -async def test_numpy(host, img_data): - for digit, d in img_data.items(): - img_arr = d["array"] - img_arr_json = json.dumps(img_arr.tolist()) - bdigit = f"{digit}".encode() - await async_request( - "POST", - f"http://{host}/predict_ndarray", - headers={"Content-Type": "application/json"}, - data=img_arr_json, - assert_status=200, - assert_data=bdigit, - ) - - -@pytest.mark.asyncio -async def test_image(host, img_data): - for digit, d in img_data.items(): - img_bytes = d["bytes"] - bdigit = f"{digit}".encode() - await async_request( - "POST", - f"http://{host}/predict_image", - data=img_bytes, - headers={"Content-Type": "image/png"}, - assert_status=200, - assert_data=bdigit, - ) diff --git a/examples/pytorch_mnist/train.py b/examples/pytorch_mnist/train.py deleted file mode 100644 index 6cd630edf86..00000000000 --- a/examples/pytorch_mnist/train.py +++ /dev/null @@ -1,223 +0,0 @@ -# pylint: disable=redefined-outer-name -import argparse -import os -import random - -import model as models -import numpy as np -import torch -from sklearn.model_selection import KFold -from torch import nn -from torchvision import transforms -from torchvision.datasets import MNIST - -import bentoml - -K_FOLDS = 5 -NUM_EPOCHS = 3 -LOSS_FUNCTION = nn.CrossEntropyLoss() - - -# reproducible setup for testing -seed = 42 -random.seed(seed) -np.random.seed(seed) -torch.manual_seed(seed) -torch.cuda.manual_seed(seed) -torch.cuda.manual_seed_all(seed) -torch.backends.cudnn.benchmark = False -torch.backends.cudnn.deterministic = True - - -def _dataloader_init_fn(): - np.random.seed(seed) - - -def get_dataset(): - # Prepare MNIST dataset by concatenating Train/Test part; we split later. - train_set = MNIST( - os.getcwd(), download=True, transform=transforms.ToTensor(), train=True - ) - test_set = MNIST( - os.getcwd(), download=True, transform=transforms.ToTensor(), train=False - ) - return train_set, test_set - - -def train_epoch(model, optimizer, loss_function, train_loader, epoch, device="cpu"): - # Mark training flag - model.train() - for batch_idx, (inputs, targets) in enumerate(train_loader): - inputs, targets = inputs.to(device), targets.to(device) - optimizer.zero_grad() - outputs = model(inputs) - loss = loss_function(outputs, targets) - loss.backward() - optimizer.step() - if batch_idx % 499 == 0: - print( - "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( - epoch, - batch_idx * len(inputs), - len(train_loader.dataset), - 100.0 * batch_idx / len(train_loader), - loss.item(), - ) - ) - - -def test_model(model, test_loader, device="cpu"): - correct, total = 0, 0 - model.eval() - with torch.no_grad(): - for _, (inputs, targets) in enumerate(test_loader): - inputs, targets = inputs.to(device), targets.to(device) - outputs = model(inputs) - _, predicted = torch.max(outputs.data, 1) - total += targets.size(0) - correct += (predicted == targets).sum().item() - - return correct, total - - -def cross_validate(dataset, epochs=NUM_EPOCHS, k_folds=K_FOLDS, device="cpu"): - results = {} - - # Define the K-fold Cross Validator - kfold = KFold(n_splits=k_folds, shuffle=True) - - print("--------------------------------") - - # K-fold Cross Validation model evaluation - for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset)): - print(f"FOLD {fold}") - print("--------------------------------") - - # Sample elements randomly from a given list of ids, no replacement. - train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) - test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids) - - # Define data loaders for training and testing data in this fold - train_loader = torch.utils.data.DataLoader( - dataset, - batch_size=10, - sampler=train_subsampler, - worker_init_fn=_dataloader_init_fn, - ) - test_loader = torch.utils.data.DataLoader( - dataset, - batch_size=10, - sampler=test_subsampler, - worker_init_fn=_dataloader_init_fn, - ) - - # Train this fold - model = models.SimpleConvNet() - optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) - loss_function = nn.CrossEntropyLoss() - model = model.to(device) - for epoch in range(epochs): - train_epoch( - model, optimizer, loss_function, train_loader, epoch, device=device - ) - - # Evaluation for this fold - correct, total = test_model(model, test_loader, device) - print("Accuracy for fold %d: %d %%" % (fold, 100.0 * correct / total)) - print("--------------------------------") - results[fold] = 100.0 * (correct / total) - - # Print fold results - print(f"K-FOLD CROSS VALIDATION RESULTS FOR {K_FOLDS} FOLDS") - print("--------------------------------") - sum_ = 0.0 - for key, value in results.items(): - print(f"Fold {key}: {value} %") - sum_ += value - - print(f"Average: {sum_/len(results.items())} %") - - return results - - -def train(dataset, epochs=NUM_EPOCHS, device="cpu"): - print("Training using %s." % device) - train_sampler = torch.utils.data.RandomSampler(dataset) - train_loader = torch.utils.data.DataLoader( - dataset, - batch_size=10, - sampler=train_sampler, - worker_init_fn=_dataloader_init_fn, - ) - model = models.SimpleConvNet() - optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) - loss_function = nn.CrossEntropyLoss() - model = model.to(device) - for epoch in range(epochs): - train_epoch(model, optimizer, loss_function, train_loader, epoch, device) - return model - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="BentoML PyTorch MNIST Example") - parser.add_argument( - "--epochs", - type=int, - default=NUM_EPOCHS, - metavar="N", - help=f"number of epochs to train (default: {NUM_EPOCHS})", - ) - parser.add_argument( - "--k-folds", - type=int, - default=K_FOLDS, - metavar="N", - help=f"number of folds for k-fold cross-validation (default: {K_FOLDS}, 1 to disable cv)", - ) - parser.add_argument( - "--cuda", action="store_true", default=False, help="enable CUDA training" - ) - parser.add_argument( - "--model-name", - type=str, - default="pytorch_mnist", - help="name for saved the model", - ) - - args = parser.parse_args() - use_cuda = args.cuda and torch.cuda.is_available() - - device = torch.device("cuda" if use_cuda else "cpu") - - train_set, test_set = get_dataset() - test_loader = torch.utils.data.DataLoader( - test_set, - batch_size=10, - sampler=torch.utils.data.RandomSampler(test_set), - worker_init_fn=_dataloader_init_fn, - ) - - if args.k_folds > 1: - cv_results = cross_validate(train_set, args.epochs, args.k_folds, device) - else: - cv_results = {} - - trained_model = train(train_set, args.epochs, device) - correct, total = test_model(trained_model, test_loader, device) - - # training related - metadata = { - "acc": float(correct) / total, - "cv_stats": cv_results, - } - - signatures = {"predict": {"batchable": True}} - - saved_model = bentoml.pytorch.save_model( - args.model_name, - trained_model, - signatures=signatures, - metadata=metadata, - external_modules=[models], - ) - print(f"Saved model: {saved_model}") diff --git a/examples/sentence-embedding/bentofile.yaml b/examples/sentence-embedding/bentofile.yaml deleted file mode 100644 index 241e3417616..00000000000 --- a/examples/sentence-embedding/bentofile.yaml +++ /dev/null @@ -1,13 +0,0 @@ -service: "service:SentenceEmbedding" -labels: - owner: bentoml-team - project: gallery -include: -- "*.py" -python: - packages: - - torch - - transformers -# envs: -# - name: NORMALIZE -# value: "False" diff --git a/examples/sentence-embedding/prepare_model.py b/examples/sentence-embedding/prepare_model.py deleted file mode 100644 index 71953b33b54..00000000000 --- a/examples/sentence-embedding/prepare_model.py +++ /dev/null @@ -1,22 +0,0 @@ -import huggingface_hub - -import bentoml -from bentoml.models import ModelContext - -api = huggingface_hub.HfApi() -repo_info = api.repo_info("sentence-transformers/all-MiniLM-L6-v2") -print(repo_info.sha) - -# Save model to BentoML local model store -with bentoml.models.create( - f"all-MiniLM-L6-v2:{repo_info.sha}", - module="bentoml.transformers", - context=ModelContext(framework_name="", framework_versions={}), - signatures={}, -) as model_ref: - huggingface_hub.snapshot_download( - "sentence-transformers/all-MiniLM-L6-v2", - local_dir=model_ref.path_of("/"), - local_dir_use_symlinks=False, - ) -print(f"Model saved: {model_ref}") diff --git a/examples/sentence-embedding/service.py b/examples/sentence-embedding/service.py deleted file mode 100644 index 24ad0be0b45..00000000000 --- a/examples/sentence-embedding/service.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -import os - -import numpy as np -import torch - -import bentoml -from bentoml import Field - - -@bentoml.service(resources={"memory": "500MiB"}, traffic={"timeout": 1}) -class SentenceEmbedding: - model_ref = bentoml.models.get("all-MiniLM-L6-v2") - - def __init__(self) -> None: - from transformers import AutoModel - from transformers import AutoTokenizer - - print("Init", self.model_ref.path) - # Load model and tokenizer - self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.model = AutoModel.from_pretrained(self.model_ref.path_of("/")).to( - self.device - ) - self.tokenizer = AutoTokenizer.from_pretrained(self.model_ref.path_of("/")) - print("Model loaded", "device:", self.device) - - @staticmethod - def mean_pooling(model_output, attention_mask): - # Mean Pooling - Take attention mask into account for correct averaging - token_embeddings = model_output[ - 0 - ] # First element of model_output contains all token embeddings - input_mask_expanded = ( - attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - ) - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( - input_mask_expanded.sum(1), min=1e-9 - ) - - @bentoml.api(batchable=True) - def encode( - self, - sentences: list[str] = Field(["hello world"], description="input sentences"), - ) -> np.ndarray: - # Tokenize sentences - encoded_input = self.tokenizer( - sentences, padding=True, truncation=True, return_tensors="pt" - ).to(self.device) - - # Compute token embeddings - with torch.no_grad(): - model_output = self.model(**encoded_input) - - # Perform pooling - sentence_embeddings = self.mean_pooling( - model_output, encoded_input["attention_mask"] - ) - - # Optional: Normalize embeddings if needed - if str(os.getenv("NORMALIZE", False)).upper() in [ - "TRUE", - "1", - "YES", - "Y", - "ON", - ]: - sentence_embeddings = torch.nn.functional.normalize( - sentence_embeddings, p=2, dim=1 - ) - - return sentence_embeddings.cpu().numpy() diff --git a/examples/sklearn/linear_regression/README.md b/examples/sklearn/linear_regression/README.md deleted file mode 100644 index 093d2951257..00000000000 --- a/examples/sklearn/linear_regression/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# BentoML Sklearn Example: Linear Regression - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train a linear regression model - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -``` -curl -X POST -H "content-type: application/json" --data "[[5, 3]]" http://127.0.0.1:3000/predict -``` - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize linear_regression:latest -``` diff --git a/examples/sklearn/linear_regression/bentofile.yaml b/examples/sklearn/linear_regression/bentofile.yaml deleted file mode 100644 index 44cad3591f8..00000000000 --- a/examples/sklearn/linear_regression/bentofile.yaml +++ /dev/null @@ -1,5 +0,0 @@ -service: "service.py:svc" -include: - - "service.py" -python: - requirements_txt: "./requirements.txt" diff --git a/examples/sklearn/linear_regression/requirements.txt b/examples/sklearn/linear_regression/requirements.txt deleted file mode 100644 index 215972b9c80..00000000000 --- a/examples/sklearn/linear_regression/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -bentoml>=1.0.0 -scikit-learn diff --git a/examples/sklearn/linear_regression/service.py b/examples/sklearn/linear_regression/service.py deleted file mode 100644 index 023b2ba38b9..00000000000 --- a/examples/sklearn/linear_regression/service.py +++ /dev/null @@ -1,13 +0,0 @@ -import bentoml -from bentoml.io import NumpyNdarray - -reg_runner = bentoml.sklearn.get("linear_reg:latest").to_runner() - -svc = bentoml.Service("linear_regression", runners=[reg_runner]) - -input_spec = NumpyNdarray(dtype="int", shape=(-1, 2)) - - -@svc.api(input=input_spec, output=NumpyNdarray()) -async def predict(input_arr): - return await reg_runner.predict.async_run(input_arr) diff --git a/examples/sklearn/linear_regression/train.py b/examples/sklearn/linear_regression/train.py deleted file mode 100644 index ecdbe620efb..00000000000 --- a/examples/sklearn/linear_regression/train.py +++ /dev/null @@ -1,16 +0,0 @@ -from sklearn import linear_model - -import bentoml - -if __name__ == "__main__": - reg = linear_model.LinearRegression() - reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) - - print("coef: ", reg.coef_) - bento_model = bentoml.sklearn.save_model("linear_reg", reg) - print(f"Model saved: {bento_model}") - - # Test running inference with BentoML runner - test_runner = bentoml.sklearn.get("linear_reg:latest").to_runner() - test_runner.init_local() - assert test_runner.predict.run([[1, 1]]) == reg.predict([[1, 1]]) diff --git a/examples/sklearn/pipeline/README.md b/examples/sklearn/pipeline/README.md deleted file mode 100644 index a41e30f8709..00000000000 --- a/examples/sklearn/pipeline/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# BentoML Sklearn Example: document classification pipeline - -0. Install dependencies: - -```bash -pip install -r ./requirements.txt -``` - -1. Train a document classification pipeline model - -```bash -python ./train.py -``` - -2. Run the service: - -```bash -bentoml serve service.py:svc -``` - -3. Send test request - -Test the `/predict` endpoint: -```bash -curl -X POST -H "content-type: application/text" --data "hello world" http://127.0.0.1:3000/predict -``` - -Test the `/predict_proba` endpoint: -```bash -curl -X POST -H "content-type: application/text" --data "hello world" http://127.0.0.1:3000/predict_proba -``` - - -4. Build Bento - -``` -bentoml build -``` - -5. Build docker image - -``` -bentoml containerize doc_classifier:latest -``` diff --git a/examples/sklearn/pipeline/bentofile.yaml b/examples/sklearn/pipeline/bentofile.yaml deleted file mode 100644 index 44cad3591f8..00000000000 --- a/examples/sklearn/pipeline/bentofile.yaml +++ /dev/null @@ -1,5 +0,0 @@ -service: "service.py:svc" -include: - - "service.py" -python: - requirements_txt: "./requirements.txt" diff --git a/examples/sklearn/pipeline/requirements.txt b/examples/sklearn/pipeline/requirements.txt deleted file mode 100644 index 215972b9c80..00000000000 --- a/examples/sklearn/pipeline/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -bentoml>=1.0.0 -scikit-learn diff --git a/examples/sklearn/pipeline/service.py b/examples/sklearn/pipeline/service.py deleted file mode 100644 index 45363fe9c4c..00000000000 --- a/examples/sklearn/pipeline/service.py +++ /dev/null @@ -1,22 +0,0 @@ -import bentoml -from bentoml.io import JSON -from bentoml.io import Text - -bento_model = bentoml.sklearn.get("twenty_news_group:latest") - -target_names = bento_model.custom_objects["target_names"] -model_runner = bento_model.to_runner() - -svc = bentoml.Service("doc_classifier", runners=[model_runner]) - - -@svc.api(input=Text(), output=JSON()) -async def predict(input_doc: str): - predictions = await model_runner.predict.async_run([input_doc]) - return {"result": target_names[predictions[0]]} - - -@svc.api(input=Text(), output=JSON()) -async def predict_proba(input_doc: str): - predictions = await model_runner.predict_proba.async_run([input_doc]) - return predictions[0] diff --git a/examples/sklearn/pipeline/train.py b/examples/sklearn/pipeline/train.py deleted file mode 100644 index aa9ee1a5ee4..00000000000 --- a/examples/sklearn/pipeline/train.py +++ /dev/null @@ -1,97 +0,0 @@ -import logging -from pprint import pprint -from time import time - -from sklearn.datasets import fetch_20newsgroups -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.feature_extraction.text import TfidfTransformer -from sklearn.linear_model import SGDClassifier -from sklearn.model_selection import GridSearchCV -from sklearn.pipeline import Pipeline - -import bentoml - -# Display progress logs on stdout -logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") - -# Load some categories from the training set -categories = [ - "alt.atheism", - "talk.religion.misc", -] - -# Uncomment the following to do the analysis on all the categories -# categories = None - -print("Loading 20 newsgroups dataset for categories:") -print(categories) - -data = fetch_20newsgroups(subset="train", categories=categories) -print("%d documents" % len(data.filenames)) -print("%d categories" % len(data.target_names)) -print() - -# Define a pipeline combining a text feature extractor with a simple classifier -pipeline = Pipeline( - [ - ("vect", CountVectorizer()), - ("tfidf", TfidfTransformer()), - ("clf", SGDClassifier(loss="log_loss")), - ] -) - -# Parameters to use for grid search. Uncommenting more parameters will give -# better exploring power but will increase processing time in a combinatorial -# way -parameters = { - "vect__max_df": (0.5, 0.75, 1.0), - # 'vect__max_features': (None, 5000, 10000, 50000), - "vect__ngram_range": ((1, 1), (1, 2)), # unigrams or bigrams - # 'tfidf__use_idf': (True, False), - # 'tfidf__norm': ('l1', 'l2'), - "clf__max_iter": (20,), - "clf__alpha": (0.00001, 0.000001), - "clf__penalty": ("l2", "elasticnet"), - # 'clf__max_iter': (10, 50, 80), -} - -# Find the best parameters for both the feature extraction and the -# classifier -grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1) - -print("Performing grid search...") -print("pipeline:", [name for name, _ in pipeline.steps]) -print("parameters:") -pprint(parameters) -t0 = time() -grid_search.fit(data.data, data.target) -print("done in %0.3fs" % (time() - t0)) -print() - -print("Best score: %0.3f" % grid_search.best_score_) -best_parameters = grid_search.best_estimator_.get_params() -best_parameters = { - param_name: best_parameters[param_name] for param_name in sorted(parameters.keys()) -} -print(f"Best parameters set: {best_parameters}") - -bento_model = bentoml.sklearn.save_model( - "twenty_news_group", - grid_search.best_estimator_, - signatures={ - "predict": {"batchable": True, "batch_dim": 0}, - "predict_proba": {"batchable": True, "batch_dim": 0}, - }, - custom_objects={ - "target_names": data.target_names, - }, - metadata=best_parameters, -) -print(f"Model saved: {bento_model}") - -# Test running inference with BentoML runner -test_runner = bentoml.sklearn.get("twenty_news_group:latest").to_runner() -test_runner.init_local() -assert test_runner.predict.run(["hello"]) == grid_search.best_estimator_.predict( - ["hello"] -) diff --git a/examples/tensorflow2_keras/.bentoignore b/examples/tensorflow2_keras/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/tensorflow2_keras/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/tensorflow2_keras/README.md b/examples/tensorflow2_keras/README.md deleted file mode 100644 index 2655d95e5b6..00000000000 --- a/examples/tensorflow2_keras/README.md +++ /dev/null @@ -1,263 +0,0 @@ -# BentoML TensorFlow 2 Tutorial - -This is a sample project demonstrating usage of BentoML following the advanced TensorFlow2 quickstart here: https://www.tensorflow.org/tutorials/quickstart/advanced - -In this project, will train a model using Tensorflow2 library and the MNIST dataset. We will then build -an ML service for the model, serve the model behind an HTTP endpoint, and containerize the model -server as a docker image for production deployment. - -This project is also available to run from a notebook: https://github.com/bentoml/BentoML/blob/main/examples/tensorflow2/tensorflow2_mnist_demo.ipynb - -### Install Dependencies - -First install the requirements for this guide -```bash -pip install -r requirements.txt -``` - -For MacOS 11+ -```bash -pip install -r requirements-macos.txt -``` - -At the time of this writing, for M1 Macbooks, if you are getting the following error: -```bash -ERROR: Could not build wheels for h5py, which is required to install pyproject.toml-based projects -``` -Then you'll need to install and configure the h5py library through brew like this: -```bash -brew install hdf5 -export CPATH="/opt/homebrew/include/" -export HDF5_DIR=/opt/homebrew/ -``` - -Then try running the "pip install tensorflow-macos" again - - -### Model Training - -First step, train a classification model with tensorflow's built in mnist dataset and save the model -with BentoML: - -```bash -python train.py -``` - -If you look at the last line of train.py, you'll see: -````python -bentoml.tensorflow.save_model("tensorflow_mnist", model) -```` - - -This should save a new model in the BentoML local model store under model name "tensorflow_mnist: - -```bash -bentoml models list -``` - -Verify that the model can be loaded as runner from Python shell: - -```python -import numpy as np -import PIL.Image - -import bentoml - -runner = bentoml.tensorflow.get("tensorflow_mnist:latest").to_runner() -runner.init_local() # for debug only. please do not call this in the service - -img = PIL.Image.open("samples/0.png") -arr = np.array(img) / 255.0 -arr = arr.astype("float32") - -# add color channel dimension for greyscale image -arr = np.expand_dims(arr, 2) -runner.run(arr) # => returns an array of probabilities for numbers 0-9 -``` - -### Create ML Service - -The ML Service code is defined in the `service.py` file: - -```python -# service.py -import typing as t - -import numpy as np -import PIL.Image -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - - -mnist_runner = bentoml.tensorflow.get("tensorflow_mnist").to_runner() - -svc = bentoml.Service( - name="tensorflow_mnist_demo", - runners=[ - mnist_runner, - ], -) - - -@svc.api( - input=NumpyNdarray(dtype="float32", enforce_dtype=True), - output=NumpyNdarray(dtype="int64"), -) -async def predict_ndarray( - inp: "np.ndarray[t.Any, np.dtype[t.Any]]", -) -> "np.ndarray[t.Any, np.dtype[t.Any]]": - assert inp.shape == (28, 28) - - # extra channel dimension - inp = np.expand_dims(inp, 2) - output_tensor = await mnist_runner.async_run(inp) - return output_tensor.numpy() - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="int64")) -async def predict_image(f: PILImage) -> "np.ndarray[t.Any, np.dtype[t.Any]]": - assert isinstance(f, PILImage) - arr = np.array(f)/255.0 - assert arr.shape == (28, 28) - - # extra channel dimension - arr = np.expand_dims(arr, (0, 3)).astype("float32") - output_tensor = await mnist_runner.async_run(arr) - return output_tensor.numpy() -``` - -We defined two api endpoints `/predict_ndarray` and `/predict_image` with single runner. - -Start an API server locally to test the service code above: - -```bash -bentoml serve service:svc --development --reload -``` - -With the `--reload` flag, the API server will automatically restart when the source -file `service.py` is being edited, to boost your development productivity. - - -Verify the endpoint can be accessed locally: -```bash -curl -H "Content-Type: multipart/form-data" -F'fileobj=@samples/0.png;type=image/png' http://127.0.0.1:3000/predict_image -``` - -We can also do a simple local benchmark if [locust](https://locust.io) is installed: -```bash -locust --headless -u 100 -r 1000 --run-time 10m --host http://127.0.0.1:3000 -``` - - -### Build Bento for deployment - -A `bentofile` is already created in this directory for building a -Bento for the service: - -```yaml -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -exclude: -- "locustfile.py" -python: - lock_packages: false - packages: - - tensorflow -``` - -Note that we exclude `locustfile.py` from the bento using `exclude`. - -Simply run `bentoml build` from current directory to build a Bento with the latest -version of the `tensorflow_mnist` model. - -This may take a while when running for the first time for BentoML to resolve all dependency versions: - -``` -> bentoml build - -[01:14:04 AM] INFO Building BentoML service "tensorflow_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq" from build context - "/home/chef/workspace/gallery/tensorflow2" - INFO Packing model "tensorflow_mnist_demo:xm6jsddtu3y6zluuvcqvsoywq" from - "/home/chef/bentoml/models/tensorflow_mnist_demo/xm6jsddtu3y6zluuvcqvsoywq" - INFO Locking PyPI package versions.. -[01:14:05 AM] INFO - ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ - ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ - ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ - ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ - ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ - ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ - - INFO Successfully built Bento(tag="tensorflow_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq") at - "/home/chef/bentoml/bentos/tensorflow_mnist_demo/bmygukdtzpy6zlc5vcqvsoywq/" -``` - -This Bento can now be loaded for serving: - -```bash -bentoml serve tensorflow_mnist_demo:latest -``` - -The Bento directory contains all code, files, models and configs required for running this service. -BentoML standarlizes this file structure which enables serving runtimes and deployment tools to be -built on top of it. By default, Bentos are managed under the `~/bentoml/bentos` directory: - -``` -> cd ~/bentoml/bentos/tensorflow_mnist_demo && cd $(cat latest) - -> tree -. -├── README.md -├── apis -│   └── openapi.yaml -├── bento.yaml -├── env -│   ├── conda -│   ├── docker -│   │   ├── Dockerfile -│   │   ├── entrypoint.sh -│   │   └── init.sh -│   └── python -│   ├── requirements.lock.txt -│   ├── requirements.txt -│   └── version.txt -├── models -│   └── tensorflow_mnist -│   ├── latest -│   └── wz77wdeuegyh2du5 -│   ├── assets -│   ├── model.yaml -│   ├── saved_model.pb -│   └── variables -│   ├── variables.data-00000-of-00001 -│   └── variables.index -└── src - ├── service.py - └── train.py - -11 directories, 16 files -``` - - -### Containerize Bento for deployment - -Make sure you have docker installed and docker deamon running, and the following command -will use your local docker environment to build a new docker image, containing the model -server configured from this Bento: - -```bash -bentoml containerize tensorflow_mnist_demo:latest -``` - -Test out the docker image built: -```bash -docker run -p 3000:3000 tensorflow_mnist_demo:latest -``` diff --git a/examples/tensorflow2_keras/bentofile.yaml b/examples/tensorflow2_keras/bentofile.yaml deleted file mode 100644 index f5e217c1cc4..00000000000 --- a/examples/tensorflow2_keras/bentofile.yaml +++ /dev/null @@ -1,14 +0,0 @@ -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -exclude: -- "locustfile.py" -python: - lock_packages: false - packages: - - tensorflow - - Pillow diff --git a/examples/tensorflow2_keras/locustfile.py b/examples/tensorflow2_keras/locustfile.py deleted file mode 100644 index c4c8103eec6..00000000000 --- a/examples/tensorflow2_keras/locustfile.py +++ /dev/null @@ -1,15 +0,0 @@ -from locust import HttpUser -from locust import between -from locust import task - -with open("samples/0.png", "rb") as f: - test_image_bytes = f.read() - - -class TensorFlow2MNISTLoadTestUser(HttpUser): - wait_time = between(0.9, 1.1) - - @task - def predict_image(self): - files = {"upload_files": ("1.png", test_image_bytes, "image/png")} - self.client.post("/predict_image", files=files) diff --git a/examples/tensorflow2_keras/requirements-macos.txt b/examples/tensorflow2_keras/requirements-macos.txt deleted file mode 100644 index 7ab4f93a7a7..00000000000 --- a/examples/tensorflow2_keras/requirements-macos.txt +++ /dev/null @@ -1,3 +0,0 @@ -tensorflow-macos -pillow -bentoml>=1.0.0 diff --git a/examples/tensorflow2_keras/requirements.txt b/examples/tensorflow2_keras/requirements.txt deleted file mode 100644 index 3c8c9705782..00000000000 --- a/examples/tensorflow2_keras/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -tensorflow -pillow diff --git a/examples/tensorflow2_keras/samples/0.png b/examples/tensorflow2_keras/samples/0.png deleted file mode 100644 index 7a93ea81868246ab670b0998b1f919c6becdb1b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 290 zcmV+-0p0$IP)C?}1GH8<5{%+pM2`k&OErf#59o8;c?- zdE|{BjLm=Ys~3tSgVfD8J`k40VYGk;0O^TWT0NuJ>4Ozh@fB*mh07*qoM6N<$g5cAGv;Y7A diff --git a/examples/tensorflow2_keras/samples/1.png b/examples/tensorflow2_keras/samples/1.png deleted file mode 100644 index 4629a3b16940439e66f1c992caa16eed1fe5c514..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119 zcmeAS@N?(olHy`uVBq!ia0vp^G9b(WBpAZe8a#lMou`XqNX4Aw1c@sNyykh9)9%ae zP`&&A-;v}u>nAGx0F6*2UngC7tDjNU* diff --git a/examples/tensorflow2_keras/samples/2.png b/examples/tensorflow2_keras/samples/2.png deleted file mode 100644 index e56adf47bdddc8ab1b02ff8004db08c0db8d8ef4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 285 zcmV+&0pk9NP)7GZwp z<#U~#qyPNDB5Aty4>W{iG2}Sk|M~i`@*+sGv!$iQ4#mu^e{KXaFiZtW{`qk9Xfd*+ z^PjJl3@kbC{(OosH`hip)%i~&184c4Ki{3PIMw;j%vsO>{JB$x&3k(9K~6oZhfR)w j!R^PNYg)Y6<>(0jw0UquY^9}n00000NkvXXu0mjf_3(`M diff --git a/examples/tensorflow2_keras/samples/3.png b/examples/tensorflow2_keras/samples/3.png deleted file mode 100644 index d441e3d6dd019458953cda989bb2f4eae15f605e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 218 zcmV<0044v4P)>l;K;YT9lLNzK21PlFjbfB$Ykmt6Me z&vY=O`Olwe=#qc`JP-ymR{nw6jVxIPrhPtRO7i|sZ_t1Q1WOLze>rcE|3X?u~yw>SK|EfY85-amoS1w+pA7@by2t zaZ1kp^T!^ia9g?ZPdAy8<=13zOOgTr9C}v)DAySIu3dZf{qNtu=#uOI{{06O1`(G!(d25r|9yMy>Q(pV zjVr=A(d2BO{(WnO%Ps%^y*9<=(7u0v8*$0Wy#DuIf>#K;r1{@}zhAw5_xjbc5Ol{d zFm%9u@izfOQsdwMe^)b;+PA#<_fH&Ml1*J*od?8_e)~5Gm%|tsmi=9d!WTg1O#izD zMRJQAf-V2?uMLXip?Pp-`oDkAQLUNt$%qNU3V8ANR~D*dz=NQ<$9XXu-CX=khd|1P(bF7wQuu#(f_&u$itav%0~l@pG}g(qJA{r~&B z7N2FiwZq)d8&A*VGyJ{TaCl|i0*4O=^^O|~YE*{F?_R>8k#oDi!d-Iy6VsYWI~`8$ lF0)XH^sfJZOtdJ1fnoWFs%ar#Z5D(4;pyt?jFW%2+3 diff --git a/examples/tensorflow2_keras/samples/8.png b/examples/tensorflow2_keras/samples/8.png deleted file mode 100644 index f5e6652c8c1d3b220e2ea8682e89bb0c1890205e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 304 zcmV-00nh%4P)+{~`c}lUX|8 za>p<|#N2tS{S{c!15=WLiG@MG(Y3LWn-Pm7JpceFxVv5wzv{mL0000k8tHIkL>z1Ox>W z6Mp<=pIxk|z`J^`fu~HF(6!8Ne|6@425fC2awjG0|F1u&sad{sCU<|_BL;@G{{jlz S^4~89x!Tj!&t;ucLK6Upep#mg diff --git a/examples/tensorflow2_keras/service.py b/examples/tensorflow2_keras/service.py deleted file mode 100644 index 821568032f6..00000000000 --- a/examples/tensorflow2_keras/service.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - -mnist_runner = bentoml.tensorflow.get("tensorflow_mnist:latest").to_runner() - -svc = bentoml.Service( - name="tensorflow_mnist_demo", - runners=[mnist_runner], -) - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="float32")) -async def predict_image(f: PILImage) -> "np.ndarray": - assert isinstance(f, PILImage) - arr = np.array(f) / 255.0 - assert arr.shape == (28, 28) - - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension - arr = np.expand_dims(arr, (0, 3)).astype("float32") # reshape to [1, 28, 28, 1] - return await mnist_runner.async_run(arr) diff --git a/examples/tensorflow2_keras/tensorflow2_mnist_demo.ipynb b/examples/tensorflow2_keras/tensorflow2_mnist_demo.ipynb deleted file mode 100644 index 8c48d6c665b..00000000000 --- a/examples/tensorflow2_keras/tensorflow2_mnist_demo.ipynb +++ /dev/null @@ -1,398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a682ea0b", - "metadata": {}, - "source": [ - "# BentoML TensorFlow2 MNIST Tutorial\n", - "\n", - "Link to source code: https://github.com/bentoml/BentoML/tree/main/examples/tensorflow2_keras/\n", - "\n", - "The code is based on the TensorFlow2 example code here: https://www.tensorflow.org/tutorials/quickstart/advanced\n", - "\n", - "Install required dependencies:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ad00863", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -r requirements.txt" - ] - }, - { - "cell_type": "markdown", - "id": "84988464", - "metadata": { - "raw_mimetype": "text/markdown" - }, - "source": [ - "If you are running MacOS use the following pip command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31a9cdfa", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -r requirements-macos.txt" - ] - }, - { - "cell_type": "markdown", - "id": "45393b74", - "metadata": {}, - "source": [ - "## Define the model\n", - "\n", - "First let's initiate the dataset we'll be using and then create a Model which we will use to train." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "caeff07d", - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "\n", - "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n", - "from tensorflow.keras import Model\n", - "\n", - "import bentoml\n", - "\n", - "print(\"TensorFlow version:\", tf.__version__)\n", - "\n", - "mnist = tf.keras.datasets.mnist\n", - "\n", - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - "x_train = x_train.reshape(60000, 28, 28, 1).astype(\"float32\") / 255\n", - "x_test = x_test.reshape(10000, 28, 28, 1).astype(\"float32\") / 255\n", - "\n", - "# Reserve 10,000 samples for validation\n", - "x_val = x_train[-10000:]\n", - "y_val = y_train[-10000:]\n", - "x_train = x_train[:-10000]\n", - "y_train = y_train[:-10000]\n", - "\n", - "\n", - "class MyModel(Model):\n", - " def __init__(self):\n", - " super(MyModel, self).__init__()\n", - " self.conv1 = Conv2D(32, 3, activation=\"relu\")\n", - " self.flatten = Flatten()\n", - " self.d1 = Dense(128, activation=\"relu\")\n", - " self.d2 = Dense(10)\n", - "\n", - " @tf.function(input_signature=[tf.TensorSpec([None, 28, 28, 1], tf.float32)])\n", - " def call(self, x):\n", - " x = self.conv1(x)\n", - " x = self.flatten(x)\n", - " x = self.d1(x)\n", - " return self.d2(x)\n", - "\n", - "\n", - "# Create an instance of the model\n", - "model = MyModel()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fd7e68a-427b-4563-9b34-d8465c2db832", - "metadata": {}, - "outputs": [], - "source": [ - "model(x_test[0:1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b6f8c40-b4b9-4824-b9cf-aea4959c27a5", - "metadata": {}, - "outputs": [], - "source": [ - "model.compile(\n", - " optimizer=tf.keras.optimizers.Adam(), # Optimizer\n", - " # Loss function to minimize\n", - " loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n", - " # List of metrics to monitor\n", - " metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "38888f0a", - "metadata": {}, - "source": [ - "## Training and Saving the model\n", - "\n", - "Then we initialize some simple tensorflow helper functions and create the training and testing methods" - ] - }, - { - "cell_type": "markdown", - "id": "ad2104a6", - "metadata": {}, - "source": [ - "### Training the model\n", - "\n", - "As provided by TensorFlow, we train and test the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b85432d-4954-4321-bdcd-a65e292d9db3", - "metadata": {}, - "outputs": [], - "source": [ - "history = model.fit(\n", - " x_train,\n", - " y_train,\n", - " batch_size=64,\n", - " epochs=2,\n", - " # We pass some validation for\n", - " # monitoring validation loss and metrics\n", - " # at the end of each epoch\n", - " validation_data=(x_val, y_val),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fae77afe-a3d7-45d3-a86c-d1bd59dde8b5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "92d9b23c", - "metadata": {}, - "source": [ - "### Saving the model\n", - "\n", - "Finally, we make one call to the bentoml library to save this tensorflow model to be used later as part of the prediction service that we will create." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fe9c4a7", - "metadata": {}, - "outputs": [], - "source": [ - "bentoml.tensorflow.save_model(\n", - " \"tensorflow_mnist\",\n", - " model,\n", - " signatures={\"__call__\": {\"batchable\": True, \"batch_dim\": 0}},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bdf35e55", - "metadata": {}, - "source": [ - "## Create a BentoML Service for serving the model\n", - "\n", - "Note: using `%%writefile` here because `bentoml.Service` instance must be created in a separate `.py` file\n", - "\n", - "Even though we have only one model, we can create as many api endpoints as we want. Here we create two end points `predict_ndarray` and `predict_image`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3e2f590", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile service.py\n", - "\n", - "import bentoml\n", - "import numpy as np\n", - "from bentoml.io import Image, NumpyNdarray\n", - "from PIL.Image import Image as PILImage\n", - "\n", - "mnist_runner = bentoml.tensorflow.get(\"tensorflow_mnist:latest\").to_runner()\n", - "\n", - "svc = bentoml.Service(\n", - " name=\"tensorflow_mnist_demo\",\n", - " runners=[mnist_runner],\n", - ")\n", - "\n", - "@svc.api(input=Image(), output=NumpyNdarray(dtype=\"float32\"))\n", - "async def predict_image(f: PILImage) -> \"np.ndarray\":\n", - " assert isinstance(f, PILImage)\n", - " arr = np.array(f)/255.0\n", - " assert arr.shape == (28, 28)\n", - "\n", - " # We are using greyscale image and our PyTorch model expect one\n", - " # extra channel dimension\n", - " arr = np.expand_dims(arr, (0, 3)).astype(\"float32\") # reshape to [1, 28, 28, 1]\n", - " return await mnist_runner.async_run(arr)" - ] - }, - { - "cell_type": "markdown", - "id": "590147aa", - "metadata": {}, - "source": [ - "Start a dev model server to test out the service defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29173871", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve service.py:svc --reload" - ] - }, - { - "cell_type": "markdown", - "id": "606c1b36", - "metadata": {}, - "source": [ - "Now you can use something like:\n", - "\n", - "`curl -H \"Content-Type: multipart/form-data\" -F'fileobj=@samples/0.png;type=image/png' http://127.0.0.1:3000/predict_image`\n", - " \n", - "to send an image to the digit recognition service." - ] - }, - { - "cell_type": "markdown", - "id": "6372c692", - "metadata": {}, - "source": [ - "We can also do a simple local benchmark if [locust](https://locust.io/) is installed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a88e9879", - "metadata": {}, - "outputs": [], - "source": [ - "!locust --headless -u 500 -r 10 --run-time 10m --host http://127.0.0.1:3000" - ] - }, - { - "cell_type": "markdown", - "id": "c7f03564", - "metadata": {}, - "source": [ - "## Build a Bento for distribution and deployment" - ] - }, - { - "cell_type": "markdown", - "id": "f51f7406", - "metadata": {}, - "source": [ - "A `bentofile` is already created in this directory for building a Bento for the service:" - ] - }, - { - "cell_type": "markdown", - "id": "3757297b", - "metadata": {}, - "source": [ - "```yaml\n", - "service: \"service:svc\"\n", - "description: \"file: ./README.md\"\n", - "labels:\n", - " owner: bentoml-team\n", - " stage: demo\n", - "include:\n", - "- \"*.py\"\n", - "exclude:\n", - "- \"tests/\"\n", - "python:\n", - " lock_packages: False\n", - " packages:\n", - " - tensorflow\n", - " - Pillow\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "2aa8e50a", - "metadata": {}, - "source": [ - "Note that we exclude `tests/` from the bento using exclude.\n", - "\n", - "Simply run `bentoml build` from current directory to build a Bento with the latest version of the `tensorflow_mnist` model. This may take a while when running for the first time for BentoML to resolve all dependency versions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d37b339", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml build" - ] - }, - { - "cell_type": "markdown", - "id": "36306933", - "metadata": {}, - "source": [ - "Starting a dev server with the Bento build:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec4b9dff", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve tensorflow2_demo:latest" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "tf2-py3.7", - "language": "python", - "name": "tf2-py3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.13" - }, - "name": "tensorflow2_mnist_demo.ipynb" - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tensorflow2_keras/tests/conftest.py b/examples/tensorflow2_keras/tests/conftest.py deleted file mode 100644 index f8ebb4b4151..00000000000 --- a/examples/tensorflow2_keras/tests/conftest.py +++ /dev/null @@ -1,21 +0,0 @@ -# pylint: disable=redefined-outer-name - -import pathlib -import subprocess -import typing as t - -import pytest - -from bentoml.testing.server import host_bento - - -def pytest_configure(config): # pylint: disable=unused-argument - import sys - - subprocess.check_call([sys.executable, pathlib.Path("train.py").absolute()]) - - -@pytest.fixture(scope="session") -def host() -> t.Generator[str, None, None]: - with host_bento(bento="tensorflow_mnist_demo:latest") as host: - yield host diff --git a/examples/tensorflow2_keras/tests/test_prediction.py b/examples/tensorflow2_keras/tests/test_prediction.py deleted file mode 100644 index 46070dcd530..00000000000 --- a/examples/tensorflow2_keras/tests/test_prediction.py +++ /dev/null @@ -1,72 +0,0 @@ -# pylint: disable=redefined-outer-name -# type: ignore[no-untyped-def] - -import asyncio -import json - -import numpy as np -import pytest - -from bentoml.testing.utils import async_request - - -@pytest.fixture() -def img_data(): - import PIL.Image - - images = {} - digits = list(range(10)) - for digit in digits: - img_path = f"samples/{digit}.png" - with open(img_path, "rb") as f: - img_bytes = f.read() - img_arr = np.array(PIL.Image.open(f"samples/{digit}.png")) - img_arr = img_arr / 255.0 - img_dic = { - "bytes": img_bytes, - "array": img_arr, - } - images[digit] = img_dic - - return images - - -@pytest.mark.asyncio -async def test_numpy(host, img_data): - datas = [json.dumps(d["array"].tolist()) for d in img_data.values()] - - # request one by one - for data in datas[:-3]: - await async_request( - "POST", - f"http://{host}/predict_ndarray", - headers={"Content-Type": "application/json"}, - data=datas[0], - assert_status=200, - ) - - # request all at once, should trigger micro-batch prediction - tasks = tuple( - async_request( - "POST", - f"http://{host}/predict_ndarray", - headers={"Content-Type": "application/json"}, - data=data, - assert_status=200, - ) - for data in datas[-3:] - ) - await asyncio.gather(*tasks) - - -@pytest.mark.asyncio -async def test_image(host, img_data): - for d in img_data.values(): - img_bytes = d["bytes"] - await async_request( - "POST", - f"http://{host}/predict_image", - data=img_bytes, - headers={"Content-Type": "image/png"}, - assert_status=200, - ) diff --git a/examples/tensorflow2_keras/train.py b/examples/tensorflow2_keras/train.py deleted file mode 100644 index f5ea85f1493..00000000000 --- a/examples/tensorflow2_keras/train.py +++ /dev/null @@ -1,67 +0,0 @@ -# pylint: disable=no-name-in-module,redefined-outer-name,abstract-method -import tensorflow as tf -from tensorflow.keras import Model -from tensorflow.keras.layers import Conv2D -from tensorflow.keras.layers import Dense -from tensorflow.keras.layers import Flatten - -import bentoml - -print("TensorFlow version:", tf.__version__) - - -class MyModel(Model): - def __init__(self): - super(MyModel, self).__init__() - self.conv1 = Conv2D(32, 3, activation="relu") - self.flatten = Flatten() - self.d1 = Dense(128, activation="relu") - self.d2 = Dense(10) - - @tf.function(input_signature=[tf.TensorSpec([None, 28, 28, 1], tf.float32)]) - def call(self, x): - x = self.conv1(x) - x = self.flatten(x) - x = self.d1(x) - return self.d2(x) - - -if __name__ == "__main__": - mnist = tf.keras.datasets.mnist - - (x_train, y_train), (x_test, y_test) = mnist.load_data() - - x_train = x_train.reshape(60000, 28, 28, 1).astype("float32") / 255 - x_test = x_test.reshape(10000, 28, 28, 1).astype("float32") / 255 - - # Reserve 10,000 samples for validation - x_val = x_train[-10000:] - y_val = y_train[-10000:] - x_train = x_train[:-10000] - y_train = y_train[:-10000] - - # Create an instance of the model - model = MyModel() - model.compile( - optimizer=tf.keras.optimizers.Adam(), # Optimizer - # Loss function to minimize - loss=tf.keras.losses.SparseCategoricalCrossentropy(), - # List of metrics to monitor - metrics=[tf.keras.metrics.SparseCategoricalAccuracy()], - ) - history = model.fit( - x_train, - y_train, - batch_size=64, - epochs=2, - # We pass some validation for - # monitoring validation loss and metrics - # at the end of each epoch - validation_data=(x_val, y_val), - ) - - bentoml.tensorflow.save_model( - "tensorflow_mnist", - model, - signatures={"__call__": {"batchable": True, "batch_dim": 0}}, - ) diff --git a/examples/tensorflow2_native/.bentoignore b/examples/tensorflow2_native/.bentoignore deleted file mode 100644 index f4e455d1cb8..00000000000 --- a/examples/tensorflow2_native/.bentoignore +++ /dev/null @@ -1,4 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -.ipynb_checkpoints diff --git a/examples/tensorflow2_native/README.md b/examples/tensorflow2_native/README.md deleted file mode 100644 index 654ae65e211..00000000000 --- a/examples/tensorflow2_native/README.md +++ /dev/null @@ -1,248 +0,0 @@ -# BentoML TensorFlow 2 Tutorial - -This is a sample project demonstrating usage of BentoML following the advanced TensorFlow2 quickstart here: https://www.tensorflow.org/tutorials/quickstart/advanced - -In this project, will train a model using Tensorflow2 library and the MNIST dataset. We will then build -an ML service for the model, serve the model behind an HTTP endpoint, and containerize the model -server as a docker image for production deployment. - -This project is also available to run from a notebook: https://github.com/bentoml/BentoML/blob/main/examples/tensorflow2/tensorflow2_mnist_demo.ipynb - -### Install Dependencies - -First install the requirements for this guide -```bash -pip install -r requirements.txt -``` - -For MacOS 11+ -```bash -pip install -r requirements-macos.txt -``` - -At the time of this writing, for M1 Macbooks, if you are getting the following error: -```bash -ERROR: Could not build wheels for h5py, which is required to install pyproject.toml-based projects -``` -Then you'll need to install and configure the h5py library through brew like this: -```bash -brew install hdf5 -export CPATH="/opt/homebrew/include/" -export HDF5_DIR=/opt/homebrew/ -``` - -Then try running the "pip install tensorflow-macos" again - - -### Model Training - -First step, train a classification model with tensorflow's built in mnist dataset and save the model -with BentoML: - -```bash -python train.py -``` - -If you look at the last line of train.py, you'll see: -````python -bentoml.tensorflow.save_model("tensorflow_mnist", model) -```` - - -This should save a new model in the BentoML local model store under model name "tensorflow_mnist: - -```bash -bentoml models list -``` - -Verify that the model can be loaded as runner from Python shell: - -```python -import numpy as np -import PIL.Image - -import bentoml - -runner = bentoml.tensorflow.get("tensorflow_mnist:latest").to_runner() -runner.init_local() # for debug only. please do not call this in the service - -img = PIL.Image.open("samples/0.png") -arr = np.array(img) / 255.0 -arr = arr.astype("float32") - -# add color channel dimension for greyscale image -arr = np.expand_dims(arr, 2) -runner.run(arr) # => returns an array of probabilities for numbers 0-9 -``` - -### Create ML Service - -The ML Service code is defined in the `service.py` file: - -```python -# service.py -import typing as t - -import numpy as np -import PIL.Image -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - - -mnist_runner = bentoml.tensorflow.get("tensorflow_mnist").to_runner() - -svc = bentoml.Service( - name="tensorflow_mnist_demo", - runners=[ - mnist_runner, - ], -) - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="int64")) -async def predict_image(f: PILImage) -> "np.ndarray[t.Any, np.dtype[t.Any]]": - assert isinstance(f, PILImage) - arr = np.array(f)/255.0 - assert arr.shape == (28, 28) - - # extra channel dimension - arr = np.expand_dims(arr, (0, 3)).astype("float32") - output_tensor = await mnist_runner.async_run(arr) - return output_tensor.numpy() -``` - -We defined two api endpoints `/predict_ndarray` and `/predict_image` with single runner. - -Start an API server locally to test the service code above: - -```bash -bentoml serve service:svc --development --reload -``` - -With the `--reload` flag, the API server will automatically restart when the source -file `service.py` is being edited, to boost your development productivity. - - -Verify the endpoint can be accessed locally: -```bash -curl -H "Content-Type: multipart/form-data" -F'fileobj=@samples/0.png;type=image/png' http://127.0.0.1:3000/predict_image -``` - -We can also do a simple local benchmark if [locust](https://locust.io) is installed: -```bash -locust --headless -u 100 -r 1000 --run-time 10m --host http://127.0.0.1:3000 -``` - - -### Build Bento for deployment - -A `bentofile` is already created in this directory for building a -Bento for the service: - -```yaml -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -exclude: -- "locustfile.py" -python: - lock_packages: false - packages: - - tensorflow -``` - -Note that we exclude `locustfile.py` from the bento using `exclude`. - -Simply run `bentoml build` from current directory to build a Bento with the latest -version of the `tensorflow_mnist` model. - -This may take a while when running for the first time for BentoML to resolve all dependency versions: - -``` -> bentoml build - -[01:14:04 AM] INFO Building BentoML service "tensorflow_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq" from build context - "/home/chef/workspace/gallery/tensorflow2" - INFO Packing model "tensorflow_mnist_demo:xm6jsddtu3y6zluuvcqvsoywq" from - "/home/chef/bentoml/models/tensorflow_mnist_demo/xm6jsddtu3y6zluuvcqvsoywq" - INFO Locking PyPI package versions.. -[01:14:05 AM] INFO - ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ - ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ - ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ - ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ - ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ - ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ - - INFO Successfully built Bento(tag="tensorflow_mnist_demo:bmygukdtzpy6zlc5vcqvsoywq") at - "/home/chef/bentoml/bentos/tensorflow_mnist_demo/bmygukdtzpy6zlc5vcqvsoywq/" -``` - -This Bento can now be loaded for serving: - -```bash -bentoml serve tensorflow_mnist_demo:latest -``` - -The Bento directory contains all code, files, models and configs required for running this service. -BentoML standarlizes this file structure which enables serving runtimes and deployment tools to be -built on top of it. By default, Bentos are managed under the `~/bentoml/bentos` directory: - -``` -> cd ~/bentoml/bentos/tensorflow_mnist_demo && cd $(cat latest) - -> tree -. -├── README.md -├── apis -│   └── openapi.yaml -├── bento.yaml -├── env -│   ├── conda -│   ├── docker -│   │   ├── Dockerfile -│   │   ├── entrypoint.sh -│   │   └── init.sh -│   └── python -│   ├── requirements.lock.txt -│   ├── requirements.txt -│   └── version.txt -├── models -│   └── tensorflow_mnist -│   ├── latest -│   └── wz77wdeuegyh2du5 -│   ├── assets -│   ├── model.yaml -│   ├── saved_model.pb -│   └── variables -│   ├── variables.data-00000-of-00001 -│   └── variables.index -└── src - ├── service.py - └── train.py - -11 directories, 16 files -``` - - -### Containerize Bento for deployment - -Make sure you have docker installed and docker deamon running, and the following command -will use your local docker environment to build a new docker image, containing the model -server configured from this Bento: - -```bash -bentoml containerize tensorflow_mnist_demo:latest -``` - -Test out the docker image built: -```bash -docker run -p 3000:3000 tensorflow_mnist_demo:latest -``` diff --git a/examples/tensorflow2_native/bentofile.yaml b/examples/tensorflow2_native/bentofile.yaml deleted file mode 100644 index f5e217c1cc4..00000000000 --- a/examples/tensorflow2_native/bentofile.yaml +++ /dev/null @@ -1,14 +0,0 @@ -service: "service:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: -- "*.py" -exclude: -- "locustfile.py" -python: - lock_packages: false - packages: - - tensorflow - - Pillow diff --git a/examples/tensorflow2_native/locustfile.py b/examples/tensorflow2_native/locustfile.py deleted file mode 100644 index c4c8103eec6..00000000000 --- a/examples/tensorflow2_native/locustfile.py +++ /dev/null @@ -1,15 +0,0 @@ -from locust import HttpUser -from locust import between -from locust import task - -with open("samples/0.png", "rb") as f: - test_image_bytes = f.read() - - -class TensorFlow2MNISTLoadTestUser(HttpUser): - wait_time = between(0.9, 1.1) - - @task - def predict_image(self): - files = {"upload_files": ("1.png", test_image_bytes, "image/png")} - self.client.post("/predict_image", files=files) diff --git a/examples/tensorflow2_native/requirements-macos.txt b/examples/tensorflow2_native/requirements-macos.txt deleted file mode 100644 index 7ab4f93a7a7..00000000000 --- a/examples/tensorflow2_native/requirements-macos.txt +++ /dev/null @@ -1,3 +0,0 @@ -tensorflow-macos -pillow -bentoml>=1.0.0 diff --git a/examples/tensorflow2_native/requirements.txt b/examples/tensorflow2_native/requirements.txt deleted file mode 100644 index 80374bccdf6..00000000000 --- a/examples/tensorflow2_native/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -tensorflow -pillow -bentoml>=1.0.0 diff --git a/examples/tensorflow2_native/samples/0.png b/examples/tensorflow2_native/samples/0.png deleted file mode 100644 index 7a93ea81868246ab670b0998b1f919c6becdb1b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 290 zcmV+-0p0$IP)C?}1GH8<5{%+pM2`k&OErf#59o8;c?- zdE|{BjLm=Ys~3tSgVfD8J`k40VYGk;0O^TWT0NuJ>4Ozh@fB*mh07*qoM6N<$g5cAGv;Y7A diff --git a/examples/tensorflow2_native/samples/1.png b/examples/tensorflow2_native/samples/1.png deleted file mode 100644 index 4629a3b16940439e66f1c992caa16eed1fe5c514..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119 zcmeAS@N?(olHy`uVBq!ia0vp^G9b(WBpAZe8a#lMou`XqNX4Aw1c@sNyykh9)9%ae zP`&&A-;v}u>nAGx0F6*2UngC7tDjNU* diff --git a/examples/tensorflow2_native/samples/2.png b/examples/tensorflow2_native/samples/2.png deleted file mode 100644 index e56adf47bdddc8ab1b02ff8004db08c0db8d8ef4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 285 zcmV+&0pk9NP)7GZwp z<#U~#qyPNDB5Aty4>W{iG2}Sk|M~i`@*+sGv!$iQ4#mu^e{KXaFiZtW{`qk9Xfd*+ z^PjJl3@kbC{(OosH`hip)%i~&184c4Ki{3PIMw;j%vsO>{JB$x&3k(9K~6oZhfR)w j!R^PNYg)Y6<>(0jw0UquY^9}n00000NkvXXu0mjf_3(`M diff --git a/examples/tensorflow2_native/samples/3.png b/examples/tensorflow2_native/samples/3.png deleted file mode 100644 index d441e3d6dd019458953cda989bb2f4eae15f605e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 218 zcmV<0044v4P)>l;K;YT9lLNzK21PlFjbfB$Ykmt6Me z&vY=O`Olwe=#qc`JP-ymR{nw6jVxIPrhPtRO7i|sZ_t1Q1WOLze>rcE|3X?u~yw>SK|EfY85-amoS1w+pA7@by2t zaZ1kp^T!^ia9g?ZPdAy8<=13zOOgTr9C}v)DAySIu3dZf{qNtu=#uOI{{06O1`(G!(d25r|9yMy>Q(pV zjVr=A(d2BO{(WnO%Ps%^y*9<=(7u0v8*$0Wy#DuIf>#K;r1{@}zhAw5_xjbc5Ol{d zFm%9u@izfOQsdwMe^)b;+PA#<_fH&Ml1*J*od?8_e)~5Gm%|tsmi=9d!WTg1O#izD zMRJQAf-V2?uMLXip?Pp-`oDkAQLUNt$%qNU3V8ANR~D*dz=NQ<$9XXu-CX=khd|1P(bF7wQuu#(f_&u$itav%0~l@pG}g(qJA{r~&B z7N2FiwZq)d8&A*VGyJ{TaCl|i0*4O=^^O|~YE*{F?_R>8k#oDi!d-Iy6VsYWI~`8$ lF0)XH^sfJZOtdJ1fnoWFs%ar#Z5D(4;pyt?jFW%2+3 diff --git a/examples/tensorflow2_native/samples/8.png b/examples/tensorflow2_native/samples/8.png deleted file mode 100644 index f5e6652c8c1d3b220e2ea8682e89bb0c1890205e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 304 zcmV-00nh%4P)+{~`c}lUX|8 za>p<|#N2tS{S{c!15=WLiG@MG(Y3LWn-Pm7JpceFxVv5wzv{mL0000k8tHIkL>z1Ox>W z6Mp<=pIxk|z`J^`fu~HF(6!8Ne|6@425fC2awjG0|F1u&sad{sCU<|_BL;@G{{jlz S^4~89x!Tj!&t;ucLK6Upep#mg diff --git a/examples/tensorflow2_native/service.py b/examples/tensorflow2_native/service.py deleted file mode 100644 index 821568032f6..00000000000 --- a/examples/tensorflow2_native/service.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -from PIL.Image import Image as PILImage - -import bentoml -from bentoml.io import Image -from bentoml.io import NumpyNdarray - -mnist_runner = bentoml.tensorflow.get("tensorflow_mnist:latest").to_runner() - -svc = bentoml.Service( - name="tensorflow_mnist_demo", - runners=[mnist_runner], -) - - -@svc.api(input=Image(), output=NumpyNdarray(dtype="float32")) -async def predict_image(f: PILImage) -> "np.ndarray": - assert isinstance(f, PILImage) - arr = np.array(f) / 255.0 - assert arr.shape == (28, 28) - - # We are using greyscale image and our PyTorch model expect one - # extra channel dimension - arr = np.expand_dims(arr, (0, 3)).astype("float32") # reshape to [1, 28, 28, 1] - return await mnist_runner.async_run(arr) diff --git a/examples/tensorflow2_native/tensorflow2_mnist_demo.ipynb b/examples/tensorflow2_native/tensorflow2_mnist_demo.ipynb deleted file mode 100644 index 9b9268febba..00000000000 --- a/examples/tensorflow2_native/tensorflow2_mnist_demo.ipynb +++ /dev/null @@ -1,453 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a682ea0b", - "metadata": {}, - "source": [ - "# BentoML TensorFlow2 MNIST Tutorial\n", - "\n", - "Link to source code: https://github.com/bentoml/BentoML/tree/main/examples/tensorflow2_mnist/\n", - "\n", - "The code is based on the TensorFlow2 example code here: https://www.tensorflow.org/tutorials/quickstart/advanced\n", - "\n", - "Install required dependencies:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ad00863", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -r requirements.txt" - ] - }, - { - "cell_type": "markdown", - "id": "84988464", - "metadata": { - "raw_mimetype": "text/markdown" - }, - "source": [ - "If you are running MacOS use the following pip command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31a9cdfa", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -r requirements-macos.txt" - ] - }, - { - "cell_type": "markdown", - "id": "45393b74", - "metadata": {}, - "source": [ - "## Define the model\n", - "\n", - "First let's initiate the dataset we'll be using and then create a Model which we will use to train." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef7a6ef8-c18b-4f29-b413-e5055648c1cc", - "metadata": {}, - "outputs": [], - "source": [ - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "x_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "caeff07d", - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "\n", - "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n", - "\n", - "\n", - "import bentoml\n", - "\n", - "print(\"TensorFlow version:\", tf.__version__)\n", - "\n", - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "x_train, x_test = x_train / 255.0, x_test / 255.0\n", - "\n", - "# Add a channels dimension\n", - "x_train = x_train.reshape(60000, 28, 28, 1).astype(\"float32\")\n", - "x_test = x_test.reshape(10000, 28, 28, 1).astype(\"float32\")\n", - "\n", - "train_ds = (\n", - " tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)\n", - ")\n", - "\n", - "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)\n", - "\n", - "\n", - "class MyModel(tf.Module):\n", - " def __init__(self):\n", - " super(MyModel, self).__init__()\n", - " self.conv1 = Conv2D(32, 3, activation=\"relu\")\n", - " self.flatten = Flatten()\n", - " self.d1 = Dense(128, activation=\"relu\")\n", - " self.d2 = Dense(10)\n", - "\n", - " @tf.function(input_signature=[tf.TensorSpec([None, 28, 28, 1], tf.float32)])\n", - " def __call__(self, x):\n", - " x = self.conv1(x)\n", - " x = self.flatten(x)\n", - " x = self.d1(x)\n", - " return self.d2(x)\n", - "\n", - "\n", - "# Create an instance of the model\n", - "model = MyModel()" - ] - }, - { - "cell_type": "markdown", - "id": "38888f0a", - "metadata": {}, - "source": [ - "## Training and Saving the model\n", - "\n", - "Then we initialize some simple tensorflow helper functions and create the training and testing methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c62db15c", - "metadata": {}, - "outputs": [], - "source": [ - "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", - "\n", - "optimizer = tf.keras.optimizers.Adam()\n", - "\n", - "train_loss = tf.keras.metrics.Mean(name=\"train_loss\")\n", - "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name=\"train_accuracy\")\n", - "\n", - "test_loss = tf.keras.metrics.Mean(name=\"test_loss\")\n", - "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name=\"test_accuracy\")" - ] - }, - { - "cell_type": "markdown", - "id": "788c19a0", - "metadata": {}, - "source": [ - "### Training and Testing TF Steps\n", - "\n", - "Now we assemble our TensorFlow2 training and testing steps. We use @tf.function as the new way (a part of TensorFlow2) to initialize a TensorFlow session.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b2fdd72", - "metadata": {}, - "outputs": [], - "source": [ - "def train_step(images, labels):\n", - " with tf.GradientTape() as tape:\n", - " # training=True is only needed if there are layers with different\n", - " # behavior during training versus inference (e.g. Dropout).\n", - " predictions = model(images)\n", - " loss = loss_object(labels, predictions)\n", - " gradients = tape.gradient(loss, model.trainable_variables)\n", - " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", - "\n", - " train_loss(loss)\n", - " train_accuracy(labels, predictions)\n", - "\n", - "\n", - "def test_step(images, labels):\n", - " # training=False is only needed if there are layers with different\n", - " # behavior during training versus inference (e.g. Dropout).\n", - " predictions = model(images)\n", - " t_loss = loss_object(labels, predictions)\n", - "\n", - " test_loss(t_loss)\n", - " test_accuracy(labels, predictions)" - ] - }, - { - "cell_type": "markdown", - "id": "ad2104a6", - "metadata": {}, - "source": [ - "### Training the model\n", - "\n", - "As provided by TensorFlow, we train and test the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3d311c5", - "metadata": {}, - "outputs": [], - "source": [ - "EPOCHS = 2\n", - "\n", - "for epoch in range(EPOCHS):\n", - " # Reset the metrics at the start of the next epoch\n", - " train_loss.reset_states()\n", - " train_accuracy.reset_states()\n", - " test_loss.reset_states()\n", - " test_accuracy.reset_states()\n", - "\n", - " for images, labels in train_ds:\n", - " train_step(images, labels)\n", - "\n", - " for test_images, test_labels in test_ds:\n", - " test_step(test_images, test_labels)\n", - "\n", - " print(\n", - " f\"Epoch {epoch + 1}, \"\n", - " f\"Loss: {train_loss.result()}, \"\n", - " f\"Accuracy: {train_accuracy.result() * 100}, \"\n", - " f\"Test Loss: {test_loss.result()}, \"\n", - " f\"Test Accuracy: {test_accuracy.result() * 100}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "92d9b23c", - "metadata": {}, - "source": [ - "### Saving the model\n", - "\n", - "Finally, we make one call to the bentoml library to save this tensorflow model to be used later as part of the prediction service that we will create." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fe9c4a7", - "metadata": {}, - "outputs": [], - "source": [ - "bentoml.tensorflow.save_model(\n", - " \"tensorflow_mnist\",\n", - " model,\n", - " signatures={\"__call__\": {\"batchable\": True, \"batch_dim\": 0}},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bdf35e55", - "metadata": {}, - "source": [ - "## Create a BentoML Service for serving the model\n", - "\n", - "Note: using `%%writefile` here because `bentoml.Service` instance must be created in a separate `.py` file\n", - "\n", - "Even though we have only one model, we can create as many api endpoints as we want. Here we create two end points `predict_ndarray` and `predict_image`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3e2f590", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile service.py\n", - "\n", - "import bentoml\n", - "import numpy as np\n", - "from bentoml.io import Image, NumpyNdarray\n", - "from PIL.Image import Image as PILImage\n", - "\n", - "mnist_runner = bentoml.tensorflow.get(\"tensorflow_mnist:latest\").to_runner()\n", - "\n", - "svc = bentoml.Service(\n", - " name=\"tensorflow_mnist_demo\",\n", - " runners=[mnist_runner],\n", - ")\n", - "\n", - "@svc.api(input=Image(), output=NumpyNdarray(dtype=\"float32\"))\n", - "async def predict_image(f: PILImage) -> \"np.ndarray\":\n", - " assert isinstance(f, PILImage)\n", - " arr = np.array(f)/255.0\n", - " assert arr.shape == (28, 28)\n", - "\n", - " # We are using greyscale image and our PyTorch model expect one\n", - " # extra channel dimension\n", - " arr = np.expand_dims(arr, (0, 3)).astype(\"float32\") # reshape to [1, 28, 28, 1]\n", - " return await mnist_runner.async_run(arr)" - ] - }, - { - "cell_type": "markdown", - "id": "590147aa", - "metadata": {}, - "source": [ - "Start a dev model server to test out the service defined above" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "29173871", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "^C\n" - ] - } - ], - "source": [ - "!bentoml serve service.py:svc --reload" - ] - }, - { - "cell_type": "markdown", - "id": "606c1b36", - "metadata": {}, - "source": [ - "Now you can use something like:\n", - "\n", - "`curl -H \"Content-Type: multipart/form-data\" -F'fileobj=@samples/0.png;type=image/png' http://127.0.0.1:3000/predict_image`\n", - " \n", - "to send an image to the digit recognition service." - ] - }, - { - "cell_type": "markdown", - "id": "6372c692", - "metadata": {}, - "source": [ - "We can also do a simple local benchmark if [locust](https://locust.io/) is installed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a88e9879", - "metadata": {}, - "outputs": [], - "source": [ - "!locust --headless -u 500 -r 10 --run-time 10m --host http://127.0.0.1:3000" - ] - }, - { - "cell_type": "markdown", - "id": "c7f03564", - "metadata": {}, - "source": [ - "## Build a Bento for distribution and deployment" - ] - }, - { - "cell_type": "markdown", - "id": "f51f7406", - "metadata": {}, - "source": [ - "A `bentofile` is already created in this directory for building a Bento for the service:" - ] - }, - { - "cell_type": "markdown", - "id": "3757297b", - "metadata": {}, - "source": [ - "```yaml\n", - "service: \"service:svc\"\n", - "description: \"file: ./README.md\"\n", - "labels:\n", - " owner: bentoml-team\n", - " stage: demo\n", - "include:\n", - "- \"*.py\"\n", - "exclude:\n", - "- \"tests/\"\n", - "python:\n", - " lock_packages: False\n", - " packages:\n", - " - tensorflow\n", - " - Pillow\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "2aa8e50a", - "metadata": {}, - "source": [ - "Note that we exclude `tests/` from the bento using exclude.\n", - "\n", - "Simply run `bentoml build` from current directory to build a Bento with the latest version of the `tensorflow_mnist` model. This may take a while when running for the first time for BentoML to resolve all dependency versions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d37b339", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml build" - ] - }, - { - "cell_type": "markdown", - "id": "36306933", - "metadata": {}, - "source": [ - "Starting a dev server with the Bento build:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec4b9dff", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve tensorflow2_demo:latest" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "tf2-py3.7", - "language": "python", - "name": "tf2-py3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.13" - }, - "name": "tensorflow2_mnist_demo.ipynb" - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tensorflow2_native/tests/conftest.py b/examples/tensorflow2_native/tests/conftest.py deleted file mode 100644 index f8ebb4b4151..00000000000 --- a/examples/tensorflow2_native/tests/conftest.py +++ /dev/null @@ -1,21 +0,0 @@ -# pylint: disable=redefined-outer-name - -import pathlib -import subprocess -import typing as t - -import pytest - -from bentoml.testing.server import host_bento - - -def pytest_configure(config): # pylint: disable=unused-argument - import sys - - subprocess.check_call([sys.executable, pathlib.Path("train.py").absolute()]) - - -@pytest.fixture(scope="session") -def host() -> t.Generator[str, None, None]: - with host_bento(bento="tensorflow_mnist_demo:latest") as host: - yield host diff --git a/examples/tensorflow2_native/tests/test_prediction.py b/examples/tensorflow2_native/tests/test_prediction.py deleted file mode 100644 index 46070dcd530..00000000000 --- a/examples/tensorflow2_native/tests/test_prediction.py +++ /dev/null @@ -1,72 +0,0 @@ -# pylint: disable=redefined-outer-name -# type: ignore[no-untyped-def] - -import asyncio -import json - -import numpy as np -import pytest - -from bentoml.testing.utils import async_request - - -@pytest.fixture() -def img_data(): - import PIL.Image - - images = {} - digits = list(range(10)) - for digit in digits: - img_path = f"samples/{digit}.png" - with open(img_path, "rb") as f: - img_bytes = f.read() - img_arr = np.array(PIL.Image.open(f"samples/{digit}.png")) - img_arr = img_arr / 255.0 - img_dic = { - "bytes": img_bytes, - "array": img_arr, - } - images[digit] = img_dic - - return images - - -@pytest.mark.asyncio -async def test_numpy(host, img_data): - datas = [json.dumps(d["array"].tolist()) for d in img_data.values()] - - # request one by one - for data in datas[:-3]: - await async_request( - "POST", - f"http://{host}/predict_ndarray", - headers={"Content-Type": "application/json"}, - data=datas[0], - assert_status=200, - ) - - # request all at once, should trigger micro-batch prediction - tasks = tuple( - async_request( - "POST", - f"http://{host}/predict_ndarray", - headers={"Content-Type": "application/json"}, - data=data, - assert_status=200, - ) - for data in datas[-3:] - ) - await asyncio.gather(*tasks) - - -@pytest.mark.asyncio -async def test_image(host, img_data): - for d in img_data.values(): - img_bytes = d["bytes"] - await async_request( - "POST", - f"http://{host}/predict_image", - data=img_bytes, - headers={"Content-Type": "image/png"}, - assert_status=200, - ) diff --git a/examples/tensorflow2_native/train.py b/examples/tensorflow2_native/train.py deleted file mode 100644 index 20219b7f0d7..00000000000 --- a/examples/tensorflow2_native/train.py +++ /dev/null @@ -1,108 +0,0 @@ -# pylint: disable=no-name-in-module,redefined-outer-name,abstract-method -import tensorflow as tf -from tensorflow.keras.layers import Conv2D -from tensorflow.keras.layers import Dense -from tensorflow.keras.layers import Flatten - -import bentoml - -print("TensorFlow version:", tf.__version__) - - -class MyModel(tf.Module): - def __init__(self): - super(MyModel, self).__init__() - self.conv1 = Conv2D(32, 3, activation="relu") - self.flatten = Flatten() - self.d1 = Dense(128, activation="relu") - self.d2 = Dense(10) - - @tf.function(input_signature=[tf.TensorSpec([None, 28, 28, 1], tf.float32)]) - def __call__(self, x): - x = self.conv1(x) - x = self.flatten(x) - x = self.d1(x) - return self.d2(x) - - -@tf.function -def train_step(images, labels): - with tf.GradientTape() as tape: - # training=True is only needed if there are layers with different - # behavior during training versus inference (e.g. Dropout). - predictions = model(images) - loss = loss_object(labels, predictions) - gradients = tape.gradient(loss, model.trainable_variables) - optimizer.apply_gradients(zip(gradients, model.trainable_variables)) - train_loss(loss) - train_accuracy(labels, predictions) - - -@tf.function -def test_step(images, labels): - # training=False is only needed if there are layers with different - # behavior during training versus inference (e.g. Dropout). - predictions = model(images) - t_loss = loss_object(labels, predictions) - - test_loss(t_loss) - test_accuracy(labels, predictions) - - -if __name__ == "__main__": - mnist = tf.keras.datasets.mnist - - (x_train, y_train), (x_test, y_test) = mnist.load_data() - x_train, x_test = x_train / 255.0, x_test / 255.0 - - # Add a channels dimension - x_train = x_train.reshape(60000, 28, 28, 1).astype("float32") - x_test = x_test.reshape(10000, 28, 28, 1).astype("float32") - - train_ds = ( - tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32) - ) - - test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32) - - # Create an instance of the model - model = MyModel() - - loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) - - optimizer = tf.keras.optimizers.Adam() - - train_loss = tf.keras.metrics.Mean(name="train_loss") - train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name="train_accuracy") - - test_loss = tf.keras.metrics.Mean(name="test_loss") - test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name="test_accuracy") - - EPOCHS = 2 - - for epoch in range(EPOCHS): - # Reset the metrics at the start of the next epoch - train_loss.reset_states() - train_accuracy.reset_states() - test_loss.reset_states() - test_accuracy.reset_states() - - for images, labels in train_ds: - train_step(images, labels) - - for test_images, test_labels in test_ds: - test_step(test_images, test_labels) - - print( - f"Epoch {epoch + 1}, " - f"Loss: {train_loss.result()}, " - f"Accuracy: {train_accuracy.result() * 100}, " - f"Test Loss: {test_loss.result()}, " - f"Test Accuracy: {test_accuracy.result() * 100}" - ) - - bentoml.tensorflow.save_model( - "tensorflow_mnist", - model, - signatures={"__call__": {"batchable": True, "batch_dim": 0}}, - ) diff --git a/examples/triton/onnx/README.md b/examples/triton/onnx/README.md deleted file mode 100644 index 903be70ab18..00000000000 --- a/examples/triton/onnx/README.md +++ /dev/null @@ -1,173 +0,0 @@ -# Triton Inference Server integration - -BentoML now provides support for Triton Inference Server. - -### Quick tour - -Triton Runner can be created via `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "local", model_respository="/path/to/model_repository" -) - -svc = bentoml.Service("my-service", runners=[triton_runner]) -``` - -`model_repository` can also accept S3 path: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", model_repository="s3://path/to/model_repository" -) -``` - -CLI arguments can be passed through the `cli_args` argument of `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", - model_repository="s3://path/to/model_repository", - cli_args=["--load-model=torchscrip_yolov5s", "--model-control-mode=explicit"], -) -``` - -An example of inference API: - -```python -@svc.api( - input=bentoml.io.NumpyNdarray.from_sample( - np.array([[1, 2, 3, 4]]), enforce_dtype=False - ), - output=bentoml.io.NumpyNdarray.from_sample(np.array([1])), -) -def triton_infer(input_data: NDArray[np.float16]) -> NDArray[np.int16]: - iris_res = iris_clf_runner.run(input_data) - res_kwargs = triton_runner.txt2img.run(IRIS_NAME=iris_res) - return iris_res -``` - -Note that each attribute of the `triton_runner` includes the name of all given -models under `model_repository` - -APIs from tritonclient are also provided through the Triton Runner: - -```python -tritonclient.grpc.aio.InferenceServerClient.get_model_metadata -> triton_runner.get_model_metadata | triton_runner.grpc_get_model_metadata -``` - -To get build your Bento with Triton, add the following to your `bentofile.yaml`: - -```yaml -service: "service.py:svc" -include: - - *.py - - /model_repository -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 -``` - -> tritonserver are currently unsupported with `--development` tag. Make sure -> to have `tritonserver` binary available in PATH if running locally. - -To find out more about BentoML Runner architecture, see -[our latest documentation](https://docs.bentoml.com/en/latest/concepts/runner.html#) - -For more information about Triton Inference Server, see -[here](https://github.com/triton-inference-server/server) - -### Instruction - -The following project includes YOLOv5 `TritonRunner` and `bentoml.Runner`. - -1. Setup Triton model repository and BentoML model: - -```bash -./export-yolov5-weights - -python3 train.py -``` - -2. To build the Bento, use [`build_bento.py`](./build_bento.py): - -```bash -python3 build_bento.py -```` - -> NOTE: To build with custom GPU, pass in `--gpu`. To build with custom tags -> pass in `--tag ` - -3. To containerize use [`containerize_bento.py`](./containerize_bento.py): - -```bash -python3 containerize_bento.py -``` - -4. To run the container with Triton, use `docker run`: - -```bash -docker run --rm -it -p 3000:3000 triton-integration-onnx serve-http -``` - -#### Develop locally: - -1. To run Triton locally, do the following: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -If you have NVIDIA GPU available, make sure to install -[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on your system. -Afterward, passing in `--gpus all` to `docker`: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --gpus all --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -Inside the container shell, there are two options to install BentoML: - -- Install from editable - -```bash -cd /opt/bentoml && pip install -r requirements/dev-requirements.txt -``` - -Run the [`setup` script](./setup): - -```bash -cd /workspace/ && pip install -r requirements/requirements.txt - -bash ./setup -``` - -2. To serve the Bento, use either `bentoml serve` or - [`serve_bento.py`](./serve_bento.py) (this requires to have `tritonserver` binary available locally on your system. To use the container, go to step 5) - -```bash -python3 serve_bento.py - -# bentoml serve-http | serve-grpc triton-integration-onnx -``` - -> NOTE: to serve previously custom tag bento, you can also pass in `--tag` to -> `serve_bento.py` - - -> Feel free to build your own tritonserver. See -> [here](https://github.com/triton-inference-server/server/blob/main/docs/customization_guide/build.md) -> for more details on building customisation. - - diff --git a/examples/triton/onnx/bentofile.yaml b/examples/triton/onnx/bentofile.yaml deleted file mode 100644 index bdeece1e63e..00000000000 --- a/examples/triton/onnx/bentofile.yaml +++ /dev/null @@ -1,16 +0,0 @@ -service: service:svc -include: - - /model_repository - - /data/*.jpg - - /export-yolov5-weights - - /*.py -exclude: - - /__pycache__ - - /venv - - /yolov5 - - /train.py - - /build_bento.py - - /containerize_bento.py -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 - setup_script: setup diff --git a/examples/triton/onnx/build_bento.py b/examples/triton/onnx/build_bento.py deleted file mode 100644 index 25db0034405..00000000000 --- a/examples/triton/onnx/build_bento.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import annotations - -import argparse -import os -import typing as t - -import attr -import torch - -import bentoml -from bentoml import Bento -from bentoml._internal.bento.build_config import BentoBuildConfig -from bentoml._internal.configuration.containers import BentoMLContainer -from bentoml._internal.utils import resolve_user_filepath - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--tag", type=str, default=None) - parser.add_argument("--gpu", action="store_true", default=False) - - args = parser.parse_args() - - bento_tag = "triton-integration-onnx" - if args.tag: - bento_tag = f"triton-integration-onnx:{args.tag}" - - try: - bentos = bentoml.get(bento_tag) - print(f"{bentos} already exists. Skipping...") - except bentoml.exceptions.NotFound: - bentofile = resolve_user_filepath("bentofile.yaml", None) - - override_attrs: dict[str, t.Any] = { - "python": { - "requirements_txt": ( - os.path.join("requirements", "requirements-gpu.txt") - if args.gpu and torch.cuda.is_available() - else os.path.join("requirements", "requirements.txt") - ) - } - } - with open(bentofile, "r", encoding="utf-8") as f: - build_config = attr.evolve(BentoBuildConfig.from_yaml(f), **override_attrs) - - print( - "Saved bento:", - Bento.create(build_config, version=args.tag).save( - BentoMLContainer.bento_store.get() - ), - ) diff --git a/examples/triton/onnx/containerize_bento.py b/examples/triton/onnx/containerize_bento.py deleted file mode 100644 index 440da099d41..00000000000 --- a/examples/triton/onnx/containerize_bento.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-onnx" - if args.tag: - tag = f"triton-integration-onnx:{args.tag}" - - backend = "docker" - try: - builder = bentoml.container.get_backend("buildx") - assert builder.health() - backend = "buildx" - except ValueError: - print("Buildx not found, using default Docker builder.") - try: - builder = bentoml.container.get_backend(backend) - assert builder.health() - except ValueError: - print("Make sure to have Docker running.") - raise - else: - bentoml.container.build( - tag, backend=backend, features=["all"], platform="linux/amd64" - ) diff --git a/examples/triton/onnx/data/bus.jpg b/examples/triton/onnx/data/bus.jpg deleted file mode 100644 index b43e311165c785f000eb7493ff8fb662d06a3f83..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 487438 zcmeFYbyODL*Ec*DA)V4KCDNT2AR-_jqKI^Ncc+4McY{cYlr%_p2}pN$ch@`USHHjY 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"$(dirname "$0")" && pwd)" - -: "${GPU:=false}" - -[ "${UPDATE_YOLOV5}" = "true" ] && [ -d "$BASEDIR/yolov5" ] && \rm -rf yolov5 - -main() { - prev_dir="$(pwd)" - cd "$BASEDIR" - - ! [ -d yolov5 ] && - mkdir -p yolov5 && cd yolov5 && - ! [ -d yolov5.git ] && git clone --bare https://github.com/ultralytics/yolov5.git && - git --git-dir=./yolov5.git --work-tree=. checkout HEAD -- data models utils LICENSE detect.py export.py val.py requirements.txt && \rm -rf yolov5.git && cd "${BASEDIR}" - - exists=0 - ! [ -d "${BASEDIR}/model_repository/onnx_${MODEL_TYPE}/1" ] && mkdir -p "${BASEDIR}/model_repository/onnx_${MODEL_TYPE}/1" - [ -f "${BASEDIR}/${MODEL_TYPE}.onnx" ] && exists=1 - if [ "${exists}" -eq 0 ] || [ "$OVERRIDE" = "true" ]; then - if [ "$GPU" = "true" ]; then - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include onnx torchscript --batch-size "$1" --dynamic --device 0 - else - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include onnx torchscript --batch-size "$1" --dynamic - fi - fi - cp "${BASEDIR}/${MODEL_TYPE}.onnx" "${BASEDIR}/model_repository/onnx_${MODEL_TYPE}/1/model.onnx" - - echo "Successfully export YOLOv5 weights for ONNX" - cd "${prev_dir}" -} - -if ! [ "$#" -eq 1 ]; then - echo "Usage: $0 . Set to 1 by default if not passed." - main 1 -else - main "$@" -fi diff --git a/examples/triton/onnx/helpers.py b/examples/triton/onnx/helpers.py deleted file mode 100644 index 1e27b930e39..00000000000 --- a/examples/triton/onnx/helpers.py +++ /dev/null @@ -1,397 +0,0 @@ -from __future__ import annotations - -import json -import os -import time -import typing as t -from pathlib import Path - -import attr -import cv2 -import numpy as np -import torch -import torchvision -from PIL import Image as PILImage - -if t.TYPE_CHECKING: - from numpy.typing import NDArray - from torch.jit._script import ScriptModule - - P = t.ParamSpec("P") - -WORKING_DIR = Path(__file__).parent -MODEL_TYPE = os.environ.get("MODEL_TYPE", "yolov5s") -MODEL_FILE = WORKING_DIR / f"{MODEL_TYPE}.pt" -TORCH_DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - -def load_traced_script() -> tuple[ScriptModule, dict[str, t.Any]]: - extra_files = {"config.txt": ""} - model = torch.jit.load( - MODEL_FILE.with_suffix(".torchscript").__fspath__(), - map_location=TORCH_DEVICE, - _extra_files=extra_files, - ) - d = {"shape": [], "stride": 32, "names": {}} - if extra_files["config.txt"]: - d = json.loads( - extra_files["config.txt"], - object_hook=lambda d: { - int(k) if k.isdigit() else k: v for k, v in d.items() - }, - ) - return model, d - - -def torch_tensor_from_numpy( - x: torch.Tensor | NDArray[t.Any], device: str = "cpu" -) -> torch.Tensor: - if isinstance(x, np.ndarray): - # Needs to create a copy if the array is not writeable - x = np.copy(x) if not x.flags["WRITEABLE"] else x - return torch.from_numpy(x).to(device) - elif isinstance(x, torch.Tensor): - return x - else: - raise TypeError( - f"Expected numpy.ndarray or torch.Tensor, got {type(x).__name__}" - ) - - -def prepare_yolov5_input( - im_: PILImage.Image, - size: int | tuple[int, int] = (640, 640), - auto: bool = False, - device: str = "cpu", - fp16: bool = False, - stride: int = 32, -) -> TensorContainer: - p = torch.empty(1, device=torch.device(device)) - # Automatic Mixed Precision (AMP) inference - autocast = fp16 and p.device.type != "cpu" - - im0 = np.asarray(exif_transpose(im_)) - # padded resize - im, _, _ = letterbox(im0, size, stride=stride, auto=auto) - im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - im = np.ascontiguousarray(im) # contiguous - - im = torch.from_numpy(im).to(p.device) - im = im.half() if fp16 else im.float() # unit8 to fp16/fp32 - im /= 255 # 0-255 to 0.0-1.0 - if len(im.shape) == 3: - im = im[None] # extends to batch_dim - - return TensorContainer(im, im0, autocast) - - -def postprocess_yolov5_prediction( - y: list[t.Any] | tuple[t.Any, ...] | torch.Tensor | NDArray[t.Any], - prep: TensorContainer, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic_nms: bool = False, - max_det: int = 1000, -): - res: dict[str, str] = {} - names = load_traced_script()[1]["names"] - if isinstance(y, (list, tuple)): - y = ( - torch_tensor_from_numpy(y[0]) - if len(y) == 1 - else [torch_tensor_from_numpy(_) for _ in y] - ) - else: - y = torch_tensor_from_numpy(y) - - y = non_max_suppression( - y, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det - ) - # post - for pred in y: - if len(pred): - pred[:, :4] = scale_boxes( - prep.im.shape[2:], pred[:, :4], prep.im0.shape - ).round() - - klass: int - for klass in pred[:, 5].unique(): - n: torch.Tensor = (pred[:, 5] == klass).sum() - res[str(n.item())] = names[int(klass)] - return res - - -@attr.define -class TensorContainer: - im: torch.Tensor - im0: NDArray[t.Any] - autocast: bool # AMP inference - - def to_numpy(self) -> NDArray[t.Any]: - return self.im.cpu().numpy() - - -# Some of the function below vendored from yolov5.utils.general - - -def exif_transpose(image: PILImage.Image): - """ - Transpose a PIL image accordingly if it has an EXIF Orientation tag. - Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() - - :param image: The image to transpose. - :return: An image. - """ - exif = image.getexif() - orientation = exif.get(0x0112, 1) # default 1 - if orientation > 1: - method = { - 2: PILImage.FLIP_LEFT_RIGHT, - 3: PILImage.ROTATE_180, - 4: PILImage.FLIP_TOP_BOTTOM, - 5: PILImage.TRANSPOSE, - 6: PILImage.ROTATE_270, - 7: PILImage.TRANSVERSE, - 8: PILImage.ROTATE_90, - }.get(orientation) - if method is not None: - image = image.transpose(method) - del exif[0x0112] - image.info["exif"] = exif.tobytes() - return image - - -def letterbox( - im: t.Any, - new_shape: tuple[int, int] | int | list[int] = (640, 640), - color: tuple[int, int, int] = (114, 114, 114), - auto: bool = True, - scaleFill: bool = False, - scaleup: bool = True, - stride: int = 32, -) -> tuple[NDArray[t.Any], tuple[float, float], tuple[float, float]]: - # Resize and pad image while meeting stride-multiple constraints - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better val mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - im = cv2.copyMakeBorder( - im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color - ) # add border - return im, ratio, (dw, dh) - - -def xywh2xyxy(x: torch.Tensor | NDArray[t.Any]): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x - y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y - y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x - y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y - return y - - -def scale_boxes( - img1_shape: tuple[int, ...], - boxes: torch.Tensor, - img0_shape: tuple[int, ...], - ratio_pad: torch.Tensor | None = None, -) -> torch.Tensor: - # Rescale boxes (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min( - img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1] - ) # gain = old / new - pad = ( - (img1_shape[1] - img0_shape[1] * gain) / 2, - (img1_shape[0] - img0_shape[0] * gain) / 2, - ) # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - boxes[..., [0, 2]] -= pad[0] # x padding - boxes[..., [1, 3]] -= pad[1] # y padding - boxes[..., :4] /= gain - clip_boxes(boxes, img0_shape) - return boxes - - -def clip_boxes(boxes: torch.Tensor | NDArray[t.Any], shape: tuple[int, ...]) -> None: - # Clip boxes (xyxy) to image shape (height, width) - if isinstance(boxes, torch.Tensor): # faster individually - boxes[..., 0].clamp_(0, shape[1]) # x1 - boxes[..., 1].clamp_(0, shape[0]) # y1 - boxes[..., 2].clamp_(0, shape[1]) # x2 - boxes[..., 3].clamp_(0, shape[0]) # y2 - else: # np.array (faster grouped) - boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 - - -def non_max_suppression( - prediction: t.Any, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic: bool = False, - multi_label: bool = False, - labels: tuple[torch.Tensor, ...] = (), - max_det: int = 300, - nm: int = 0, # number of masks -): - """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - # Checks - assert ( - 0 <= conf_thres <= 1 - ), f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" - assert ( - 0 <= iou_thres <= 1 - ), f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" - if isinstance( - prediction, (list, tuple) - ): # YOLOv5 model in validation model, output = (inference_out, loss_out) - prediction = prediction[0] # select only inference output - - device = prediction.device - mps = "mps" in device.type # Apple MPS - if mps: # MPS not fully supported yet, convert tensors to CPU before NMS - prediction = prediction.cpu() - bs = prediction.shape[0] # batch size - nc = prediction.shape[2] - nm - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # min_wh = 2 # (pixels) minimum box width and height - max_wh = 7680 # (pixels) maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 0.5 + 0.05 * bs # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - mi = 5 + nc # mask start index - output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - lb = labels[xi] - v = torch.zeros((len(lb), nc + nm + 5), device=x.device) - v[:, :4] = lb[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box/Mask - # center_x, center_y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - mask = x[:, mi:] # zero columns if no masks - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) - else: # best class only - conf, j = x[:, 5:mi].max(1, keepdim=True) - x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - x = x[x[:, 4].argsort(descending=True)[:max_nms]] - # sort by confidence and remove excess boxes - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - i = i[:max_det] # limit detections - if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum( - 1, keepdim=True - ) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if mps: - output[xi] = output[xi].to(device) - if (time.time() - t) > time_limit: - print(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") - break # time limit exceeded - - return output - - -def box_iou(box1: torch.Tensor, box2: torch.Tensor, eps: float = 1e-7) -> torch.Tensor: - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) - inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) - - # IoU = inter / (area1 + area2 - inter) - return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) diff --git a/examples/triton/onnx/locustfile.py b/examples/triton/onnx/locustfile.py deleted file mode 100644 index ddcf9540c11..00000000000 --- a/examples/triton/onnx/locustfile.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -from locust import HttpUser -from locust import constant -from locust import task - - -class TritonOnnxYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def triton_onnx_yolov5_infer(self): - self.client.post( - "/triton_onnx_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/jpeg"))], - ) - - -class BentoOnnxYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def bentoml_onnx_yolov5_infer(self): - self.client.post( - "/bentoml_onnx_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/jpeg"))], - ) diff --git a/examples/triton/onnx/model_repository/onnx_yolov5s/config.pbtxt b/examples/triton/onnx/model_repository/onnx_yolov5s/config.pbtxt deleted file mode 100644 index 487554f9b00..00000000000 --- a/examples/triton/onnx/model_repository/onnx_yolov5s/config.pbtxt +++ /dev/null @@ -1,16 +0,0 @@ -platform: "onnxruntime_onnx" -input { -name: "images" -data_type: TYPE_FP32 -dims: -1 -dims: 3 -dims: 640 -dims: 640 -} -output { -name: "output0" -data_type: TYPE_FP32 -dims: -1 -dims: -1 -dims: -1 -} diff --git a/examples/triton/onnx/requirements/requirements-gpu.txt b/examples/triton/onnx/requirements/requirements-gpu.txt deleted file mode 100644 index 100ca74d61f..00000000000 --- a/examples/triton/onnx/requirements/requirements-gpu.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -nvidia-tensorrt -onnxruntime-gpu diff --git a/examples/triton/onnx/requirements/requirements.txt b/examples/triton/onnx/requirements/requirements.txt deleted file mode 100644 index 930946ae08c..00000000000 --- a/examples/triton/onnx/requirements/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -tritonclient[all]>=2.29.0 -opencv-python -pydantic -locust -torch -torchvision -onnx -onnxruntime -# YOLOv5 dependencies -seaborn -thop -tqdm -pandas -mss -albumentations diff --git a/examples/triton/onnx/serve_bento.py b/examples/triton/onnx/serve_bento.py deleted file mode 100644 index f47c094a5e3..00000000000 --- a/examples/triton/onnx/serve_bento.py +++ /dev/null @@ -1,53 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - import time - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument( - "--grpc", action="store_true", default=False, help="Whether to serve as gRPC." - ) - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-onnx" - if args.tag: - tag = f"triton-integration-onnx:{args.tag}" - - server_type = "grpc" if args.grpc else "http" - - try: - bento = bentoml.get(tag) - except bentoml.exceptions.NotFound: - raise ValueError( - "Bento is not yet built. Make sure to run 'python3 build_bento.py' and try to run this script again." - ) - else: - bento = bentoml.get(tag) - server = bentoml.serve( - bento, - server_type=server_type, - production=True, - ) - try: - while True: - bentoml.client.Client.wait_until_server_ready( - server.host, server.port, 1000 - ) - client = bentoml.client.Client.from_url( - f"http://localhost:{server.port}" - ) - print( - "ONNX config:", - client.model_config( - inp={"model_name": "onnx_yolov5s", "protocol": "grpc"} - ), - ) - time.sleep(1e9) - except KeyboardInterrupt: - server.stop() - raise SystemExit(0) diff --git a/examples/triton/onnx/service.py b/examples/triton/onnx/service.py deleted file mode 100644 index ef04f84bc9f..00000000000 --- a/examples/triton/onnx/service.py +++ /dev/null @@ -1,83 +0,0 @@ -from __future__ import annotations - -import typing as t - -import helpers -import torch - -import bentoml - -if t.TYPE_CHECKING: - from PIL.Image import Image - -# triton runner -triton_runner = bentoml.triton.Runner( - "triton_runner", - "./model_repository", - cli_args=["--model-control-mode=explicit", "--load-model=onnx_yolov5s"], -) - -bentoml_yolov5_onnx = ( - bentoml.onnx.get("onnx-yolov5") - .with_options( - providers=( - ["CUDAExecutionProvider", "CPUExecutionProvider"] - if torch.cuda.is_available() - else ["CPUExecutionProvider"] - ) - ) - .to_runner() -) - -svc = bentoml.Service( - "triton-integration-onnx", runners=[triton_runner, bentoml_yolov5_onnx] -) - - -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), output=bentoml.io.JSON() -) -async def bentoml_onnx_yolov5_infer(im: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - im, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - y = await bentoml_yolov5_onnx.async_run(prep.to_numpy()) - return helpers.postprocess_yolov5_prediction(y, prep) - - -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), output=bentoml.io.JSON() -) -async def triton_onnx_yolov5_infer(im: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - im, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - InferResult = await triton_runner.onnx_yolov5s.async_run(prep.to_numpy()) - return helpers.postprocess_yolov5_prediction(InferResult.as_numpy("output0"), prep) - - -# Triton Model management API -@svc.api( - input=bentoml.io.JSON.from_sample({"model_name": "onnx_yolov5s"}), - output=bentoml.io.JSON(), -) -async def model_config(input_model: dict[t.Literal["model_name"], str]): - return await triton_runner.get_model_config(input_model["model_name"], as_json=True) - - -@svc.api(input=bentoml.io.Text.from_sample("onnx_yolov5s"), output=bentoml.io.JSON()) -async def unload_model(input_model: str): - await triton_runner.unload_model(input_model) - return {"unloaded": input_model} - - -@svc.api(input=bentoml.io.Text.from_sample("onnx_yolov5s"), output=bentoml.io.JSON()) -async def load_model(input_model: str): - await triton_runner.load_model(input_model) - return {"loaded": input_model} - - -@svc.api(input=bentoml.io.Text(), output=bentoml.io.JSON()) -async def list_models(_: str) -> list[str]: - resp = await triton_runner.get_model_repository_index() - return [i.name for i in resp.models] diff --git a/examples/triton/onnx/setup b/examples/triton/onnx/setup deleted file mode 100755 index cd52a5a0945..00000000000 --- a/examples/triton/onnx/setup +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash - -set -ex - -if [ -f /etc/lsb-release ]; then - # In Ubuntu - apt-get update && apt-get install -y pkg-config libhdf5-dev ffmpeg libsm6 libxext6 -fi - -if [ -n "${BENTO_PATH}" ] || [ -f /.dockerenv ]; then - # We need to export LD_PRELOAD inside bento container. - SITE_PACKAGES=$(python3 -c "import site; print(site.getsitepackages()[0])") - - export LD_PRELOAD=$(find "$SITE_PACKAGES/torch/lib/" -name "libgomp*" -printf "%p\n") -fi diff --git a/examples/triton/onnx/train.py b/examples/triton/onnx/train.py deleted file mode 100644 index 1f7130548fc..00000000000 --- a/examples/triton/onnx/train.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -import onnx -import onnx.checker as onnx_checker -from helpers import MODEL_FILE -from helpers import load_traced_script - -import bentoml - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - - parser.add_argument( - "--override", - action="store_true", - default=False, - help="Override existing models", - ) - args = parser.parse_args() - - bento_model_name = "onnx-yolov5" - _, metadata = load_traced_script() - d = {"stride": metadata["stride"], "names": metadata["names"]} - try: - bentoml.models.get(bento_model_name) - if args.override: - raise bentoml.exceptions.NotFound( - "'override=True', overriding previously saved weights/conversions." - ) - print(f"{bento_model_name} already exists. Skipping...") - except bentoml.exceptions.NotFound: - ModelProto = onnx.load(MODEL_FILE.with_suffix(".onnx").__fspath__()) - onnx_checker.check_model(ModelProto) - for k, v in d.items(): - meta = ModelProto.metadata_props.add() - meta.key, meta.value = k, str(v) - print( - "Saved model:", - bentoml.onnx.save_model( - bento_model_name, - ModelProto, - signatures={"run": {"batchable": True, "batch_dim": (0, 0)}}, - metadata={"model_info": d}, - ), - ) - except Exception: - print("Failed to save model:", bento_model_name) - raise diff --git a/examples/triton/pytorch/README.md b/examples/triton/pytorch/README.md deleted file mode 100644 index b366f355ae0..00000000000 --- a/examples/triton/pytorch/README.md +++ /dev/null @@ -1,191 +0,0 @@ -# Triton Inference Server integration - -BentoML now provides support for Triton Inference Server. - -### Quick tour - -Triton Runner can be created via `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "local", model_respository="/path/to/model_repository" -) - -svc = bentoml.Service("my-service", runners=[triton_runner]) -``` - -`model_repository` can also accept S3 path: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", model_repository="s3://path/to/model_repository" -) -``` - -CLI arguments can be passed through the `cli_args` argument of `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", - model_repository="s3://path/to/model_repository", - cli_args=["--load-model=torchscrip_yolov5s", "--model-control-mode=explicit"], -) -``` - -An example of inference API: - -```python -@svc.api( - input=bentoml.io.NumpyNdarray.from_sample( - np.array([[1, 2, 3, 4]]), enforce_dtype=False - ), - output=bentoml.io.NumpyNdarray.from_sample(np.array([1])), -) -def triton_infer(input_data: NDArray[np.float16]) -> NDArray[np.int16]: - iris_res = iris_clf_runner.run(input_data) - res_kwargs = triton_runner.txt2img.run(IRIS_NAME=iris_res) - return iris_res -``` - -Note that each attribute of the `triton_runner` includes the name of all given -models under `model_repository` - -APIs from tritonclient are also provided through the Triton Runner: - -```python -tritonclient.grpc.aio.InferenceServerClient.get_model_metadata -> triton_runner.get_model_metadata | triton_runner.grpc_get_model_metadata -``` - -To get build your Bento with Triton, add the following to your `bentofile.yaml`: - -```yaml -service: "service.py:svc" -include: - - *.py - - /model_repository -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 -``` - -To find out more about BentoML Runner architecture, see -[our latest documentation](https://docs.bentoml.com/en/latest/concepts/runner.html#) - -For more information about Triton Inference Server, see -[here](https://github.com/triton-inference-server/server) - -### Instruction - -The following project includes YOLOv5 `TritonRunner` and `bentoml.Runner`. - -1. Setup Triton model repository and BentoML model: - -```bash -./export-yolov5-weights - -python3 train.py -``` - -2. To build the Bento, use [`build_bento.py`](./build_bento.py): - -```bash -python3 build_bento.py -```` - -> NOTE: To build with custom GPU, pass in `--gpu`. To build with custom tags -> pass in `--tag ` - -3. To containerize use [`containerize_bento.py`](./containerize_bento.py): - -```bash -python3 containerize_bento.py -``` - -4. To run the container with Triton, use `docker run`: - -```bash -docker run --rm -it -p 3000:3000 triton-integration-pytorch serve-http -``` - -#### Develop locally: - -1. To run Triton locally, do the following: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -If you have NVIDIA GPU available, make sure to install -[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on your system. -Afterward, passing in `--gpus all` to `docker`: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --gpus all --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -Inside the container shell, there are two options to install BentoML: - -- Install from editable - -```bash -cd /opt/bentoml && pip install -r requirements/dev-requirements.txt -``` - -Run the [`setup` script](./setup): - -```bash -cd /workspace/ && pip install -r requirements/requirements.txt - -bash ./setup -``` - -2. To serve the Bento, use either `bentoml serve` or - [`serve_bento.py`](./serve_bento.py) (this requires to have `tritonserver` binary available locally on your system. To use the container, go to step 5) - -```bash -python3 serve_bento.py - -# bentoml serve-http | serve-grpc triton-integration-pytorch -``` - -> NOTE: to serve previously custom tag bento, you can also pass in `--tag` to -> `serve_bento.py` - - -> Feel free to build your own tritonserver. See -> [here](https://github.com/triton-inference-server/server/blob/main/docs/customization_guide/build.md) -> for more details on building customisation. - -NOTE: If you running into this issue: - -````prolog -I0217 00:33:40.955605 3626 server.cc:633] -+---------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------+ -| Model | Version | Status | -+---------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------+ -| torchscript_yolov5s | 1 | UNAVAILABLE: Not found: unable to load shared library: /lib/aarch64-linux-gnu/libgomp.so.1: cannot allocate memory in static TLS block | -+---------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------+ - -``` - -Exit out of the process. Then do the following: - -```bash -export LD_PRELOAD=/lib/aarch64-linux-gnu/libgomp.so.1 -``` - -Then run the `bentoml serve` command again. - - - diff --git a/examples/triton/pytorch/bentofile.yaml b/examples/triton/pytorch/bentofile.yaml deleted file mode 100644 index bdeece1e63e..00000000000 --- a/examples/triton/pytorch/bentofile.yaml +++ /dev/null @@ -1,16 +0,0 @@ -service: service:svc -include: - - /model_repository - - /data/*.jpg - - /export-yolov5-weights - - /*.py -exclude: - - /__pycache__ - - /venv - - /yolov5 - - /train.py - - /build_bento.py - - /containerize_bento.py -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 - setup_script: setup diff --git a/examples/triton/pytorch/build_bento.py b/examples/triton/pytorch/build_bento.py deleted file mode 100644 index a114660f292..00000000000 --- a/examples/triton/pytorch/build_bento.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import annotations - -import argparse -import os -import typing as t - -import attr -import torch - -import bentoml -from bentoml import Bento -from bentoml._internal.bento.build_config import BentoBuildConfig -from bentoml._internal.configuration.containers import BentoMLContainer -from bentoml._internal.utils import resolve_user_filepath - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--tag", type=str, default=None) - parser.add_argument("--gpu", action="store_true", default=False) - - args = parser.parse_args() - - tag = "triton-integration-pytorch" - if args.tag: - tag = f"triton-integration-pytorch:{args.tag}" - - try: - bentos = bentoml.get(tag) - print(f"{bentos} already exists. Skipping...") - except bentoml.exceptions.NotFound: - bentofile = resolve_user_filepath("bentofile.yaml", None) - - override_attrs: dict[str, t.Any] = { - "python": { - "requirements_txt": ( - os.path.join("requirements", "requirements-gpu.txt") - if args.gpu and torch.cuda.is_available() - else os.path.join("requirements", "requirements.txt") - ) - } - } - with open(bentofile, "r", encoding="utf-8") as f: - build_config = attr.evolve(BentoBuildConfig.from_yaml(f), **override_attrs) - - print( - "Saved bento:", - Bento.create(build_config, version=args.tag).save( - BentoMLContainer.bento_store.get() - ), - ) diff --git a/examples/triton/pytorch/containerize_bento.py b/examples/triton/pytorch/containerize_bento.py deleted file mode 100644 index 4582c7b504b..00000000000 --- a/examples/triton/pytorch/containerize_bento.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-pytorch" - if args.tag: - tag = f"triton-integration-pytorch:{args.tag}" - - backend = "docker" - try: - builder = bentoml.container.get_backend("buildx") - assert builder.health() - backend = "buildx" - except ValueError: - print("Buildx not found, using default Docker builder.") - try: - builder = bentoml.container.get_backend(backend) - assert builder.health() - except ValueError: - print("Make sure to have Docker running.") - raise - finally: - bentoml.container.build( - tag, backend=backend, features=["all"], platform="linux/amd64" - ) diff --git a/examples/triton/pytorch/data/bus.jpg b/examples/triton/pytorch/data/bus.jpg deleted file mode 100644 index b43e311165c785f000eb7493ff8fb662d06a3f83..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 487438 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-BASEDIR="$(cd "$(dirname "$0")" && pwd)" - -: "${GPU:=false}" - -[ "${UPDATE_YOLOV5}" = "true" ] && [ -d "$BASEDIR/yolov5" ] && \rm -rf yolov5 - -main() { - prev_dir="$(pwd)" - cd "$BASEDIR" - - ! [ -d yolov5 ] && - mkdir -p yolov5 && cd yolov5 && - ! [ -d yolov5.git ] && git clone --bare https://github.com/ultralytics/yolov5.git && - git --git-dir=./yolov5.git --work-tree=. checkout HEAD -- data models utils LICENSE detect.py export.py val.py requirements.txt && \rm -rf yolov5.git && cd "${BASEDIR}" - - exists=0 - - ! [ -d "${BASEDIR}/model_repository/torchscript_${MODEL_TYPE}/1" ] && mkdir -p "${BASEDIR}/model_repository/torchscript_${MODEL_TYPE}/1" - [ -f "${BASEDIR}/${MODEL_TYPE}.torchscript" ] && exists=1 - if [ "${exists}" -eq 0 ] || [ "$OVERRIDE" = "true" ]; then - if [ "$GPU" = "true" ]; then - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include torchscript --batch-size "$1" --dynamic --device 0 - else - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include torchscript --batch-size "$1" --dynamic - fi - fi - cp "${BASEDIR}/${MODEL_TYPE}.torchscript" "${BASEDIR}/model_repository/torchscript_${MODEL_TYPE}/1/model.pt" - - echo "Successfully export YOLOv5 weights for TorchScript." - cd "${prev_dir}" -} - -if ! [ "$#" -eq 1 ]; then - echo "Usage: $0 . Set to 1 by default if not passed." - main 1 -else - main "$@" -fi diff --git a/examples/triton/pytorch/helpers.py b/examples/triton/pytorch/helpers.py deleted file mode 100644 index 1e27b930e39..00000000000 --- a/examples/triton/pytorch/helpers.py +++ /dev/null @@ -1,397 +0,0 @@ -from __future__ import annotations - -import json -import os -import time -import typing as t -from pathlib import Path - -import attr -import cv2 -import numpy as np -import torch -import torchvision -from PIL import Image as PILImage - -if t.TYPE_CHECKING: - from numpy.typing import NDArray - from torch.jit._script import ScriptModule - - P = t.ParamSpec("P") - -WORKING_DIR = Path(__file__).parent -MODEL_TYPE = os.environ.get("MODEL_TYPE", "yolov5s") -MODEL_FILE = WORKING_DIR / f"{MODEL_TYPE}.pt" -TORCH_DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - -def load_traced_script() -> tuple[ScriptModule, dict[str, t.Any]]: - extra_files = {"config.txt": ""} - model = torch.jit.load( - MODEL_FILE.with_suffix(".torchscript").__fspath__(), - map_location=TORCH_DEVICE, - _extra_files=extra_files, - ) - d = {"shape": [], "stride": 32, "names": {}} - if extra_files["config.txt"]: - d = json.loads( - extra_files["config.txt"], - object_hook=lambda d: { - int(k) if k.isdigit() else k: v for k, v in d.items() - }, - ) - return model, d - - -def torch_tensor_from_numpy( - x: torch.Tensor | NDArray[t.Any], device: str = "cpu" -) -> torch.Tensor: - if isinstance(x, np.ndarray): - # Needs to create a copy if the array is not writeable - x = np.copy(x) if not x.flags["WRITEABLE"] else x - return torch.from_numpy(x).to(device) - elif isinstance(x, torch.Tensor): - return x - else: - raise TypeError( - f"Expected numpy.ndarray or torch.Tensor, got {type(x).__name__}" - ) - - -def prepare_yolov5_input( - im_: PILImage.Image, - size: int | tuple[int, int] = (640, 640), - auto: bool = False, - device: str = "cpu", - fp16: bool = False, - stride: int = 32, -) -> TensorContainer: - p = torch.empty(1, device=torch.device(device)) - # Automatic Mixed Precision (AMP) inference - autocast = fp16 and p.device.type != "cpu" - - im0 = np.asarray(exif_transpose(im_)) - # padded resize - im, _, _ = letterbox(im0, size, stride=stride, auto=auto) - im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - im = np.ascontiguousarray(im) # contiguous - - im = torch.from_numpy(im).to(p.device) - im = im.half() if fp16 else im.float() # unit8 to fp16/fp32 - im /= 255 # 0-255 to 0.0-1.0 - if len(im.shape) == 3: - im = im[None] # extends to batch_dim - - return TensorContainer(im, im0, autocast) - - -def postprocess_yolov5_prediction( - y: list[t.Any] | tuple[t.Any, ...] | torch.Tensor | NDArray[t.Any], - prep: TensorContainer, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic_nms: bool = False, - max_det: int = 1000, -): - res: dict[str, str] = {} - names = load_traced_script()[1]["names"] - if isinstance(y, (list, tuple)): - y = ( - torch_tensor_from_numpy(y[0]) - if len(y) == 1 - else [torch_tensor_from_numpy(_) for _ in y] - ) - else: - y = torch_tensor_from_numpy(y) - - y = non_max_suppression( - y, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det - ) - # post - for pred in y: - if len(pred): - pred[:, :4] = scale_boxes( - prep.im.shape[2:], pred[:, :4], prep.im0.shape - ).round() - - klass: int - for klass in pred[:, 5].unique(): - n: torch.Tensor = (pred[:, 5] == klass).sum() - res[str(n.item())] = names[int(klass)] - return res - - -@attr.define -class TensorContainer: - im: torch.Tensor - im0: NDArray[t.Any] - autocast: bool # AMP inference - - def to_numpy(self) -> NDArray[t.Any]: - return self.im.cpu().numpy() - - -# Some of the function below vendored from yolov5.utils.general - - -def exif_transpose(image: PILImage.Image): - """ - Transpose a PIL image accordingly if it has an EXIF Orientation tag. - Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() - - :param image: The image to transpose. - :return: An image. - """ - exif = image.getexif() - orientation = exif.get(0x0112, 1) # default 1 - if orientation > 1: - method = { - 2: PILImage.FLIP_LEFT_RIGHT, - 3: PILImage.ROTATE_180, - 4: PILImage.FLIP_TOP_BOTTOM, - 5: PILImage.TRANSPOSE, - 6: PILImage.ROTATE_270, - 7: PILImage.TRANSVERSE, - 8: PILImage.ROTATE_90, - }.get(orientation) - if method is not None: - image = image.transpose(method) - del exif[0x0112] - image.info["exif"] = exif.tobytes() - return image - - -def letterbox( - im: t.Any, - new_shape: tuple[int, int] | int | list[int] = (640, 640), - color: tuple[int, int, int] = (114, 114, 114), - auto: bool = True, - scaleFill: bool = False, - scaleup: bool = True, - stride: int = 32, -) -> tuple[NDArray[t.Any], tuple[float, float], tuple[float, float]]: - # Resize and pad image while meeting stride-multiple constraints - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better val mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - im = cv2.copyMakeBorder( - im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color - ) # add border - return im, ratio, (dw, dh) - - -def xywh2xyxy(x: torch.Tensor | NDArray[t.Any]): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x - y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y - y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x - y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y - return y - - -def scale_boxes( - img1_shape: tuple[int, ...], - boxes: torch.Tensor, - img0_shape: tuple[int, ...], - ratio_pad: torch.Tensor | None = None, -) -> torch.Tensor: - # Rescale boxes (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min( - img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1] - ) # gain = old / new - pad = ( - (img1_shape[1] - img0_shape[1] * gain) / 2, - (img1_shape[0] - img0_shape[0] * gain) / 2, - ) # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - boxes[..., [0, 2]] -= pad[0] # x padding - boxes[..., [1, 3]] -= pad[1] # y padding - boxes[..., :4] /= gain - clip_boxes(boxes, img0_shape) - return boxes - - -def clip_boxes(boxes: torch.Tensor | NDArray[t.Any], shape: tuple[int, ...]) -> None: - # Clip boxes (xyxy) to image shape (height, width) - if isinstance(boxes, torch.Tensor): # faster individually - boxes[..., 0].clamp_(0, shape[1]) # x1 - boxes[..., 1].clamp_(0, shape[0]) # y1 - boxes[..., 2].clamp_(0, shape[1]) # x2 - boxes[..., 3].clamp_(0, shape[0]) # y2 - else: # np.array (faster grouped) - boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 - - -def non_max_suppression( - prediction: t.Any, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic: bool = False, - multi_label: bool = False, - labels: tuple[torch.Tensor, ...] = (), - max_det: int = 300, - nm: int = 0, # number of masks -): - """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - # Checks - assert ( - 0 <= conf_thres <= 1 - ), f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" - assert ( - 0 <= iou_thres <= 1 - ), f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" - if isinstance( - prediction, (list, tuple) - ): # YOLOv5 model in validation model, output = (inference_out, loss_out) - prediction = prediction[0] # select only inference output - - device = prediction.device - mps = "mps" in device.type # Apple MPS - if mps: # MPS not fully supported yet, convert tensors to CPU before NMS - prediction = prediction.cpu() - bs = prediction.shape[0] # batch size - nc = prediction.shape[2] - nm - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # min_wh = 2 # (pixels) minimum box width and height - max_wh = 7680 # (pixels) maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 0.5 + 0.05 * bs # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - mi = 5 + nc # mask start index - output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - lb = labels[xi] - v = torch.zeros((len(lb), nc + nm + 5), device=x.device) - v[:, :4] = lb[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box/Mask - # center_x, center_y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - mask = x[:, mi:] # zero columns if no masks - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) - else: # best class only - conf, j = x[:, 5:mi].max(1, keepdim=True) - x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - x = x[x[:, 4].argsort(descending=True)[:max_nms]] - # sort by confidence and remove excess boxes - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - i = i[:max_det] # limit detections - if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum( - 1, keepdim=True - ) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if mps: - output[xi] = output[xi].to(device) - if (time.time() - t) > time_limit: - print(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") - break # time limit exceeded - - return output - - -def box_iou(box1: torch.Tensor, box2: torch.Tensor, eps: float = 1e-7) -> torch.Tensor: - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) - inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) - - # IoU = inter / (area1 + area2 - inter) - return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) diff --git a/examples/triton/pytorch/locustfile.py b/examples/triton/pytorch/locustfile.py deleted file mode 100644 index fb368db5b19..00000000000 --- a/examples/triton/pytorch/locustfile.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -from locust import HttpUser -from locust import constant -from locust import task - - -class TritonTorchscriptYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def triton_torchscript_yolov5_infer(self): - self.client.post( - "/triton_torchscript_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/png"))], - ) - - -class BentoTorchscriptYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def bentoml_torchscript_yolov5_infer(self): - self.client.post( - "/bentoml_torchscript_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/jpeg"))], - ) diff --git a/examples/triton/pytorch/model_repository/torchscript_yolov5s/config.pbtxt b/examples/triton/pytorch/model_repository/torchscript_yolov5s/config.pbtxt deleted file mode 100644 index 95e5f51cb4f..00000000000 --- a/examples/triton/pytorch/model_repository/torchscript_yolov5s/config.pbtxt +++ /dev/null @@ -1,16 +0,0 @@ -platform: "pytorch_libtorch" -input { -name: "INPUT__0" -data_type: TYPE_FP32 -dims: -1 -dims: 3 -dims: 640 -dims: 640 -} -output { -name: "OUTPUT__0" -data_type: TYPE_FP32 -dims: -1 -dims: 25200 -dims: 85 -} diff --git a/examples/triton/pytorch/requirements/requirements-gpu.txt b/examples/triton/pytorch/requirements/requirements-gpu.txt deleted file mode 100644 index 100ca74d61f..00000000000 --- a/examples/triton/pytorch/requirements/requirements-gpu.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -nvidia-tensorrt -onnxruntime-gpu diff --git a/examples/triton/pytorch/requirements/requirements.txt b/examples/triton/pytorch/requirements/requirements.txt deleted file mode 100644 index 3991bd20e72..00000000000 --- a/examples/triton/pytorch/requirements/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -tritonclient[all]>=2.29.0 -opencv-python -torchvision -pydantic -locust -tensorflow;platform_system!='Darwin' -tensorflow-io;platform_system!='Darwin' -tensorflow-macos;platform_system=='Darwin' -onnx -onnxruntime -torch -# YOLOv5 dependencies -seaborn -thop -tqdm -pandas -mss -albumentations diff --git a/examples/triton/pytorch/serve_bento.py b/examples/triton/pytorch/serve_bento.py deleted file mode 100644 index cc1026dca41..00000000000 --- a/examples/triton/pytorch/serve_bento.py +++ /dev/null @@ -1,53 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - import time - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument( - "--grpc", action="store_true", default=False, help="Whether to serve as gRPC." - ) - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-pytorch" - if args.tag: - tag = f"triton-integration-pytorch:{args.tag}" - - server_type = "grpc" if args.grpc else "http" - - try: - bento = bentoml.get(tag) - except bentoml.exceptions.NotFound: - raise ValueError( - "Bento is not yet built. Make sure to run 'python3 build_bento.py' and try to run this script again." - ) - else: - bento = bentoml.get(tag) - server = bentoml.serve( - bento, - server_type=server_type, - production=True, - ) - try: - while True: - bentoml.client.Client.wait_until_server_ready( - server.host, server.port, 1000 - ) - client = bentoml.client.Client.from_url( - f"http://localhost:{server.port}" - ) - print( - "TorchScript config:", - client.model_config( - inp={"model_name": "torchscript_yolov5s", "protocol": "grpc"} - ), - ) - time.sleep(1e9) - except KeyboardInterrupt: - server.stop() - raise SystemExit(0) diff --git a/examples/triton/pytorch/service.py b/examples/triton/pytorch/service.py deleted file mode 100644 index b28d57370b8..00000000000 --- a/examples/triton/pytorch/service.py +++ /dev/null @@ -1,86 +0,0 @@ -from __future__ import annotations - -import typing as t - -import helpers -import torch - -import bentoml - -if t.TYPE_CHECKING: - from PIL.Image import Image - -# triton runner - -triton_runner = bentoml.triton.Runner( - "triton_runner", - "./model_repository", - cli_args=[ - "--model-control-mode=explicit", - "--load-model=torchscript_yolov5s", - ], -) -bentoml_yolov5_torchscript = bentoml.torchscript.get("torchscript-yolov5").to_runner() - -svc = bentoml.Service( - "triton-integration-pytorch", - runners=[triton_runner, bentoml_yolov5_torchscript], -) - - -#### BentoML YOLOv5 -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), output=bentoml.io.JSON() -) -async def bentoml_torchscript_yolov5_infer(fp: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - fp, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - y = await bentoml_yolov5_torchscript.async_run(prep.to_numpy()) - return helpers.postprocess_yolov5_prediction(y, prep) - - -### Triton YOLOv5 -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), - output=bentoml.io.JSON(), -) -async def triton_torchscript_yolov5_infer(im: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - im, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - InferResult = await triton_runner.torchscript_yolov5s.async_run(prep.to_numpy()) - return helpers.postprocess_yolov5_prediction( - InferResult.as_numpy("OUTPUT__0"), prep - ) - - -# Triton Model management API -@svc.api( - input=bentoml.io.JSON.from_sample({"model_name": "torchscript_yolov5s"}), - output=bentoml.io.JSON(), -) -async def model_config(input_model: dict[t.Literal["model_name"], str]): - return await triton_runner.get_model_config(input_model["model_name"], as_json=True) - - -@svc.api( - input=bentoml.io.Text.from_sample("torchscript_yolov5s"), output=bentoml.io.JSON() -) -async def unload_model(input_model: str): - await triton_runner.unload_model(input_model) - return {"unloaded": input_model} - - -@svc.api( - input=bentoml.io.Text.from_sample("torchscript_yolov5s"), output=bentoml.io.JSON() -) -async def load_model(input_model: str): - await triton_runner.load_model(input_model) - return {"loaded": input_model} - - -@svc.api(input=bentoml.io.Text(), output=bentoml.io.JSON()) -async def list_models(_: str) -> list[str]: - resp = await triton_runner.get_model_repository_index() - return [i.name for i in resp.models] diff --git a/examples/triton/pytorch/setup b/examples/triton/pytorch/setup deleted file mode 100755 index cd52a5a0945..00000000000 --- a/examples/triton/pytorch/setup +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash - -set -ex - -if [ -f /etc/lsb-release ]; then - # In Ubuntu - apt-get update && apt-get install -y pkg-config libhdf5-dev ffmpeg libsm6 libxext6 -fi - -if [ -n "${BENTO_PATH}" ] || [ -f /.dockerenv ]; then - # We need to export LD_PRELOAD inside bento container. - SITE_PACKAGES=$(python3 -c "import site; print(site.getsitepackages()[0])") - - export LD_PRELOAD=$(find "$SITE_PACKAGES/torch/lib/" -name "libgomp*" -printf "%p\n") -fi diff --git a/examples/triton/pytorch/train.py b/examples/triton/pytorch/train.py deleted file mode 100644 index fda82f304f7..00000000000 --- a/examples/triton/pytorch/train.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -import json -import typing as t - -from helpers import load_traced_script - -import bentoml - -if t.TYPE_CHECKING: - pass - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - - parser.add_argument( - "--override", - action="store_true", - default=False, - help="Override existing models", - ) - args = parser.parse_args() - - bento_model_name = "torchscript-yolov5" - model, metadata = load_traced_script() - - try: - bentoml.models.get(bento_model_name) - if args.override: - raise bentoml.exceptions.NotFound( - "'override=True', overriding previously saved weights/conversions." - ) - print(f"{bento_model_name} already exists. Skipping...") - except bentoml.exceptions.NotFound: - print( - "Saved model:", - bentoml.torchscript.save_model( - bento_model_name, - model, - signatures={"__call__": {"batchable": True, "batch_dim": (0, 0)}}, - metadata={"model_info": metadata}, - _extra_files={"config.txt": json.dumps(metadata)}, - ), - ) - except Exception: - print("Failed to save model:", bento_model_name) - raise diff --git a/examples/triton/tensorflow/README.md b/examples/triton/tensorflow/README.md deleted file mode 100644 index ddb75bdf552..00000000000 --- a/examples/triton/tensorflow/README.md +++ /dev/null @@ -1,171 +0,0 @@ -# Triton Inference Server integration - -BentoML now provides support for Triton Inference Server. - -### Quick tour - -Triton Runner can be created via `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "local", model_respository="/path/to/model_repository" -) - -svc = bentoml.Service("my-service", runners=[triton_runner]) -``` - -`model_repository` can also accept S3 path: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", model_repository="s3://path/to/model_repository" -) -``` - -CLI arguments can be passed through the `cli_args` argument of `bentoml.triton.Runner`: - -```python -triton_runner = bentoml.triton.Runner( - "triton-runners", - model_repository="s3://path/to/model_repository", - cli_args=["--load-model=torchscrip_yolov5s", "--model-control-mode=explicit"], -) -``` - -An example of inference API: - -```python -@svc.api( - input=bentoml.io.NumpyNdarray.from_sample( - np.array([[1, 2, 3, 4]]), enforce_dtype=False - ), - output=bentoml.io.NumpyNdarray.from_sample(np.array([1])), -) -def triton_infer(input_data: NDArray[np.float16]) -> NDArray[np.int16]: - iris_res = iris_clf_runner.run(input_data) - res_kwargs = triton_runner.txt2img.run(IRIS_NAME=iris_res) - return iris_res -``` - -Note that each attribute of the `triton_runner` includes the name of all given -models under `model_repository` - -APIs from tritonclient are also provided through the Triton Runner: - -```python -tritonclient.grpc.aio.InferenceServerClient.get_model_metadata -> triton_runner.get_model_metadata | triton_runner.grpc_get_model_metadata -``` - -To get build your Bento with Triton, add the following to your `bentofile.yaml`: - -```yaml -service: "service.py:svc" -include: - - *.py - - /model_repository -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 -``` - -To find out more about BentoML Runner architecture, see -[our latest documentation](https://docs.bentoml.com/en/latest/concepts/runner.html#) - -For more information about Triton Inference Server, see -[here](https://github.com/triton-inference-server/server) - - -### Instruction - -The following project includes YOLOv5 `TritonRunner` and `bentoml.Runner`. - -1. Setup Triton model repository and BentoML model: - -```bash -./export-yolov5-weights - -python3 train.py -``` - -2. To build the Bento, use [`build_bento.py`](./build_bento.py): - -```bash -python3 build_bento.py -```` - -> NOTE: To build with custom GPU, pass in `--gpu`. To build with custom tags -> pass in `--tag ` - -3. To containerize use [`containerize_bento.py`](./containerize_bento.py): - -```bash -python3 containerize_bento.py -``` - -4. To run the container with Triton, use `docker run`: - -```bash -docker run --rm -it -p 3000:3000 triton-integration-tensorflow serve-http -``` - -#### Develop locally: - -1. To run Triton locally, do the following: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -If you have NVIDIA GPU available, make sure to install -[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on your system. -Afterward, passing in `--gpus all` to `docker`: - -```bash -BENTOML_GIT_ROOT=$(git rev-parse --show-toplevel) -docker run --gpus all --rm -it -p 3000-4000:3000-4000 \ - -v $PWD:/workspace -v $BENTOML_GIT_ROOT:/opt/bentoml \ - -v $BENTOML_HOME:/home/bentoml --env BENTOML_HOME=/home/bentoml \ - nvcr.io/nvidia/tritonserver:22.12-py3 bash -``` - -Inside the container shell, there are two options to install BentoML: - -- Install from editable - -```bash -cd /opt/bentoml && pip install -r requirements/dev-requirements.txt -``` - -Run the [`setup` script](./setup): - -```bash -cd /workspace/ && pip install -r requirements/requirements.txt - -bash ./setup -``` - -2. To serve the Bento, use either `bentoml serve` or - [`serve_bento.py`](./serve_bento.py) (this requires to have `tritonserver` binary available locally on your system. To use the container, go to step 5) - -```bash -python3 serve_bento.py - -# bentoml serve-http | serve-grpc triton-integration-tensorflow -``` - -> NOTE: to serve previously custom tag bento, you can also pass in `--tag` to -> `serve_bento.py` - - -> Feel free to build your own tritonserver. See -> [here](https://github.com/triton-inference-server/server/blob/main/docs/customization_guide/build.md) -> for more details on building customisation. - - diff --git a/examples/triton/tensorflow/bentofile.yaml b/examples/triton/tensorflow/bentofile.yaml deleted file mode 100644 index bdeece1e63e..00000000000 --- a/examples/triton/tensorflow/bentofile.yaml +++ /dev/null @@ -1,16 +0,0 @@ -service: service:svc -include: - - /model_repository - - /data/*.jpg - - /export-yolov5-weights - - /*.py -exclude: - - /__pycache__ - - /venv - - /yolov5 - - /train.py - - /build_bento.py - - /containerize_bento.py -docker: - base_image: nvcr.io/nvidia/tritonserver:22.12-py3 - setup_script: setup diff --git a/examples/triton/tensorflow/build_bento.py b/examples/triton/tensorflow/build_bento.py deleted file mode 100644 index cbe5f799665..00000000000 --- a/examples/triton/tensorflow/build_bento.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import annotations - -import argparse -import os -import typing as t - -import attr -import torch - -import bentoml -from bentoml import Bento -from bentoml._internal.bento.build_config import BentoBuildConfig -from bentoml._internal.configuration.containers import BentoMLContainer -from bentoml._internal.utils import resolve_user_filepath - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--tag", type=str, default=None) - parser.add_argument("--gpu", action="store_true", default=False) - - args = parser.parse_args() - - tag = "triton-integration-tensorflow" - if args.tag: - tag = f"triton-integration-tensorflow:{args.tag}" - - try: - bentos = bentoml.get(tag) - print(f"{bentos} already exists. Skipping...") - except bentoml.exceptions.NotFound: - bentofile = resolve_user_filepath("bentofile.yaml", None) - - override_attrs: dict[str, t.Any] = { - "python": { - "requirements_txt": ( - os.path.join("requirements", "requirements-gpu.txt") - if args.gpu and torch.cuda.is_available() - else os.path.join("requirements", "requirements.txt") - ) - } - } - with open(bentofile, "r", encoding="utf-8") as f: - build_config = attr.evolve(BentoBuildConfig.from_yaml(f), **override_attrs) - - print( - "Saved bento:", - Bento.create(build_config, version=args.tag).save( - BentoMLContainer.bento_store.get() - ), - ) diff --git a/examples/triton/tensorflow/containerize_bento.py b/examples/triton/tensorflow/containerize_bento.py deleted file mode 100644 index 536eab0825a..00000000000 --- a/examples/triton/tensorflow/containerize_bento.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-tensorflow" - if args.tag: - tag = f"triton-integration-tensorflow:{args.tag}" - - backend = "docker" - try: - builder = bentoml.container.get_backend("buildx") - assert builder.health() - backend = "buildx" - except ValueError: - print("Buildx not found, using default Docker builder.") - try: - builder = bentoml.container.get_backend(backend) - assert builder.health() - except ValueError: - print("Make sure to have Docker running.") - raise - finally: - bentoml.container.build( - tag, backend=backend, features=["all"], platform="linux/amd64" - ) diff --git a/examples/triton/tensorflow/data/bus.jpg b/examples/triton/tensorflow/data/bus.jpg deleted file mode 100644 index b43e311165c785f000eb7493ff8fb662d06a3f83..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 487438 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-: "${GPU:=false}" - -BASEDIR="$(cd "$(dirname "$0")" && pwd)" - -[ "${UPDATE_YOLOV5}" = "true" ] && [ -d "$BASEDIR/yolov5" ] && \rm -rf yolov5 - -main() { - prev_dir="$(pwd)" - cd "$BASEDIR" - - ! [ -d yolov5 ] && - mkdir -p yolov5 && cd yolov5 && - ! [ -d yolov5.git ] && git clone --bare https://github.com/ultralytics/yolov5.git && - git --git-dir=./yolov5.git --work-tree=. checkout HEAD -- data models utils LICENSE detect.py export.py val.py requirements.txt && \rm -rf yolov5.git && cd "${BASEDIR}" - - exists=0 - - [ -d "${BASEDIR}/${MODEL_TYPE}_saved_model" ] && exists=1 - [ "${OVERRIDE}" = "true" ] && \rm -rf "${BASEDIR}/${MODEL_TYPE}_saved_model" - ! [ -d "${BASEDIR}/model_repository/tensorflow_${MODEL_TYPE}/1" ] && mkdir -p "${BASEDIR}/model_repository/tensorflow_${MODEL_TYPE}/1" - [ -d "${BASEDIR}/model_repository/tensorflow_${MODEL_TYPE}/1/model.savedmodel" ] && \rm -rf "$BASEDIR/model_repository/tensorflow_${MODEL_TYPE}/1/model.savedmodel" - - if [ "${exists}" -eq 0 ] || [ "$OVERRIDE" = "true" ]; then - if [ "$GPU" = "true" ]; then - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include saved_model torchscript --batch-size "$1" --dynamic --device 0 - else - python3 yolov5/export.py --weights "$MODEL_TYPE.pt" --include saved_model torchscript --batch-size "$1" --dynamic - fi - fi - cp -r "${BASEDIR}/${MODEL_TYPE}_saved_model" "${BASEDIR}/model_repository/tensorflow_${MODEL_TYPE}/1/model.savedmodel" - - echo "Successfully export YOLOv5 weights for SavedModel." - cd "${prev_dir}" -} - -if ! [ "$#" -eq 1 ]; then - echo "Usage: $0 . Set to 1 by default if not passed." - main 1 -else - main "$@" -fi diff --git a/examples/triton/tensorflow/helpers.py b/examples/triton/tensorflow/helpers.py deleted file mode 100644 index 1e27b930e39..00000000000 --- a/examples/triton/tensorflow/helpers.py +++ /dev/null @@ -1,397 +0,0 @@ -from __future__ import annotations - -import json -import os -import time -import typing as t -from pathlib import Path - -import attr -import cv2 -import numpy as np -import torch -import torchvision -from PIL import Image as PILImage - -if t.TYPE_CHECKING: - from numpy.typing import NDArray - from torch.jit._script import ScriptModule - - P = t.ParamSpec("P") - -WORKING_DIR = Path(__file__).parent -MODEL_TYPE = os.environ.get("MODEL_TYPE", "yolov5s") -MODEL_FILE = WORKING_DIR / f"{MODEL_TYPE}.pt" -TORCH_DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - -def load_traced_script() -> tuple[ScriptModule, dict[str, t.Any]]: - extra_files = {"config.txt": ""} - model = torch.jit.load( - MODEL_FILE.with_suffix(".torchscript").__fspath__(), - map_location=TORCH_DEVICE, - _extra_files=extra_files, - ) - d = {"shape": [], "stride": 32, "names": {}} - if extra_files["config.txt"]: - d = json.loads( - extra_files["config.txt"], - object_hook=lambda d: { - int(k) if k.isdigit() else k: v for k, v in d.items() - }, - ) - return model, d - - -def torch_tensor_from_numpy( - x: torch.Tensor | NDArray[t.Any], device: str = "cpu" -) -> torch.Tensor: - if isinstance(x, np.ndarray): - # Needs to create a copy if the array is not writeable - x = np.copy(x) if not x.flags["WRITEABLE"] else x - return torch.from_numpy(x).to(device) - elif isinstance(x, torch.Tensor): - return x - else: - raise TypeError( - f"Expected numpy.ndarray or torch.Tensor, got {type(x).__name__}" - ) - - -def prepare_yolov5_input( - im_: PILImage.Image, - size: int | tuple[int, int] = (640, 640), - auto: bool = False, - device: str = "cpu", - fp16: bool = False, - stride: int = 32, -) -> TensorContainer: - p = torch.empty(1, device=torch.device(device)) - # Automatic Mixed Precision (AMP) inference - autocast = fp16 and p.device.type != "cpu" - - im0 = np.asarray(exif_transpose(im_)) - # padded resize - im, _, _ = letterbox(im0, size, stride=stride, auto=auto) - im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - im = np.ascontiguousarray(im) # contiguous - - im = torch.from_numpy(im).to(p.device) - im = im.half() if fp16 else im.float() # unit8 to fp16/fp32 - im /= 255 # 0-255 to 0.0-1.0 - if len(im.shape) == 3: - im = im[None] # extends to batch_dim - - return TensorContainer(im, im0, autocast) - - -def postprocess_yolov5_prediction( - y: list[t.Any] | tuple[t.Any, ...] | torch.Tensor | NDArray[t.Any], - prep: TensorContainer, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic_nms: bool = False, - max_det: int = 1000, -): - res: dict[str, str] = {} - names = load_traced_script()[1]["names"] - if isinstance(y, (list, tuple)): - y = ( - torch_tensor_from_numpy(y[0]) - if len(y) == 1 - else [torch_tensor_from_numpy(_) for _ in y] - ) - else: - y = torch_tensor_from_numpy(y) - - y = non_max_suppression( - y, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det - ) - # post - for pred in y: - if len(pred): - pred[:, :4] = scale_boxes( - prep.im.shape[2:], pred[:, :4], prep.im0.shape - ).round() - - klass: int - for klass in pred[:, 5].unique(): - n: torch.Tensor = (pred[:, 5] == klass).sum() - res[str(n.item())] = names[int(klass)] - return res - - -@attr.define -class TensorContainer: - im: torch.Tensor - im0: NDArray[t.Any] - autocast: bool # AMP inference - - def to_numpy(self) -> NDArray[t.Any]: - return self.im.cpu().numpy() - - -# Some of the function below vendored from yolov5.utils.general - - -def exif_transpose(image: PILImage.Image): - """ - Transpose a PIL image accordingly if it has an EXIF Orientation tag. - Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() - - :param image: The image to transpose. - :return: An image. - """ - exif = image.getexif() - orientation = exif.get(0x0112, 1) # default 1 - if orientation > 1: - method = { - 2: PILImage.FLIP_LEFT_RIGHT, - 3: PILImage.ROTATE_180, - 4: PILImage.FLIP_TOP_BOTTOM, - 5: PILImage.TRANSPOSE, - 6: PILImage.ROTATE_270, - 7: PILImage.TRANSVERSE, - 8: PILImage.ROTATE_90, - }.get(orientation) - if method is not None: - image = image.transpose(method) - del exif[0x0112] - image.info["exif"] = exif.tobytes() - return image - - -def letterbox( - im: t.Any, - new_shape: tuple[int, int] | int | list[int] = (640, 640), - color: tuple[int, int, int] = (114, 114, 114), - auto: bool = True, - scaleFill: bool = False, - scaleup: bool = True, - stride: int = 32, -) -> tuple[NDArray[t.Any], tuple[float, float], tuple[float, float]]: - # Resize and pad image while meeting stride-multiple constraints - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better val mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - im = cv2.copyMakeBorder( - im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color - ) # add border - return im, ratio, (dw, dh) - - -def xywh2xyxy(x: torch.Tensor | NDArray[t.Any]): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x - y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y - y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x - y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y - return y - - -def scale_boxes( - img1_shape: tuple[int, ...], - boxes: torch.Tensor, - img0_shape: tuple[int, ...], - ratio_pad: torch.Tensor | None = None, -) -> torch.Tensor: - # Rescale boxes (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min( - img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1] - ) # gain = old / new - pad = ( - (img1_shape[1] - img0_shape[1] * gain) / 2, - (img1_shape[0] - img0_shape[0] * gain) / 2, - ) # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - boxes[..., [0, 2]] -= pad[0] # x padding - boxes[..., [1, 3]] -= pad[1] # y padding - boxes[..., :4] /= gain - clip_boxes(boxes, img0_shape) - return boxes - - -def clip_boxes(boxes: torch.Tensor | NDArray[t.Any], shape: tuple[int, ...]) -> None: - # Clip boxes (xyxy) to image shape (height, width) - if isinstance(boxes, torch.Tensor): # faster individually - boxes[..., 0].clamp_(0, shape[1]) # x1 - boxes[..., 1].clamp_(0, shape[0]) # y1 - boxes[..., 2].clamp_(0, shape[1]) # x2 - boxes[..., 3].clamp_(0, shape[0]) # y2 - else: # np.array (faster grouped) - boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 - - -def non_max_suppression( - prediction: t.Any, - conf_thres: float = 0.25, - iou_thres: float = 0.45, - classes: list[int] | None = None, - agnostic: bool = False, - multi_label: bool = False, - labels: tuple[torch.Tensor, ...] = (), - max_det: int = 300, - nm: int = 0, # number of masks -): - """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - # Checks - assert ( - 0 <= conf_thres <= 1 - ), f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" - assert ( - 0 <= iou_thres <= 1 - ), f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" - if isinstance( - prediction, (list, tuple) - ): # YOLOv5 model in validation model, output = (inference_out, loss_out) - prediction = prediction[0] # select only inference output - - device = prediction.device - mps = "mps" in device.type # Apple MPS - if mps: # MPS not fully supported yet, convert tensors to CPU before NMS - prediction = prediction.cpu() - bs = prediction.shape[0] # batch size - nc = prediction.shape[2] - nm - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # min_wh = 2 # (pixels) minimum box width and height - max_wh = 7680 # (pixels) maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 0.5 + 0.05 * bs # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - mi = 5 + nc # mask start index - output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - lb = labels[xi] - v = torch.zeros((len(lb), nc + nm + 5), device=x.device) - v[:, :4] = lb[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box/Mask - # center_x, center_y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - mask = x[:, mi:] # zero columns if no masks - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) - else: # best class only - conf, j = x[:, 5:mi].max(1, keepdim=True) - x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - x = x[x[:, 4].argsort(descending=True)[:max_nms]] - # sort by confidence and remove excess boxes - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - i = i[:max_det] # limit detections - if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum( - 1, keepdim=True - ) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if mps: - output[xi] = output[xi].to(device) - if (time.time() - t) > time_limit: - print(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") - break # time limit exceeded - - return output - - -def box_iou(box1: torch.Tensor, box2: torch.Tensor, eps: float = 1e-7) -> torch.Tensor: - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) - inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) - - # IoU = inter / (area1 + area2 - inter) - return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) diff --git a/examples/triton/tensorflow/locustfile.py b/examples/triton/tensorflow/locustfile.py deleted file mode 100644 index 67e21c219ad..00000000000 --- a/examples/triton/tensorflow/locustfile.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -from locust import HttpUser -from locust import constant -from locust import task - - -class TritonTensorflowYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def triton_tensorflow_yolov5_infer(self): - self.client.post( - "/triton_tensorflow_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/jpeg"))], - ) - - -class BentoTensorflowYolov5User(HttpUser): - wait_time = constant(0) - filename = "./data/zidane.jpg" - - @task - def bentoml_tensorflow_yolov5_infer(self): - self.client.post( - "/bentoml_tensorflow_yolov5_infer", - files=[("file", (self.filename, open(self.filename, "rb"), "image/jpeg"))], - ) diff --git a/examples/triton/tensorflow/model_repository/tensorflow_yolov5s/config.pbtxt b/examples/triton/tensorflow/model_repository/tensorflow_yolov5s/config.pbtxt deleted file mode 100644 index ac04789659b..00000000000 --- a/examples/triton/tensorflow/model_repository/tensorflow_yolov5s/config.pbtxt +++ /dev/null @@ -1,16 +0,0 @@ -platform: "tensorflow_savedmodel" -input { -name: "x" -data_type: TYPE_FP32 -dims: -1 -dims: 640 -dims: 640 -dims: 3 -} -output { -name: "output_0" -data_type: TYPE_FP32 -dims: -1 -dims: 25200 -dims: 85 -} diff --git a/examples/triton/tensorflow/requirements/requirements-gpu.txt b/examples/triton/tensorflow/requirements/requirements-gpu.txt deleted file mode 100644 index 100ca74d61f..00000000000 --- a/examples/triton/tensorflow/requirements/requirements-gpu.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -nvidia-tensorrt -onnxruntime-gpu diff --git a/examples/triton/tensorflow/requirements/requirements.txt b/examples/triton/tensorflow/requirements/requirements.txt deleted file mode 100644 index 3991bd20e72..00000000000 --- a/examples/triton/tensorflow/requirements/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -tritonclient[all]>=2.29.0 -opencv-python -torchvision -pydantic -locust -tensorflow;platform_system!='Darwin' -tensorflow-io;platform_system!='Darwin' -tensorflow-macos;platform_system=='Darwin' -onnx -onnxruntime -torch -# YOLOv5 dependencies -seaborn -thop -tqdm -pandas -mss -albumentations diff --git a/examples/triton/tensorflow/serve_bento.py b/examples/triton/tensorflow/serve_bento.py deleted file mode 100644 index d2538ef9b4f..00000000000 --- a/examples/triton/tensorflow/serve_bento.py +++ /dev/null @@ -1,53 +0,0 @@ -from __future__ import annotations - -if __name__ == "__main__": - import argparse - import time - - import bentoml - - parser = argparse.ArgumentParser() - parser.add_argument( - "--grpc", action="store_true", default=False, help="Whether to serve as gRPC." - ) - parser.add_argument("--tag", type=str, default=None) - - args = parser.parse_args() - - tag = "triton-integration-tensorflow" - if args.tag: - tag = f"triton-integration-tensorflow:{args.tag}" - - server_type = "grpc" if args.grpc else "http" - - try: - bento = bentoml.get(tag) - except bentoml.exceptions.NotFound: - raise ValueError( - "Bento is not yet built. Make sure to run 'python3 build_bento.py' and try to run this script again." - ) - else: - bento = bentoml.get(tag) - server = bentoml.serve( - bento, - server_type=server_type, - production=True, - ) - try: - while True: - bentoml.client.Client.wait_until_server_ready( - server.host, server.port, 1000 - ) - client = bentoml.client.Client.from_url( - f"http://localhost:{server.port}" - ) - print( - "Tensorflow config:", - client.model_config( - inp={"model_name": "tensorflow_yolov5s", "protocol": "grpc"} - ), - ) - time.sleep(1e9) - except KeyboardInterrupt: - server.stop() - raise SystemExit(0) diff --git a/examples/triton/tensorflow/service.py b/examples/triton/tensorflow/service.py deleted file mode 100644 index f782c79a3b9..00000000000 --- a/examples/triton/tensorflow/service.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import annotations - -import typing as t - -import helpers -import numpy as np -import torch - -import bentoml - -if t.TYPE_CHECKING: - from PIL.Image import Image - -# triton runner -triton_runner = bentoml.triton.Runner( - "triton_runner", - "./model_repository", - cli_args=[ - "--model-control-mode=explicit", - "--load-model=tensorflow_yolov5s", - ], -) -bentoml_yolov5_tensorflow = bentoml.tensorflow.get("tensorflow-yolov5").to_runner() - -svc = bentoml.Service( - "triton-integration", - runners=[triton_runner, bentoml_yolov5_tensorflow], -) - - -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), output=bentoml.io.JSON() -) -async def bentoml_tensorflow_yolov5_infer(fp: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - fp, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - im = prep.im - _, _, h, w = prep.im.shape - im = prep.im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192, 3) - y = await bentoml_yolov5_tensorflow.async_run(im.cpu().numpy()) - y = [logit if isinstance(logit, np.ndarray) else logit.numpy() for logit in y] - y[0][..., :4] *= [w, h, w, h] - return helpers.postprocess_yolov5_prediction(y, prep) - - -@svc.api( - input=bentoml.io.Image.from_sample("./data/zidane.jpg"), output=bentoml.io.JSON() -) -async def triton_tensorflow_yolov5_infer(fp: Image) -> dict[str, str]: - prep = helpers.prepare_yolov5_input( - fp, 640, False, "cpu" if not torch.cuda.is_available() else "cuda:0", False, 32 - ) - im = prep.im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192, 3) - InferResult = await triton_runner.tensorflow_yolov5s.async_run(im.cpu().numpy()) - return helpers.postprocess_yolov5_prediction(InferResult.as_numpy("output_0"), prep) - - -# Triton Model management API -@svc.api( - input=bentoml.io.JSON.from_sample({"model_name": "tensorflow_yolov5s"}), - output=bentoml.io.JSON(), -) -async def model_config(input_model: dict[t.Literal["model_name"], str]): - return await triton_runner.get_model_config(input_model["model_name"], as_json=True) - - -@svc.api( - input=bentoml.io.Text.from_sample("tensorflow_yolov5s"), output=bentoml.io.JSON() -) -async def unload_model(input_model: str): - await triton_runner.unload_model(input_model) - return {"unloaded": input_model} - - -@svc.api( - input=bentoml.io.Text.from_sample("tensorflow_yolov5s"), output=bentoml.io.JSON() -) -async def load_model(input_model: str): - await triton_runner.load_model(input_model) - return {"loaded": input_model} - - -@svc.api(input=bentoml.io.Text(), output=bentoml.io.JSON()) -async def list_models(_: str) -> list[str]: - resp = await triton_runner.get_model_repository_index() - return [i.name for i in resp.models] diff --git a/examples/triton/tensorflow/setup b/examples/triton/tensorflow/setup deleted file mode 100755 index cd52a5a0945..00000000000 --- a/examples/triton/tensorflow/setup +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash - -set -ex - -if [ -f /etc/lsb-release ]; then - # In Ubuntu - apt-get update && apt-get install -y pkg-config libhdf5-dev ffmpeg libsm6 libxext6 -fi - -if [ -n "${BENTO_PATH}" ] || [ -f /.dockerenv ]; then - # We need to export LD_PRELOAD inside bento container. - SITE_PACKAGES=$(python3 -c "import site; print(site.getsitepackages()[0])") - - export LD_PRELOAD=$(find "$SITE_PACKAGES/torch/lib/" -name "libgomp*" -printf "%p\n") -fi diff --git a/examples/triton/tensorflow/train.py b/examples/triton/tensorflow/train.py deleted file mode 100644 index 0781abe0e82..00000000000 --- a/examples/triton/tensorflow/train.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -import tensorflow as tf -from helpers import MODEL_FILE -from helpers import load_traced_script - -import bentoml - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - - parser.add_argument( - "--override", - action="store_true", - default=False, - help="Override existing models", - ) - args = parser.parse_args() - - bento_model_name = "tensorflow-yolov5" - - try: - bentoml.models.get(bento_model_name) - if args.override: - raise bentoml.exceptions.NotFound( - "'override=True', overriding previously saved weights/conversions." - ) - print(f"{bento_model_name} already exists. Skipping...") - except bentoml.exceptions.NotFound: - _, metadata = load_traced_script() - model = tf.saved_model.load( - MODEL_FILE.__fspath__().replace(".pt", "_saved_model") - ) - print( - "Saved model:", - bentoml.tensorflow.save_model( - bento_model_name, - model, - signatures={"__call__": {"batchable": True, "batch_dim": (0, 0)}}, - tf_save_options=tf.saved_model.SaveOptions( - experimental_custom_gradients=False - ), - metadata={"model_info": metadata}, - ), - ) - except Exception: - print("Failed to save model:", bento_model_name) - raise diff --git a/examples/tutorial/bentofile.yaml b/examples/tutorial/bentofile.yaml deleted file mode 100644 index dfdad9a1b15..00000000000 --- a/examples/tutorial/bentofile.yaml +++ /dev/null @@ -1,8 +0,0 @@ -service: 'service.py:svc' -name: text-classification-svc -include: - - 'service.py' -python: - packages: - - torch>=2.0 - - transformers diff --git a/examples/tutorial/init_model.py b/examples/tutorial/init_model.py deleted file mode 100644 index 9fdfa0e7ddf..00000000000 --- a/examples/tutorial/init_model.py +++ /dev/null @@ -1,11 +0,0 @@ -import transformers - -import bentoml - -pipe = transformers.pipeline("text-classification") - -bentoml.transformers.save_model( - "text-classification-pipe", - pipe, - signatures={"__call__": {"batchable": True}}, # Enable dynamic batching for model -) diff --git a/examples/tutorial/requirements.txt b/examples/tutorial/requirements.txt deleted file mode 100644 index 3813d0a7aa8..00000000000 --- a/examples/tutorial/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -bentoml -torch>=2.0 -transformers diff --git a/examples/tutorial/service.py b/examples/tutorial/service.py deleted file mode 100644 index 6caa172f1a7..00000000000 --- a/examples/tutorial/service.py +++ /dev/null @@ -1,11 +0,0 @@ -import bentoml - -model_runner = bentoml.models.get("text-classification-pipe").to_runner() - -svc = bentoml.Service("text-classification-service", runners=[model_runner]) - - -@svc.api(input=bentoml.io.Text(), output=bentoml.io.JSON()) -async def classify(text: str) -> str: - results = await model_runner.async_run([text]) - return results[0] diff --git a/examples/xgboost/README.md b/examples/xgboost/README.md deleted file mode 100644 index 675e6f01219..00000000000 --- a/examples/xgboost/README.md +++ /dev/null @@ -1,180 +0,0 @@ -# BentoML XGBoost Demo - -This is a sample project demonstrating basic usage of BentoML with XGBoost. - -### Install Dependencies - -Install python packages required for running this project: - -```bash -pip install -r ./requirements.txt -``` - -### Model Training - -First step, train a classification model with the UCI Machine Learning Repository Agaricus mushroom -dataset and save the model with BentoML: - -```bash -python train.py -``` - -This should save a new model in the BentoML local model store: - -```bash -bentoml models list agaricus -``` - -Verify that the model can be loaded as runner from Python shell: - -```python -import bentoml - -runner = bentoml.xgboost.get("agaricus:latest").to_runner() - -runner.run([0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, - 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, - 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, - 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]) # => array(0.01241208, dtype=float32) -``` - -### Create ML Service - -The ML Service code is defined in the `agaricus.py` file: - -```python -# agaricus.py -import typing - -import bentoml -import xgboost -from bentoml.io import NumpyNdarray, File - -if typing.TYPE_CHECKING: - import numpy as np - -agaricus_runner = bentoml.xgboost.get("agaricus:latest").to_runner() - -svc = bentoml.Service("agaricus", runners=[agaricus_runner]) - - -@svc.api(input=NumpyNdarray(), output=NumpyNdarray()) -def classify(input_data: "np.ndarray") -> "np.ndarray": - return agaricus_runner.run(input_data) -``` - -Start an API server locally to test the service code above: - -```bash -bentoml serve agaricus:svc --development --reload -``` - -With the `--reload` flag, the API server will automatically restart when the source -file `agaricus.py` is being edited, to boost your development productivity. - -Verify the endpoint can be accessed locally: - -```bash -curl -X POST -H "content-type: application/json" --data "[[0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]]" http://127.0.0.1:3000/classify -``` - -### Build Bento for deployment - -A `bentofile` for the agaricus service is also contained in this directory: - -```yaml -service: "agaricus:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: - - "*.py" -exclude: - - "locustfile.py" -python: - packages: - - xgboost -``` - -Simply run `bentoml build` from this directory to build a Bento with the latest version of the -`agaricus` model. This may take a while when running for the first time, as BentoML needs to resolve -all dependency versions: - -``` -> bentoml build - -03/07/2022 12:25:16 PM INFO [cli] Building BentoML service "agaricus:uvkv7d46cgnvgeb5" from build context "/home/user/devel/gallery/xgboost" -03/07/2022 12:25:16 PM INFO [cli] Packing model "agaricus:3t4533c6ufi6zcz2ca6rzl235" from "/home/user/bentoml/models/agaricus/3t4533c6ufi6zcz2ca6rzl235" -03/07/2022 12:25:16 PM INFO [cli] Successfully saved Model(tag="agaricus:3t4533c6ufi6zcz2ca6rzl235", - path="/run/user/1000/tmpw8lyba_sbentoml_bento_agaricus/models/agaricus/3t4533c6ufi6zcz2ca6rzl235/") -03/07/2022 12:25:16 PM INFO [cli] Locking PyPI package versions.. -03/07/2022 12:25:17 PM INFO [cli] - ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ - ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ - ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ - ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ - ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ - ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ - -03/07/2022 12:25:17 PM INFO [cli] Successfully built Bento(tag="agaricus:uvkv7d46cgnvgeb5") at "/home/user/bentoml/bentos/agaricus/uvkv7d46cgnvgeb5/" -``` - -This Bento can now be served: - -```bash -bentoml serve agaricus:latest -``` - -The Bento directory contains all code, files, models and configuration required to run this service. -BentoML standarizes this file structure, enabling serving runtimes and deployment tools to be built -on top of it. By default, Bentos are managed under the `~/bentoml/bentos` directory: - -``` -> cd $(bentoml get agaricus:latest -o path) - -> tree -. -├── apis -│   └── openapi.yaml -├── bento.yaml -├── env -│   ├── conda -│   ├── docker -│   │   ├── Dockerfile -│   │   ├── entrypoint.sh -│   │   └── init.sh -│   └── python -│   ├── requirements.lock.txt -│   ├── requirements.txt -│   └── version.txt -├── models -│   └── agaricus -│   ├── 3t4533c6ufi6zcz2ca6rzl235 -│   │   ├── model.yaml -│   │   └── saved_model.json -│   └── latest -├── README.md -└── src - ├── agaricus.py - └── train.py - -9 directories, 14 files -``` - -### Containerize Bento for deployment - -Make sure you have docker installed and the docker daemon is running. The following command will use -your local docker environment to build a new docker image containing the Bento: - -```bash -bentoml containerize agaricus:latest -``` - -To test out the newly created docker image: - -```bash -docker run agaricus: -p 3000:3000 -``` diff --git a/examples/xgboost/agaricus.ipynb b/examples/xgboost/agaricus.ipynb deleted file mode 100644 index 3c2738056ce..00000000000 --- a/examples/xgboost/agaricus.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "747e0e8d", - "metadata": {}, - "source": [ - "# Quickstart with BentoML and XGBoost\n", - "\n", - "Link to source code: https://github.com/bentoml/BentoML/tree/main/examples/xgboost" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d4454bd", - "metadata": {}, - "outputs": [], - "source": [ - "import xgboost\n", - "import bentoml" - ] - }, - { - "cell_type": "markdown", - "id": "b66e31f7", - "metadata": {}, - "source": [ - "### Train a classifier using the agaricus dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb526488", - "metadata": {}, - "outputs": [], - "source": [ - "# read in data\n", - "dtrain = xgboost.DMatrix(\"data/agaricus.txt.train\")\n", - "\n", - "# specify parameters via map\n", - "param = {\"max_depth\": 2, \"eta\": 1, \"objective\": \"binary:logistic\"}\n", - "num_round = 2\n", - "bst = xgboost.train(param, dtrain, num_round)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e94ed449", - "metadata": {}, - "outputs": [], - "source": [ - "bentoml.xgboost.save_model(\"agaricus\", bst)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a876780", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml models list" - ] - }, - { - "cell_type": "markdown", - "id": "672721c4", - "metadata": {}, - "source": [ - "Test loading the model as a BentoML Runner instance:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28ac794b", - "metadata": {}, - "outputs": [], - "source": [ - "test_runner = bentoml.xgboost.get(\"agaricus:latest\").to_runner()\n", - "test_runner.init_local()\n", - "\n", - "# fmt: off\n", - "test_runner.run([[0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n", - " 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n", - " 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n", - " 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n", - " 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n", - " 0, 0, 0, 0, 0, 0, 1]]) # => array(0.01241208, dtype=float32)\n", - "# fmt: on" - ] - }, - { - "cell_type": "markdown", - "id": "3fa68254", - "metadata": {}, - "source": [ - "### Create a BentoML Service for serving the model\n", - "\n", - "Note: using `%%writefile` here because `bentoml.Service` instance must be created in a separate `.py` file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "127aa3fd", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile agaricus.py\n", - "import typing\n", - "\n", - "import bentoml\n", - "import xgboost\n", - "from bentoml.io import NumpyNdarray, File\n", - "\n", - "if typing.TYPE_CHECKING:\n", - " import numpy as np\n", - "\n", - "agaricus_runner = bentoml.xgboost.get(\"agaricus:latest\").to_runner()\n", - "\n", - "svc = bentoml.Service(\"agaricus\", runners=[agaricus_runner])\n", - "\n", - "@svc.api(input=NumpyNdarray(), output=NumpyNdarray())\n", - "def classify(input_data: \"np.ndarray\") -> \"np.ndarray\":\n", - " return agaricus_runner.run(input_data)" - ] - }, - { - "cell_type": "markdown", - "id": "203beeed", - "metadata": {}, - "source": [ - "Start a dev model server to test out the service defined above:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7523b58f", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve agaricus.py:svc" - ] - }, - { - "cell_type": "markdown", - "id": "3974e4ce", - "metadata": {}, - "source": [ - "Open a new browser tab and test out the API endpoint from the web UI." - ] - }, - { - "cell_type": "markdown", - "id": "4f1a8bcc", - "metadata": {}, - "source": [ - "### Build a Bento for distribution and deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ef11159", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml build" - ] - }, - { - "cell_type": "markdown", - "id": "47505e3c", - "metadata": {}, - "source": [ - "Starting a dev server with the Bento build:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7cab8b2", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml serve agaricus:latest" - ] - }, - { - "cell_type": "markdown", - "id": "4c159551", - "metadata": {}, - "source": [ - "## Hooray, your model is ready for production now 🎉\n", - "\n", - "Bento is a standarized format for storing models alongside with all their API definitions, configuration, and environment settings. BentoML can start an REST API server serving a Bento, run a Bento as batch processing job on distributed dataset, or containerize all dependencies and models into a docker container image for easy production deployment." - ] - }, - { - "cell_type": "markdown", - "id": "81ed8b84", - "metadata": {}, - "source": [ - "### Optional: create a docker image for the model server" - ] - }, - { - "cell_type": "markdown", - "id": "8c215454", - "metadata": {}, - "source": [ - "This will require docker to be installed and docker daemon to be running:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5047751c", - "metadata": {}, - "outputs": [], - "source": [ - "!bentoml containerize agaricus:latest" - ] - }, - { - "cell_type": "markdown", - "id": "b5bdf2a1", - "metadata": {}, - "source": [ - "## What's next?\n", - "\n", - "* Learn more at http://docs.bentoml.com 📖\n", - "* Join [BentoML Slack community](https://join.slack.com/t/bentoml/shared_invite/enQtNjcyMTY3MjE4NTgzLTU3ZDc1MWM5MzQxMWQxMzJiNTc1MTJmMzYzMTYwMjQ0OGEwNDFmZDkzYWQxNzgxYWNhNjAxZjk4MzI4OGY1Yjg) 👈\n", - "* Follow us on [Twitter](https://twitter.com/bentomlai) 🐦\n", - "* Contribute to [BentoML on GitHub](https://github.com/bentoml/BentoML) 💻" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65e092c7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.5 64-bit ('3.10.5')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - }, - "vscode": { - "interpreter": { - "hash": "b6c37f2c42f5754f4e69d3366aab52a1fae3caec233d069fa3e31792c24a7a5a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/xgboost/agaricus.py b/examples/xgboost/agaricus.py deleted file mode 100644 index b37e1810181..00000000000 --- a/examples/xgboost/agaricus.py +++ /dev/null @@ -1,16 +0,0 @@ -import typing - -import bentoml -from bentoml.io import NumpyNdarray - -if typing.TYPE_CHECKING: - import numpy as np - -agaricus_runner = bentoml.xgboost.get("agaricus:latest").to_runner() - -svc = bentoml.Service("agaricus", runners=[agaricus_runner]) - - -@svc.api(input=NumpyNdarray(), output=NumpyNdarray()) -async def classify(input_data: "np.ndarray") -> "np.ndarray": - return await agaricus_runner.async_run(input_data) diff --git a/examples/xgboost/bentofile.yaml b/examples/xgboost/bentofile.yaml deleted file mode 100644 index 9ceeb78d1b9..00000000000 --- a/examples/xgboost/bentofile.yaml +++ /dev/null @@ -1,12 +0,0 @@ -service: "agaricus:svc" -description: "file: ./README.md" -labels: - owner: bentoml-team - stage: demo -include: - - "*.py" -exclude: - - "locustfile.py" -python: - packages: - - xgboost diff --git a/examples/xgboost/blah.txt b/examples/xgboost/blah.txt deleted file mode 100644 index b2f0ed93c84..00000000000 --- a/examples/xgboost/blah.txt +++ /dev/null @@ -1 +0,0 @@ -1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1 diff --git a/examples/xgboost/data/agaricus.txt.train b/examples/xgboost/data/agaricus.txt.train deleted file mode 100644 index 10c790226d4..00000000000 --- a/examples/xgboost/data/agaricus.txt.train +++ /dev/null @@ -1,6513 +0,0 @@ -1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1 -0 3:1 10:1 20:1 21:1 23:1 34:1 36:1 39:1 41:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 116:1 120:1 -0 1:1 10:1 19:1 21:1 24:1 34:1 36:1 39:1 42:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 116:1 122:1 -1 3:1 9:1 19:1 21:1 30:1 34:1 36:1 40:1 42:1 53:1 58:1 65:1 69:1 77:1 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91:1 95:1 102:1 107:1 115:1 121:1 -0 3:1 10:1 11:1 22:1 29:1 32:1 36:1 39:1 52:1 53:1 61:1 65:1 69:1 74:1 83:1 88:1 91:1 95:1 102:1 110:1 115:1 121:1 diff --git a/examples/xgboost/locustfile.py b/examples/xgboost/locustfile.py deleted file mode 100644 index d7a83026681..00000000000 --- a/examples/xgboost/locustfile.py +++ /dev/null @@ -1,17 +0,0 @@ -import numpy as np -from locust import HttpUser -from locust import between -from locust import task -from sklearn.datasets import load_svmlight_file - -test_data = load_svmlight_file("data/agaricus.txt.train") -num_of_rows = len(test_data[0]) - - -class AgaricusLoadTestUser(HttpUser): - @task - def classify(self): - input_data = test_data[1][np.random.choice(num_of_rows)] - self.client.post("/classify", json=list(input_data)) - - wait_time = between(0.01, 2) diff --git a/examples/xgboost/requirements.txt b/examples/xgboost/requirements.txt deleted file mode 100644 index 566796d826f..00000000000 --- a/examples/xgboost/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -bentoml>=1.0.0 -xgboost diff --git a/examples/xgboost/train.py b/examples/xgboost/train.py deleted file mode 100644 index 7507183c9a9..00000000000 --- a/examples/xgboost/train.py +++ /dev/null @@ -1,19 +0,0 @@ -import xgboost - -import bentoml - -if __name__ == "__main__": - # read in data - dtrain = xgboost.DMatrix("data/agaricus.txt.train") - - # specify parameters via dictionary - param = { - "booster": "dart", - "max_depth": 2, - "eta": 1, - "objective": "binary:logistic", - } - num_round = 2 - bst = xgboost.train(param, dtrain, num_round) - - bentoml.xgboost.save_model("agaricus", bst) From 528d78c9c97dbcbc704b4ed903b234c3b53a65e8 Mon Sep 17 00:00:00 2001 From: Zhao Shenyang Date: Mon, 29 Apr 2024 17:24:24 +0800 Subject: [PATCH 2/3] add back old monitoring example for now --- .../task_classification/.bentoignore | 4 + .../monitoring/task_classification/.gitignore | 1 + .../monitoring/task_classification/README.md | 189 ++++++++++++++++++ .../task_classification/bentofile.yaml | 10 + .../task_classification/deployment.yaml | 2 + .../task_classification/locustfile.py | 33 +++ .../task_classification/requirements.txt | 3 + .../monitoring/task_classification/service.py | 37 ++++ .../task_classification/tests/conftest.py | 66 ++++++ .../tests/test_log_collection.py | 22 ++ .../monitoring/task_classification/train.py | 21 ++ 11 files changed, 388 insertions(+) create mode 100644 examples/monitoring/task_classification/.bentoignore create mode 100644 examples/monitoring/task_classification/.gitignore create mode 100644 examples/monitoring/task_classification/README.md create mode 100644 examples/monitoring/task_classification/bentofile.yaml create mode 100644 examples/monitoring/task_classification/deployment.yaml create mode 100644 examples/monitoring/task_classification/locustfile.py create mode 100644 examples/monitoring/task_classification/requirements.txt create mode 100644 examples/monitoring/task_classification/service.py create mode 100644 examples/monitoring/task_classification/tests/conftest.py create mode 100644 examples/monitoring/task_classification/tests/test_log_collection.py create mode 100644 examples/monitoring/task_classification/train.py diff --git a/examples/monitoring/task_classification/.bentoignore b/examples/monitoring/task_classification/.bentoignore new file mode 100644 index 00000000000..f4e455d1cb8 --- /dev/null +++ b/examples/monitoring/task_classification/.bentoignore @@ -0,0 +1,4 @@ +__pycache__/ +*.py[cod] +*$py.class +.ipynb_checkpoints diff --git a/examples/monitoring/task_classification/.gitignore b/examples/monitoring/task_classification/.gitignore new file mode 100644 index 00000000000..05e584b7434 --- /dev/null +++ b/examples/monitoring/task_classification/.gitignore @@ -0,0 +1 @@ +/monitoring diff --git a/examples/monitoring/task_classification/README.md b/examples/monitoring/task_classification/README.md new file mode 100644 index 00000000000..31582674136 --- /dev/null +++ b/examples/monitoring/task_classification/README.md @@ -0,0 +1,189 @@ +# BentoML monitoring example for classification tasks + +This is a sample project demonstrating basic monitoring usage of [BentoML](https://github.com/bentoml). + +In this project, we will train a classifier model using Scikit-learn and the Iris dataset, build +an prediction service for serving the trained model with monitoring enabled, and deploy the +model server as a docker image for production deployment. + +### Install Dependencies + +Install python packages required for running this project: +```bash +pip install -r ./requirements.txt +``` + +### Model Training + +Create an Iris classifier and save it with `bentoml.sklearn`: + +```bash +import bentoml +from sklearn import svm, datasets + +# Load training data +iris = datasets.load_iris() +X, y = iris.data, iris.target + +# Model Training +clf = svm.SVC() +clf.fit(X, y) + +# Save model to BentoML local model store +bentoml.sklearn.save_model("iris_clf", clf) +``` + +### Serving the model +Draft a `service.py` file with monitoring data collection lines, and run your service with Bento Server locally: + +```python +import numpy as np + +import bentoml +from bentoml.io import Text +from bentoml.io import NumpyNdarray + +CLASS_NAMES = ["setosa", "versicolor", "virginica"] + +iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner() +svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner]) + + +@svc.api( + input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)), + output=Text(), +) +async def classify(features: np.ndarray) -> str: + with bentoml.monitor("iris_classifier_prediction") as mon: + mon.log(features[0], name="sepal length", role="feature", data_type="numerical") + mon.log(features[1], name="sepal width", role="feature", data_type="numerical") + mon.log(features[2], name="petal length", role="feature", data_type="numerical") + mon.log(features[3], name="petal width", role="feature", data_type="numerical") + + results = await iris_clf_runner.predict.async_run([features]) + result = results[0] + category = CLASS_NAMES[result] + + mon.log(category, name="pred", role="prediction", data_type="categorical") + return category +``` + +```bash +bentoml serve service.py:svc --reload +``` + +Open your web browser at http://127.0.0.1:3000 to view the Bento UI for sending test requests. + +You may also send request with `curl` command or any HTTP client, e.g.: + +```bash +curl -X POST -H "content-type: application/json" --data "[[5.9, 3, 5.1, 1.8]]" http://127.0.0.1:3000/classify +``` + + +Then you can find the exported data under the `./monitoring//data` directory. +Here's the example output: + +```json +{"timestamp": "2022-11-02T12:38:38.701396", "request_id": 8781503815303167270, "sepal length": 5.9, "sepal width": 3.0, "petal length": 1.4, "petal width": 0.2, "pred": "0"} +{"timestamp": "2022-11-02T12:38:48.345552", "request_id": 14419506828678509143, "sepal length": 4.9, "sepal width": 3.0, "petal length": 1.4, "petal width": 0.2, "pred": "0"} +``` + + +### Customizing the monitoring + +You can customize the monitoring by modifying the config file of bentoml. The default is: + +```yaml +monitoring: + enabled: true + type: default + options: + output_dir: ./monitoring +``` + +You can draft your own bentoml config file `deployment.yaml` and change the `output_dir` to any directory you want. You can also use other monitoring solutions by changing the `type` to your desired handler. For example, if you want to use the `arize` handler, you can change the config to: + +```yaml +monitoring: + enabled: true + type: bentoml_plugins.arize.ArizeMonitor + options: + api_key: + space_key: +``` + +Then you can specify the config file through environment variable `BENTOML_CONFIG`: +```bash +BENTOML_CONFIG=deployment.yaml bentoml serve service.py:svc +``` + + +### Containerized Serving with monitoring + +Bento is the distribution format in BentoML which captures all the source code, model files, config +files and dependency specifications required for running the service for production deployment. Think +of it as Docker/Container designed for machine learning models. + +To begin with building Bento, create a `bentofile.yaml` under your project directory: + +```yaml +service: "service.py:svc" +labels: + owner: bentoml-team + project: gallery +include: +- "*.py" +python: + packages: + - scikit-learn + - pandas +``` + +Next, run `bentoml build` from current directory to start the Bento build: + +``` +> bentoml build + +05/05/2022 19:19:16 INFO [cli] Building BentoML service "iris_classifier:5wtigdwm4kwzduqj" from build context "/Users/bentoml/workspace/gallery/quickstart" +05/05/2022 19:19:16 INFO [cli] Packing model "iris_clf:4i7wbngm4crhpuqj" from "/Users/bentoml/bentoml/models/iris_clf/4i7wbngm4crhpuqj" +05/05/2022 19:19:16 INFO [cli] Successfully saved Model(tag="iris_clf:4i7wbngm4crhpuqj", + path="/var/folders/bq/gdsf0kmn2k1bf880r_l238600000gn/T/tmp26dx354ubentoml_bento_iris_classifier/models/iris_clf/4i7wbngm4crhpuqj/") +05/05/2022 19:19:16 INFO [cli] Locking PyPI package versions.. +05/05/2022 19:19:17 INFO [cli] + ██████╗░███████╗███╗░░██╗████████╗░█████╗░███╗░░░███╗██╗░░░░░ + ██╔══██╗██╔════╝████╗░██║╚══██╔══╝██╔══██╗████╗░████║██║░░░░░ + ██████╦╝█████╗░░██╔██╗██║░░░██║░░░██║░░██║██╔████╔██║██║░░░░░ + ██╔══██╗██╔══╝░░██║╚████║░░░██║░░░██║░░██║██║╚██╔╝██║██║░░░░░ + ██████╦╝███████╗██║░╚███║░░░██║░░░╚█████╔╝██║░╚═╝░██║███████╗ + ╚═════╝░╚══════╝╚═╝░░╚══╝░░░╚═╝░░░░╚════╝░╚═╝░░░░░╚═╝╚══════╝ + +05/05/2022 19:19:17 INFO [cli] Successfully built Bento(tag="iris_classifier:5wtigdwm4kwzduqj") at "/Users/bentoml/bentoml/bentos/iris_classifier/5wtigdwm4kwzduqj/" +``` + +A new Bento is now built and saved to local Bento store. You can view and manage it via +`bentoml list`,`bentoml get` and `bentoml delete` CLI command. + +Then we will convert a Bento into a Docker image containing the HTTP model server. + +Make sure you have docker installed and docker deamon running, and run the following commnand: + +```bash +bentoml containerize iris_classifier:latest +``` + +This will build a new docker image with all source code, model files and dependencies in place, +and ready for production deployment. To start a container with this docker image locally, run: + +```bash +docker run -p 3000:3000 iris_classifier:invwzzsw7li6zckb2ie5eubhd --mount type=bind,source=,target=/bento/monitoring +``` + +## What's Next? + +- 👉 [Pop into our Slack community!](https://l.bentoml.com/join-slack) We're happy to help with any issue you face or even just to meet you and hear what you're working on. +- Dive deeper into the [Core Concepts](https://docs.bentoml.com/en/latest/concepts/index.html) in BentoML +- Learn how to use BentoML with other ML Frameworks at [Frameworks Guide](https://docs.bentoml.com/en/latest/frameworks/index.html) or check out other [gallery projects](https://github.com/bentoml/BentoML/tree/main/examples) +- Learn more about model deployment options for Bento: + - [🦄️ Yatai](https://github.com/bentoml/Yatai): Model Deployment at scale on Kubernetes + - [🚀 bentoctl](https://github.com/bentoml/bentoctl): Fast model deployment on any cloud platform diff --git a/examples/monitoring/task_classification/bentofile.yaml b/examples/monitoring/task_classification/bentofile.yaml new file mode 100644 index 00000000000..749288a228a --- /dev/null +++ b/examples/monitoring/task_classification/bentofile.yaml @@ -0,0 +1,10 @@ +service: "service.py:svc" +labels: + owner: bentoml-team + project: gallery +include: +- "*.py" +python: + packages: + - scikit-learn + - pandas diff --git a/examples/monitoring/task_classification/deployment.yaml b/examples/monitoring/task_classification/deployment.yaml new file mode 100644 index 00000000000..5302969a146 --- /dev/null +++ b/examples/monitoring/task_classification/deployment.yaml @@ -0,0 +1,2 @@ +monitoring: + enabled: true diff --git a/examples/monitoring/task_classification/locustfile.py b/examples/monitoring/task_classification/locustfile.py new file mode 100644 index 00000000000..8021cf373c0 --- /dev/null +++ b/examples/monitoring/task_classification/locustfile.py @@ -0,0 +1,33 @@ +import numpy as np +from locust import HttpUser +from locust import between +from locust import task +from sklearn import datasets + +test_data = datasets.load_iris().data +num_of_rows = test_data.shape[0] + + +class IrisHttpUser(HttpUser): + """ + Usage: + Run the iris_classifier service in production mode: + + bentoml serve-http iris_classifier:latest + + Start locust load testing client with: + + locust --class-picker -H http://localhost:3000 + + Open browser at http://0.0.0.0:8089, adjust desired number of users and spawn + rate for the load test from the Web UI and start swarming. + """ + + @task + def classify(self): + index = np.random.choice(num_of_rows - 1) + + input_data = test_data[index] + self.client.post("/classify", json=input_data.tolist()) + + wait_time = between(0.01, 2) diff --git a/examples/monitoring/task_classification/requirements.txt b/examples/monitoring/task_classification/requirements.txt new file mode 100644 index 00000000000..4e853a4faf0 --- /dev/null +++ b/examples/monitoring/task_classification/requirements.txt @@ -0,0 +1,3 @@ +scikit-learn +pandas +bentoml[monitor-otlp]>=1.0.19 diff --git a/examples/monitoring/task_classification/service.py b/examples/monitoring/task_classification/service.py new file mode 100644 index 00000000000..b368426992c --- /dev/null +++ b/examples/monitoring/task_classification/service.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +import os + +import numpy as np + +import bentoml +from bentoml.io import NumpyNdarray +from bentoml.io import Text + +CLASS_NAMES = ["setosa", "versicolor", "virginica"] + +iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner() +svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner]) + +LOG_PATH = os.environ.get("MONITORING_LOG_PATH", "/tmp/iris_monitoring") + + +@svc.api( + input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)), + output=Text(), +) +async def classify(features: np.ndarray) -> str: + with bentoml.monitor( + "iris_classifier_prediction", monitor_options={"log_path": LOG_PATH} + ) as mon: + mon.log(features[0], name="sepal length", role="feature", data_type="numerical") + mon.log(features[1], name="sepal width", role="feature", data_type="numerical") + mon.log(features[2], name="petal length", role="feature", data_type="numerical") + mon.log(features[3], name="petal width", role="feature", data_type="numerical") + + results = await iris_clf_runner.predict.async_run([features]) + result = results[0] + category = CLASS_NAMES[result] + + mon.log(category, name="pred", role="prediction", data_type="categorical") + return category diff --git a/examples/monitoring/task_classification/tests/conftest.py b/examples/monitoring/task_classification/tests/conftest.py new file mode 100644 index 00000000000..d74464e3f11 --- /dev/null +++ b/examples/monitoring/task_classification/tests/conftest.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import contextlib +import os +import subprocess +import sys +import typing as t +from pathlib import Path + +import numpy as np +import pytest + +import bentoml +from bentoml._internal.configuration.containers import BentoMLContainer + +if t.TYPE_CHECKING: + from _pytest.fixtures import FixtureRequest + from _pytest.tmpdir import TempPathFactory + +PROJECT_DIR = Path(__file__).parent.parent + + +@pytest.fixture(scope="session", autouse=True) +def prepare_model() -> None: + try: + print(f"Found {bentoml.models.get('iris_clf')}, skipping model saving.") + except bentoml.exceptions.NotFound: + subprocess.check_call( + [sys.executable, PROJECT_DIR.joinpath("train.py").__fspath__()] + ) + + +@pytest.fixture( + name="monitoring_type", params=["default", "otlp"], scope="session", autouse=True +) +def fixture_monitoring_type(request: FixtureRequest) -> str: + BentoMLContainer.config.monitoring.type.set(request.param) + return request.param + + +@pytest.fixture(name="monitoring_dir", scope="session") +def fixture_monitoring_dir(tmp_path_factory: TempPathFactory) -> Path: + d = tmp_path_factory.mktemp("monitoring") + os.environ["MONITORING_LOG_PATH"] = d.__fspath__() + return d + + +@pytest.fixture(scope="session") +def host( + bentoml_home: str, + deployment_mode: t.Literal["container", "distributed", "standalone"], + clean_context: contextlib.ExitStack, + monitoring_dir: Path, +): + from bentoml.testing.server import host_bento + + with host_bento( + "service:svc", + project_path=PROJECT_DIR.__fspath__(), + deployment_mode=deployment_mode, + bentoml_home=bentoml_home, + clean_context=clean_context, + ) as _host: + client = bentoml.client.Client.from_url(_host) + for _ in range(10): + client.classify(np.array([4.9, 3.0, 1.4, 0.2])) diff --git a/examples/monitoring/task_classification/tests/test_log_collection.py b/examples/monitoring/task_classification/tests/test_log_collection.py new file mode 100644 index 00000000000..42b55cfa9c0 --- /dev/null +++ b/examples/monitoring/task_classification/tests/test_log_collection.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +import typing as t + +import pandas as pd +import pytest + +if t.TYPE_CHECKING: + from pathlib import Path + + +@pytest.mark.asyncio +async def test_log_collection(host: str, monitoring_dir: Path): + data_path = monitoring_dir.joinpath("iris_classifier_prediction", "data") + assert monitoring_dir.exists() + assert data_path.exists() + assert ( + pd.concat( + [pd.read_json(f.__fspath__(), lines=True) for f in data_path.glob("*")] + ) + is not None + ) diff --git a/examples/monitoring/task_classification/train.py b/examples/monitoring/task_classification/train.py new file mode 100644 index 00000000000..122186740f4 --- /dev/null +++ b/examples/monitoring/task_classification/train.py @@ -0,0 +1,21 @@ +import logging + +from sklearn import datasets +from sklearn import svm + +import bentoml + +logging.basicConfig(level=logging.WARN) + +if __name__ == "__main__": + # Load training data + iris = datasets.load_iris() + X, y = iris.data, iris.target + + # Model Training + clf = svm.SVC() + clf.fit(X, y) + + # Save model to BentoML local model store + saved_model = bentoml.sklearn.save_model("iris_clf", clf) + print(f"Model saved: {saved_model}") From a8771b1c01e9399b298e07e5663a003ed2fa8e73 Mon Sep 17 00:00:00 2001 From: Zhao Shenyang Date: Mon, 29 Apr 2024 18:24:03 +0800 Subject: [PATCH 3/3] minor fix for io descriptor example --- examples/io-descriptors/requirements.txt | 3 --- examples/io-descriptors/service.py | 4 ++-- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/examples/io-descriptors/requirements.txt b/examples/io-descriptors/requirements.txt index 555c2926121..3f2b5da9ae4 100644 --- a/examples/io-descriptors/requirements.txt +++ b/examples/io-descriptors/requirements.txt @@ -1,8 +1,5 @@ -diffusers bentoml -transformers torch -accelerate pydub pdf2img pandas diff --git a/examples/io-descriptors/service.py b/examples/io-descriptors/service.py index 0790366c4e5..5fe2b430bf4 100644 --- a/examples/io-descriptors/service.py +++ b/examples/io-descriptors/service.py @@ -48,9 +48,9 @@ def append_string_to_eof( txt_file: t.Annotated[Path, bentoml.validators.ContentType("text/plain")], input_string: str, ) -> t.Annotated[Path, bentoml.validators.ContentType("text/plain")]: - with open(output_path, "a") as file: + with open(txt_file, "a") as file: file.write(input_string) - return output_path + return txt_file @bentoml.service()