From 9e540f758a79c63f84671fc4c9fc5d66b388d24d Mon Sep 17 00:00:00 2001 From: Hongbo Miao <3375461+Hongbo-Miao@users.noreply.github.com> Date: Sun, 13 Aug 2023 04:12:36 -0700 Subject: [PATCH] feat(sagemaker): add pytorch mnist --- .editorconfig | 8 +- .github/workflows/.static-type-check.yml | 1 + Makefile | 2 + .../studio/hm-studio/hm-workspace.ipynb | 8 - .../hm-motor-jdbc-sink-connector.json | 2 +- aws/amazon-sagemaker/Makefile | 7 + aws/amazon-sagemaker/pytorch-mnist/Makefile | 23 + .../pytorch-mnist/notebook.ipynb | 163 ++ .../pytorch-mnist/poetry.lock | 1471 +++++++++++++++++ .../pytorch-mnist/pyproject.toml | 21 + .../pytorch-mnist/src/main.py | 27 + .../pytorch-mnist/src/models/net.py | 21 + .../src/utils/average_gradients.py | 9 + .../pytorch-mnist/src/utils/get_args.py | 84 + .../src/utils/get_test_data_loader.py | 22 + .../src/utils/get_train_data_loader.py | 29 + .../pytorch-mnist/src/utils/save_model.py | 12 + .../pytorch-mnist/src/utils/test.py | 31 + .../pytorch-mnist/src/utils/train.py | 104 ++ 19 files changed, 2032 insertions(+), 13 deletions(-) create mode 100644 aws/amazon-sagemaker/Makefile create mode 100644 aws/amazon-sagemaker/pytorch-mnist/Makefile create mode 100644 aws/amazon-sagemaker/pytorch-mnist/notebook.ipynb create mode 100644 aws/amazon-sagemaker/pytorch-mnist/poetry.lock create mode 100644 aws/amazon-sagemaker/pytorch-mnist/pyproject.toml create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/main.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/models/net.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/average_gradients.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/get_args.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/get_test_data_loader.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/get_train_data_loader.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/save_model.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/test.py create mode 100644 aws/amazon-sagemaker/pytorch-mnist/src/utils/train.py diff --git a/.editorconfig b/.editorconfig index 5f8f6c9e7e..d8e01fee4c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -18,6 +18,10 @@ indent_style = space indent_size = 2 indent_style = space +[{*.ipynb,*.py}] +indent_size = 4 +indent_style = space + [*.java] indent_size = 2 indent_style = space @@ -34,10 +38,6 @@ indent_style = space indent_size = 4 indent_style = space -[*.py] -indent_size = 4 -indent_style = space - [*.rego] indent_size = 4 indent_style = tab diff --git a/.github/workflows/.static-type-check.yml b/.github/workflows/.static-type-check.yml index dc16bfca0f..5221224e6b 100644 --- a/.github/workflows/.static-type-check.yml +++ b/.github/workflows/.static-type-check.yml @@ -104,6 +104,7 @@ jobs: - name: Static type check Python run: | poetry run poe static-type-check-python -- --package=api-python + poetry run poe static-type-check-python -- --package=aws.amazon-sagemaker.pytorch-mnist poetry run poe static-type-check-python -- --package=chatbot poetry run poe static-type-check-python -- --package=convolutional-neural-network poetry run poe static-type-check-python -- --package=data-distribution-service diff --git a/Makefile b/Makefile index 98873979fc..b639c12819 100644 --- a/Makefile +++ b/Makefile @@ -177,6 +177,7 @@ poetry-cache-clear: clean-jupyter-notebook: poetry run poe clean-jupyter-notebook -- aws/amazon-emr/studio/hm-studio/hm-workspace.ipynb + poetry run poe clean-jupyter-notebook -- aws/amazon-sagemaker/pytorch-mnist/notebook.ipynb lint-ansible: poetry run poe lint-ansible lint-matlab: @@ -227,6 +228,7 @@ lint-yaml: poetry run poe lint-yaml static-type-check-python: poetry run poe static-type-check-python -- --package=api-python + poetry run poe static-type-check-python -- --package=aws.amazon-sagemaker.pytorch-mnist poetry run poe static-type-check-python -- --package=chatbot poetry run poe static-type-check-python -- --package=convolutional-neural-network poetry run poe static-type-check-python -- --package=data-distribution-service diff --git a/aws/amazon-emr/studio/hm-studio/hm-workspace.ipynb b/aws/amazon-emr/studio/hm-studio/hm-workspace.ipynb index 3017e5313e..0f30efb81c 100644 --- a/aws/amazon-emr/studio/hm-studio/hm-workspace.ipynb +++ b/aws/amazon-emr/studio/hm-studio/hm-workspace.ipynb @@ -52,14 +52,6 @@ "%%sql\n", "select * from hm_motor" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c562d57", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/aws/amazon-msk/connectors/hm-motor-jdbc-sink-connector/hm-motor-jdbc-sink-connector.json b/aws/amazon-msk/connectors/hm-motor-jdbc-sink-connector/hm-motor-jdbc-sink-connector.json index 61778e997a..dd766ec9bf 100644 --- a/aws/amazon-msk/connectors/hm-motor-jdbc-sink-connector/hm-motor-jdbc-sink-connector.json +++ b/aws/amazon-msk/connectors/hm-motor-jdbc-sink-connector/hm-motor-jdbc-sink-connector.json @@ -69,5 +69,5 @@ } } ], - "serviceExecutionRoleArn": "arn:aws:iam::xxx:role/xxx" + "serviceExecutionRoleArn": "arn:aws:iam::xxxxxxxxxxxx:role/xxx" } diff --git a/aws/amazon-sagemaker/Makefile b/aws/amazon-sagemaker/Makefile new file mode 100644 index 0000000000..eb70cbab3c --- /dev/null +++ b/aws/amazon-sagemaker/Makefile @@ -0,0 +1,7 @@ +sagemaker-create-notebook-instance: + aws sagemaker create-notebook-instance \ + --notebook-instance-name=hm-sagemaker-notebook \ + --instance-type=ml.m5.xlarge \ + --role-arn=arn:aws:iam::xxxxxxxxxxxx:role/service-role/xxx \ + --platform-identifier=notebook-al2-v2 \ + --root-access=Enabled diff --git a/aws/amazon-sagemaker/pytorch-mnist/Makefile b/aws/amazon-sagemaker/pytorch-mnist/Makefile new file mode 100644 index 0000000000..ed929dd1b0 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/Makefile @@ -0,0 +1,23 @@ +poetry-env-use: + poetry env use 3.10 +poetry-update-lock-file: + poetry lock --no-update +poetry-install: + poetry install --no-root +poetry-add: + poetry add xxx +poetry-add-dev: + poetry add xxx --group=dev + +poetry-run-dev: + poetry run poe dev + + +zip-pytorch-mnist: + cd .. && \ + zip -r pytorch-mnist.zip pytorch-mnist \ + -x 'pytorch-mnist/.venv/*' +# In SageMaker Noteebok instance's JupyterLab terminal +unzip-pytorch-mnist: + cd SageMaker/ + unzip pytorch-mnist.zip diff --git a/aws/amazon-sagemaker/pytorch-mnist/notebook.ipynb b/aws/amazon-sagemaker/pytorch-mnist/notebook.ipynb new file mode 100644 index 0000000000..36d40d0f7f --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/notebook.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.html\n", + "\n", + "# Set up\n", + "import sagemaker\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "aws_region = sagemaker_session.boto_region_name\n", + "s3_bucket = sagemaker_session.default_bucket()\n", + "s3_key_prefix = \"amazon-sagemaker/pytorch-mnist\"\n", + "iam_role_irn = sagemaker.get_execution_role()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the data\n", + "from torchvision.datasets import MNIST\n", + "from torchvision import transforms\n", + "\n", + "MNIST.mirrors = [\n", + " f\"https://sagemaker-example-files-prod-{aws_region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", + "]\n", + "MNIST(\n", + " \"data\",\n", + " download=True,\n", + " transform=transforms.Compose(\n", + " [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload the data to S3\n", + "data_s3_uri = sagemaker_session.upload_data(\n", + " path=\"data\", bucket=s3_bucket, key_prefix=s3_key_prefix\n", + ")\n", + "print(data_s3_uri)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train\n", + "from sagemaker.pytorch import PyTorch\n", + "\n", + "estimator = PyTorch(\n", + " source_dir=\"src/\",\n", + " entry_point=\"main.py\",\n", + " role=iam_role_irn,\n", + " py_version=\"py310\",\n", + " framework_version=\"2.0.0\",\n", + " instance_count=2,\n", + " instance_type=\"ml.c5.2xlarge\",\n", + " hyperparameters={\"epochs\": 1, \"backend\": \"gloo\"},\n", + ")\n", + "estimator.fit(\n", + " inputs={\"training\": data_s3_uri},\n", + " job_name=\"amazon-sagemaker-pytorch-mnist-job\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Deploy\n", + "predictor = estimator.deploy(\n", + " initial_instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " model_name=\"amazon-sagemaker-pytorch-mnist-model\",\n", + " endpoint_name=\"amazon-sagemaker-pytorch-mnist-endpoint\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate\n", + "import gzip\n", + "import numpy as np\n", + "import random\n", + "import os\n", + "\n", + "data_dir = \"data/MNIST/raw\"\n", + "with gzip.open(os.path.join(data_dir, \"t10k-images-idx3-ubyte.gz\"), \"rb\") as f:\n", + " images = (\n", + " np.frombuffer(f.read(), np.uint8, offset=16)\n", + " .reshape(-1, 28, 28)\n", + " .astype(np.float32)\n", + " )\n", + "\n", + "# Randomly select some of the test images\n", + "mask = random.sample(range(len(images)), 16)\n", + "mask = np.array(mask, dtype=np.int_)\n", + "data = images[mask]\n", + "\n", + "response = predictor.predict(np.expand_dims(data, axis=1))\n", + "print(\"Raw prediction result:\", response)\n", + "\n", + "labeled_predictions = list(zip(range(10), response[0]))\n", + "print(\"Labeled predictions:\", labeled_predictions)\n", + "\n", + "labeled_predictions.sort(key=lambda label_and_prob: 1.0 - label_and_prob[1])\n", + "print(\"Most likely answer:\", labeled_predictions[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cleanup\n", + "sagemaker_session.delete_endpoint(endpoint_name=predictor.endpoint_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (PyTorch 1.13 Python 3.9 CPU Optimized)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/pytorch-1.13-cpu-py39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2" + }, + "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/aws/amazon-sagemaker/pytorch-mnist/poetry.lock b/aws/amazon-sagemaker/pytorch-mnist/poetry.lock new file mode 100644 index 0000000000..8f6bacbd62 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/poetry.lock @@ -0,0 +1,1471 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "attrs" +version = "23.1.0" +description = "Classes Without Boilerplate" +optional = false +python-versions = ">=3.7" +files = [ + {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"}, + {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"}, +] + +[package.extras] +cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]", "pre-commit"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] +tests = ["attrs[tests-no-zope]", "zope-interface"] +tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] + +[[package]] +name = "boto3" +version = "1.28.25" +description = "The AWS SDK for Python" +optional = false +python-versions = ">= 3.7" +files = [ + {file = "boto3-1.28.25-py3-none-any.whl", hash = "sha256:f08f6c83608721c2142abd2ccc5f15bd5c98c282ad9e0d39f9efc59d98604658"}, + {file = "boto3-1.28.25.tar.gz", hash = "sha256:20feedb753e87d6dd55665e2e9dda08b031518291350c9c57b552c86a537fd4e"}, +] + +[package.dependencies] +botocore = ">=1.31.25,<1.32.0" +jmespath = ">=0.7.1,<2.0.0" +s3transfer = ">=0.6.0,<0.7.0" + +[package.extras] +crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] + +[[package]] +name = "botocore" +version = "1.31.25" +description = "Low-level, data-driven core of boto 3." +optional = false +python-versions = ">= 3.7" +files = [ + {file = "botocore-1.31.25-py3-none-any.whl", hash = "sha256:17cc6db84644251a5b519aeccd5eb1c313a18ef2e92616ec16182aa30c877152"}, + {file = "botocore-1.31.25.tar.gz", hash = "sha256:b8a40b0ca1e3c8290a4c0d473c8e1575d2e8b2ddc3c61dd8814c3976357cac84"}, +] + +[package.dependencies] +jmespath = ">=0.7.1,<2.0.0" +python-dateutil = ">=2.1,<3.0.0" +urllib3 = ">=1.25.4,<1.27" + +[package.extras] +crt = ["awscrt (==0.16.26)"] + +[[package]] +name = "certifi" +version = "2023.7.22" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"}, + {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.2.0" +description = "The Real First Universal Charset Detector. 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a/aws/amazon-sagemaker/pytorch-mnist/src/models/net.py b/aws/amazon-sagemaker/pytorch-mnist/src/models/net.py new file mode 100644 index 0000000000..6653cd5065 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/models/net.py @@ -0,0 +1,21 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class Net(nn.Module): + def __init__(self): + super(Net, self).__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=1) diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/average_gradients.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/average_gradients.py new file mode 100644 index 0000000000..1162d951ae --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/average_gradients.py @@ -0,0 +1,9 @@ +import torch.distributed as dist + + +def average_gradients(model): + # Gradient averaging. + size = float(dist.get_world_size()) + for param in model.parameters(): + dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM) + param.grad.data /= size diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_args.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_args.py new file mode 100644 index 0000000000..5789ed8da7 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_args.py @@ -0,0 +1,84 @@ +import argparse +import json +import os + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--batch-size", + type=int, + default=64, + help="input batch size for training (default: 64)", + ) + parser.add_argument( + "--test-batch-size", + type=int, + default=1000, + help="input batch size for testing (default: 1000)", + ) + parser.add_argument( + "--epochs", + type=int, + default=10, + help="number of epochs to train (default: 10)", + ) + parser.add_argument( + "--lr", + type=float, + default=0.01, + help="learning rate (default: 0.01)", + ) + parser.add_argument( + "--momentum", + type=float, + default=0.5, + help="SGD momentum (default: 0.5)", + ) + parser.add_argument( + "--seed", + type=int, + default=1, + help="random seed (default: 1)", + ) + parser.add_argument( + "--log-interval", + type=int, + default=100, + help="how many batches to wait before logging training status", + ) + parser.add_argument( + "--backend", + type=str, + default=None, + help="backend for distributed training (tcp, gloo on cpu and gloo, nccl on gpu)", + ) + + # SageMaker container environments + parser.add_argument( + "--hosts", + type=list, + default=json.loads(os.environ["SM_HOSTS"]), + ) + parser.add_argument( + "--current-host", + type=str, + default=os.environ["SM_CURRENT_HOST"], + ) + parser.add_argument( + "--model-dir", + type=str, + default=os.environ["SM_MODEL_DIR"], + ) + parser.add_argument( + "--data-dir", + type=str, + default=os.environ["SM_CHANNEL_TRAINING"], + ) + parser.add_argument( + "--num-gpus", + type=int, + default=os.environ["SM_NUM_GPUS"], + ) + + return parser.parse_args() diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_test_data_loader.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_test_data_loader.py new file mode 100644 index 0000000000..1491dc658e --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_test_data_loader.py @@ -0,0 +1,22 @@ +import logging + +import torch +import torch.utils.data +import torch.utils.data.distributed +from torchvision import datasets, transforms + + +def get_test_data_loader(test_batch_size, training_dir, **kwargs): + logging.info("Get test data loader") + return torch.utils.data.DataLoader( + datasets.MNIST( + training_dir, + train=False, + transform=transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] + ), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs + ) diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_train_data_loader.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_train_data_loader.py new file mode 100644 index 0000000000..48a59ca33a --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/get_train_data_loader.py @@ -0,0 +1,29 @@ +import logging + +import torch +import torch.utils.data +import torch.utils.data.distributed +from torchvision import datasets, transforms + + +def get_train_data_loader(batch_size, training_dir, is_distributed, **kwargs): + logging.info("Get train data loader") + dataset = datasets.MNIST( + training_dir, + train=True, + transform=transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] + ), + ) + train_sampler = ( + torch.utils.data.distributed.DistributedSampler(dataset) + if is_distributed + else None + ) + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=train_sampler is None, + sampler=train_sampler, + **kwargs + ) diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/save_model.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/save_model.py new file mode 100644 index 0000000000..6fee9bc472 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/save_model.py @@ -0,0 +1,12 @@ +import logging +import os + +import torch +import torch.utils.data +import torch.utils.data.distributed + + +def save_model(model, model_dir): + logging.info("Save the model.") + path = os.path.join(model_dir, "model.pth") + torch.save(model.cpu().state_dict(), path) diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/test.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/test.py new file mode 100644 index 0000000000..c9483ec7ab --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/test.py @@ -0,0 +1,31 @@ +import logging + +import torch +import torch.nn.functional as F +import torch.utils.data +import torch.utils.data.distributed + + +def test(model, test_loader, device): + 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, size_average=False).item() + # get the index of the max log-probability + pred = output.max(1, keepdim=True)[1] + correct += pred.eq(target.view_as(pred)).sum().item() + + test_loss /= len(test_loader.dataset) + logging.info( + "Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( + test_loss, + correct, + len(test_loader.dataset), + 100.0 * correct / len(test_loader.dataset), + ) + ) diff --git a/aws/amazon-sagemaker/pytorch-mnist/src/utils/train.py b/aws/amazon-sagemaker/pytorch-mnist/src/utils/train.py new file mode 100644 index 0000000000..0538c53d06 --- /dev/null +++ b/aws/amazon-sagemaker/pytorch-mnist/src/utils/train.py @@ -0,0 +1,104 @@ +import logging +import os + +import torch +import torch.distributed as dist +import torch.nn.functional as F +import torch.optim as optim +import torch.utils.data +import torch.utils.data.distributed +from models.net import Net +from utils.average_gradients import average_gradients +from utils.get_test_data_loader import get_test_data_loader +from utils.get_train_data_loader import get_train_data_loader +from utils.save_model import save_model +from utils.test import test + + +def train(args): + is_distributed = len(args.hosts) > 1 and args.backend is not None + logging.info("Distributed training:", is_distributed) + + use_cuda = args.num_gpus > 0 + logging.info("Number of gpus available:", args.num_gpus) + kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {} + device = torch.device("cuda" if use_cuda else "cpu") + + if is_distributed: + # Initialize the distributed environment. + world_size = len(args.hosts) + os.environ["WORLD_SIZE"] = str(world_size) + host_rank = args.hosts.index(args.current_host) + os.environ["RANK"] = str(host_rank) + dist.init_process_group( + backend=args.backend, rank=host_rank, world_size=world_size + ) + logging.info( + "Initialized the distributed environment: '{}' backend on {} nodes. ".format( + args.backend, dist.get_world_size() + ) + + "Current host rank is {}. Number of gpus: {}".format( + dist.get_rank(), args.num_gpus + ) + ) + + # set the seed for generating random numbers + torch.manual_seed(args.seed) + if use_cuda: + torch.cuda.manual_seed(args.seed) + + train_loader = get_train_data_loader( + args.batch_size, args.data_dir, is_distributed, **kwargs + ) + test_loader = get_test_data_loader(args.test_batch_size, args.data_dir, **kwargs) + + logging.info( + "Processes {}/{} ({:.0f}%) of train data".format( + len(train_loader.sampler), + len(train_loader.dataset), + 100.0 * len(train_loader.sampler) / len(train_loader.dataset), + ) + ) + + logging.info( + "Processes {}/{} ({:.0f}%) of test data".format( + len(test_loader.sampler), + len(test_loader.dataset), + 100.0 * len(test_loader.sampler) / len(test_loader.dataset), + ) + ) + + model = Net().to(device) + if is_distributed and use_cuda: + # multi-machine multi-gpu case + model = torch.nn.parallel.DistributedDataParallel(model) + else: + # single-machine multi-gpu case or single-machine or multi-machine cpu case + model = torch.nn.DataParallel(model) + + optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) + + for epoch in range(1, args.epochs + 1): + model.train() + for batch_idx, (data, target) in enumerate(train_loader, 1): + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = F.nll_loss(output, target) + loss.backward() + if is_distributed and not use_cuda: + # average gradients manually for a multi-machine cpu case only + average_gradients(model) + optimizer.step() + if batch_idx % args.log_interval == 0: + logging.info( + "Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}".format( + epoch, + batch_idx * len(data), + len(train_loader.sampler), + 100.0 * batch_idx / len(train_loader), + loss.item(), + ) + ) + test(model, test_loader, device) + save_model(model, args.model_dir)