feat(sagemaker): add pytorch mnist (#10354)

.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

.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

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

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": {

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"
 }

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

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

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
+}