training/heterogeneous-clusters/hello.world.sagemaker/helloworld-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heterogeneous Cluster - a hello world training job\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/training|heterogeneous-clusters|hello.world.sagemaker|helloworld-example.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This basic example on how to run a Heterogeneous Clusters training job consisting of two instance groups. Each instance group includes a different instance type.  Each instance prints its environment information including its instance group and exits.\n",
    "\n",
    "You can retrieve environment information in either of the following ways:\n",
    "  - **Option 1**: Read instance group information using the convenient `sagemaker_training.environment.Environment` class.\n",
    "  - **Option 2**: Read instance group information from `/opt/ml/input/config/resourceconfig.json`.\n",
    " \n",
    " \n",
    "Note: This notebook does not demonstrate offloading of data preprocessing job to data group and deep neural network training to dnn_group. We will cover those examples in [TensorFlow's tf.data.service based Amazon SageMaker Heterogeneous Clusters for training](../tf.data.service.sagemaker/hetero-tensorflow-restnet50.ipynb) and [PyTorch and gRPC distributed dataloader based Amazon SageMaker Heterogeneous Clusters for training](../pt.grpc.sagemaker/hetero-pytorch-mnist.ipynb) notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Setting up SageMaker Studio notebook\n",
    "#### Before you start\n",
    "Ensure you have selected Python 3 (_TensorFlow 2.6 Python 3.8 CPU Optimized_) image for your SageMaker Studio Notebook instance, and running on _ml.t3.medium_ instance type.\n",
    "\n",
    "#### Step 1 - Upgrade SageMaker SDK and dependent packages\n",
    "Heterogeneous Clusters for Amazon SageMaker model training was [announced](https://aws.amazon.com/about-aws/whats-new/2022/07/announcing-heterogeneous-clusters-amazon-sagemaker-model-training) on 07/08/2022. This feature release requires you to have updated SageMaker SDK and boto3 client libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "python3 -m pip install --upgrade boto3 botocore awscli sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2 - Restart the notebook kernel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import IPython\n",
    "# IPython.Application.instance().kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3 - Validate SageMaker Python SDK and TensorFlow versions\n",
    "Ensure the output of the cell below reflects:\n",
    "\n",
    "- SageMaker Python SDK version 2.98.0 or above, \n",
    "- boto3 1.24 or above \n",
    "- botocore 1.27 or above \n",
    "- TensorFlow 2.6 or above "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip show sagemaker boto3 botocore tensorflow protobuf |egrep 'Name|Version|---'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Run a heterogeneous cluster training job\n",
    "\n",
    "#### Step 1: Set up training environment\n",
    "Import the required libraries that enable you to use Heterogeneous clusters for training. In this step, you are also inheriting this notebook's IAM role and SageMaker session. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import datetime\n",
    "\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.tensorflow import TensorFlow\n",
    "from sagemaker.instance_group import InstanceGroup\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Define instance groups \n",
    "Here we define instance groups. Each instance group includes a different instance type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_group = InstanceGroup(\"data_group\", \"ml.c5.xlarge\", 1)\n",
    "dnn_group = InstanceGroup(\"dnn_group\", \"ml.m4.xlarge\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Review the \"hello world\" training code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pygmentize source_dir/train.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Configure the Estimator\n",
    "In order to use SageMaker to fit our algorithm, we'll create an `Estimator` that defines how to use the container to train. This includes the configuration we need to invoke SageMaker training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "estimator = TensorFlow(\n",
    "    entry_point=\"train.py\",\n",
    "    source_dir=\"./source_dir\",\n",
    "    # instance_type='ml.m4.xlarge',\n",
    "    # instance_count=1,\n",
    "    instance_groups=[\n",
    "        data_group,\n",
    "        dnn_group,\n",
    "    ],\n",
    "    framework_version=\"2.9\",\n",
    "    py_version=\"py39\",\n",
    "    role=role,\n",
    "    volume_size=10,\n",
    "    max_run=3600,\n",
    "    disable_profiler=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Submit the training job\n",
    "Here you are submitting the heterogeneous cluster training job. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "estimator.fit(\n",
    "    job_name=\"hello-world-heterogenous\"\n",
    "    + \"-\"\n",
    "    + datetime.datetime.utcnow().strftime(\"%Y%m%dT%H%M%SZ\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Review the logs for environment information\n",
    "\n",
    "Wait for the training job to finish, and review its logs in the AWS Console (click on **View logs** from the **Training Jobs** node in **Amazon SageMaker Console**)  You'll find two logs: Algo1, Algo2. Examine the printouts on each node on how to retrieve instance group environment information. An example is shown here:\n",
    "\n",
    "```\n",
    "Option-1: Read instance group information from the sagemaker_training.environment.Environment class\n",
    "env.is_hetero: True\n",
    "env.current_host: algo-1\n",
    "env.current_instance_type: ml.c5.xlarge\n",
    "env.current_instance_group: data_group\n",
    "env.current_instance_group_hosts: ['algo-1']\n",
    "env.instance_groups: ['data_group', 'dnn_group']\n",
    "\n",
    "Option-2: Read instance group information from {file_path}.            You'll need to parse the json yourself. This doesn't require an additional library.\n",
    "/opt/ml/input/config/resourceconfig.json dump = {\n",
    "    \"current_group_name\": \"data_group\",\n",
    "    \"current_host\": \"algo-1\",\n",
    "    \"current_instance_type\": \"ml.c5.xlarge\",\n",
    "    \"hosts\": [\n",
    "        \"algo-1\",\n",
    "        \"algo-2\"\n",
    "    ],\n",
    "    \"instance_groups\": [\n",
    "        {\n",
    "            \"hosts\": [\n",
    "                \"algo-1\"\n",
    "            ],\n",
    "            \"instance_group_name\": \"data_group\",\n",
    "            \"instance_type\": \"ml.c5.xlarge\"\n",
    "        },\n",
    "        {\n",
    "            \"hosts\": [\n",
    "                \"algo-2\"\n",
    "            ],\n",
    "            \"instance_group_name\": \"dnn_group\",\n",
    "            \"instance_type\": \"ml.m4.xlarge\"\n",
    "        }\n",
    "    ],\n",
    "    \"network_interface_name\": \"eth0\"\n",
    "}\n",
    "env.is_hetero: True\n",
    "current_host=algo-1\n",
    "current_instance_type=ml.c5.xlarge\n",
    "env.current_instance_group: data_group\n",
    "env.current_instance_group_hosts: TODO\n",
    "env.instance_groups: TODO\n",
    "env.instance_groups_dict: [{'instance_group_name': 'data_group', 'instance_type': 'ml.c5.xlarge', 'hosts': ['algo-1']}, {'instance_group_name': 'dnn_group', 'instance_type': 'ml.m4.xlarge', 'hosts': ['algo-2']}]\n",
    "env.distribution_hosts: TODO\n",
    "env.distribution_instance_groups: TODO\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Next steps\n",
    "\n",
    "In this notebook, we demonstrated how to retrieve the environment information, and differentiate which instance group an instance belongs to. Based on this, you can build logic to offload data processing tasks in your training job to a dedicated instance group. To understand how that can be done with a real-world example, we suggest going through the following notebook examples:  \n",
    "\n",
    "- [TensorFlow's tf.data.service based Amazon SageMaker Heterogeneous Clusters for training](../tf.data.service.sagemaker/hetero-tensorflow-restnet50.ipynb)\n",
    "- [PyTorch and gRPC distributed dataloader based Amazon SageMaker Heterogeneous Clusters for training](../pt.grpc.sagemaker/hetero-pytorch-mnist.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/training|heterogeneous-clusters|hello.world.sagemaker|helloworld-example.ipynb)\n",
    "\n",
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    "\n",
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   ]
  }
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