      build_and_train_models/sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use SageMaker Distributed Model Parallel with Amazon SageMaker to Launch Training Job with Model Parallelization\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/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "SageMaker Distributed Model Parallel is a model parallelism library for training large deep learning models that were previously difficult to train due to GPU memory limitations. SageMaker Distributed Model Parallel automatically and efficiently splits a model across multiple GPUs and instances and coordinates model training, allowing you to increase prediction accuracy by creating larger models with more parameters.\n",
    "\n",
    "Use this notebook to configure Sagemaker Distributed Model Parallel to train a model using TensorFlow and [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/overview.html#train-a-model-with-the-sagemaker-python-sdk). \n",
    "\n",
    "\n",
    "### Additional Resources\n",
    "If you are a new user of Amazon SageMaker, you may find the following helpful to understand how SageMaker uses Docker to train custom models.\n",
    "* To learn more about using Amazon SageMaker with your own training image, see [Use Your Own Training Algorithms\n",
    "](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html).\n",
    "\n",
    "* To learn more about using Docker to train your own models with Amazon SageMaker, see [Example Notebooks: Use Your Own Algorithm or Model](https://docs.aws.amazon.com/sagemaker/latest/dg/adv-bring-own-examples.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amazon SageMaker Initialization\n",
    "\n",
    "Run the following cells to initialize the notebook instance. Get the SageMaker execution role used to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install sagemaker-experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install sagemaker --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.tensorflow import TensorFlow\n",
    "from smexperiments.experiment import Experiment\n",
    "from smexperiments.trial import Trial\n",
    "import boto3\n",
    "from time import gmtime, strftime\n",
    "from datetime import datetime\n",
    "\n",
    "role = (\n",
    "    get_execution_role()\n",
    ")  # provide a pre-existing role ARN as an alternative to creating a new role\n",
    "print(f\"SageMaker Execution Role:{role}\")\n",
    "\n",
    "session = boto3.session.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare your training script\n",
    "\n",
    "Run the following cells to view example-training scripts for TensorFlow versions 2.3. The `tf2.py` is pure model paralleism and `tf2_hvd.py` is data/model paralleism using Horovod. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to see an example of a training scripts that you can use to configure -\n",
    "# SageMaker Distributed Model Parallel with TensorFlow versions 2.3\n",
    "!cat utils/tf2.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to see an example of a training scripts that you can use to configure -\n",
    "# SageMaker Distributed Model Parallel using Horvod with TensorFlow 2.3\n",
    "!cat utils/tf2_hvd.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define SageMaker Training Job\n",
    "\n",
    "Next, you will use SageMaker Estimator API to define a SageMaker Training Job. You will use an [`Estimator`](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html) to define the number and type of EC2 instances Amazon SageMaker uses for training, as well as the size of the volume attached to those instances. \n",
    "\n",
    "You must update the following:\n",
    "* `processes_per_host`\n",
    "* `entry_point`\n",
    "* `instance_count`\n",
    "* `instance_type`\n",
    "* `base_job_name`\n",
    "\n",
    "In addition, you can supply and modify configuration parameters for the SageMaker Distributed Model Parallel library. These parameters will be passed in through the `distributions` argument, as shown below.\n",
    "\n",
    "### Update the Type and Number of EC2 Instances Used\n",
    "\n",
    "Pick your `entry_point` from one of the example scripts: `tf2.py`, `tf2_hvd.py`\n",
    "\n",
    "Specify your `processes_per_host`, for `tf2.py` only use 2, for `tf2_hvd.py` use at least 4. Note that it must be a multiple of your partitions, which by default is 2.\n",
    "\n",
    "The instance type and number of instances you specify in `instance_type` and `instance_count` respecitvely will determine the number of GPUs Amazon SageMaker uses during training. Explicitly, `instance_type` will determine the number of GPUs on a single instance and that number will be multiplied by `instance_count`. \n",
    "\n",
    "You must specify values for `instance_type` and `instance_count` so that the total number of GPUs available for training is equal to `partitions` in `parameters` of your model parallel distributions argument in the Estimator API\n",
    "\n",
    "If you use `tf2_hvd.py`, the total number of  model replicas your training job can support will be equal to the total number of GPUs you specify, divided by `partitions`. Therefore, if you use Horovod for data parallelization, specify the total number of GPUs to be the desired number of model replicas times `partitions`: `total-model-replicas` x `partitions`. \n",
    "\n",
    "To look up instances types, see [Amazon EC2 Instance Types](https://aws.amazon.com/sagemaker/pricing/).\n",
    "\n",
    "### Uploading Checkpoint During Training or Resuming Checkpoint from Previous Training\n",
    "We also provide a custom way for users to upload checkpoints during training or resume checkpoints from previous training. We have integrated this into our `tf2.py` example script. Please see the functions `aws_s3_sync`, `sync_local_checkpoints_to_s3`, and `sync_s3_checkpoints_to_local`. For the purpose of this example, we are only uploading a checkpoint during training, by using `sync_local_checkpoints_to_s3`. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you have updated `entry_point`, `instance_count`, `instance_type` and `base_job_name`, run the following to create an estimator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_session = sagemaker.session.Session(boto_session=session)\n",
    "mpioptions = \"-verbose -x orte_base_help_aggregate=0 \"\n",
    "\n",
    "# choose an experiment name (only need to create it once)\n",
    "experiment_name = \"SM-MP-DEMO-{}\".format(datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "all_experiment_names = [exp.experiment_name for exp in Experiment.list()]\n",
    "# Load the experiment if it exists, otherwise create\n",
    "if experiment_name not in all_experiment_names:\n",
    "    customer_churn_experiment = Experiment.create(\n",
    "        experiment_name=experiment_name, sagemaker_boto_client=boto3.client(\"sagemaker\")\n",
    "    )\n",
    "else:\n",
    "    customer_churn_experiment = Experiment.load(\n",
    "        experiment_name=experiment_name, sagemaker_boto_client=boto3.client(\"sagemaker\")\n",
    "    )\n",
    "\n",
    "# Create a trial for the current run\n",
    "trial = Trial.create(\n",
    "    trial_name=\"SMD-MP-demo-{}\".format(strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())),\n",
    "    experiment_name=customer_churn_experiment.experiment_name,\n",
    "    sagemaker_boto_client=boto3.client(\"sagemaker\"),\n",
    ")\n",
    "\n",
    "\n",
    "smd_mp_estimator = TensorFlow(\n",
    "    entry_point=\"tf2.py\",  # Pick your train script\n",
    "    source_dir=\"utils\",\n",
    "    role=role,\n",
    "    framework_version=\"2.3.1\",\n",
    "    py_version=\"py37\",\n",
    "    instance_type=\"ml.p3.16xlarge\",\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    instance_count=1,\n",
    "    distribution={\n",
    "        \"smdistributed\": {\n",
    "            \"modelparallel\": {\n",
    "                \"enabled\": True,\n",
    "                \"parameters\": {\n",
    "                    \"microbatches\": 2,\n",
    "                    \"partitions\": 2,\n",
    "                    \"pipeline\": \"interleaved\",\n",
    "                    \"optimize\": \"memory\",\n",
    "                    # \"horovod\": True, #Set to True if using the horovod script\n",
    "                },\n",
    "            }\n",
    "        },\n",
    "        \"mpi\": {\n",
    "            \"enabled\": True,\n",
    "            \"processes_per_host\": 2,  # Pick your processes_per_host\n",
    "            \"custom_mpi_options\": mpioptions,\n",
    "        },\n",
    "    },\n",
    "    base_job_name=\"SMD-MP-demo\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you will use the estimator to launch the SageMaker training job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "smd_mp_estimator.fit(\n",
    "    experiment_config={\n",
    "        \"ExperimentName\": customer_churn_experiment.experiment_name,\n",
    "        \"TrialName\": trial.trial_name,\n",
    "        \"TrialComponentDisplayName\": \"Training\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "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/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This us-east-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-east-2/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This us-west-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-west-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ca-central-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/ca-central-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This sa-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/sa-east-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This eu-west-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/eu-west-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This eu-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/eu-west-2/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This eu-west-3 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/eu-west-3/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This eu-central-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/eu-central-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This eu-north-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/eu-north-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ap-southeast-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/ap-southeast-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ap-southeast-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/ap-southeast-2/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ap-northeast-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/ap-northeast-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ap-northeast-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/ap-northeast-2/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n",
    "\n",
    "![This ap-south-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/ap-south-1/build_and_train_models|sm-distributed_model_parallel|sm-distributed_model_parallel.ipynb)\n"
   ]
  }
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