Using Reinforcement Learning with the SageMaker Python SDK ¶

Contents

Using Reinforcement Learning with the SageMaker Python SDK

With Reinforcement Learning (RL) Estimators, you can train reinforcement learning models on Amazon SageMaker.

For supported RL toolkits and their versions, see https://github.com/aws/sagemaker-rl-container/#rl-images-provided-by-sagemaker

RL Training ¶

Training RL models using RLEstimator is a two-step process:

Prepare a training script to run on SageMaker
Run this script on SageMaker via an RLEstimator.

You should prepare your script in a separate source file than the notebook, terminal session, or source file you’re using to submit the script to SageMaker via an RLEstimator. This will be discussed in further detail below.

Suppose that you already have a training script called coach-train.py. You can then create an RLEstimator with keyword arguments to point to this script and define how SageMaker runs it:

from sagemaker.rl import RLEstimator, RLToolkit, RLFramework

rl_estimator = RLEstimator(entry_point='coach-train.py',
                           toolkit=RLToolkit.COACH,
                           toolkit_version='0.11.1',
                           framework=RLFramework.TENSORFLOW,
                           role='SageMakerRole',
                           instance_type='ml.p3.2xlarge',
                           instance_count=1)

After that, you simply tell the estimator to start a training job:

rl_estimator.fit()

In the following sections, we’ll discuss how to prepare a training script for execution on SageMaker and how to run that script on SageMaker using RLEstimator.

Preparing the RL Training Script ¶

Your RL training script must be a Python 3.5 compatible source file from MXNet framework or Python 3.6 for TensorFlow.

The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, such as

SM_MODEL_DIR: A string representing the path to the directory to write model artifacts to. These artifacts are uploaded to S3 for model hosting.
SM_NUM_GPUS: An integer representing the number of GPUs available to the host.
SM_OUTPUT_DATA_DIR: A string representing the filesystem path to write output artifacts to. Output artifacts may include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed and uploaded to S3 to the same S3 prefix as the model artifacts.

For the exhaustive list of available environment variables, see the SageMaker Containers documentation.

RL Estimators ¶

The RLEstimator constructor takes both required and optional arguments.

Required arguments ¶

The following are required arguments to the RLEstimator constructor. When you create an instance of RLEstimator, you must include these in the constructor, either positionally or as keyword arguments.

entry_point Path (absolute or relative) to the Python file which should be executed as the entry point to training.
role An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if accessing AWS resource.
instance_count Number of Amazon EC2 instances to use for training.
instance_type Type of EC2 instance to use for training, for example, ‘ml.m4.xlarge’.

You must as well include either:

toolkit RL toolkit (Ray RLlib or Coach) you want to use for executing your model training code.
toolkit_version RL toolkit version you want to be use for executing your model training code.
framework Framework (MXNet or TensorFlow) you want to be used as a toolkit backed for reinforcement learning training.

or provide:

image_uri An alternative Docker image to use for training and serving. If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. Refer to: SageMaker RL Docker Containers for details on what the Official images support and where to find the source code to build your custom image.

Optional arguments ¶

When you create an RLEstimator object, you can specify a number of optional arguments. For more information, see sagemaker.rl.estimator.RLEstimator.

Calling fit ¶

You start your training script by calling fit on an RLEstimator. For more information about what arguments can be passed to fit, see sagemaker.estimator.EstimatorBase.fit().

Distributed RL Training ¶

Amazon SageMaker RL supports multi-core and multi-instance distributed training. Depending on your use case, training and/or environment rollout can be distributed.

Please see the Amazon SageMaker examples on how it can be done using different RL toolkits.

Saving models ¶

In order to save your trained PyTorch model for deployment on SageMaker, your training script should save your model to a certain filesystem path /opt/ml/model. This value is also accessible through the environment variable SM_MODEL_DIR.

Deploying RL Models ¶

After an RL Estimator has been fit, you can host the newly created model in SageMaker.

After calling fit, you can call deploy on an RLEstimator Estimator to create a SageMaker Endpoint. The Endpoint runs one of the SageMaker-provided model server based on the framework parameter specified in the RLEstimator constructor and hosts the model produced by your training script, which was run when you called fit. This was the model you saved to model_dir. In case if image_uri was specified it would use provided image for the deployment.

deploy returns a sagemaker.mxnet.MXNetPredictor for MXNet or sagemaker.tensorflow.TensorFlowPredictor for TensorFlow.

predict returns the result of inference against your model.

# Train my estimator
rl_estimator = RLEstimator(entry_point='coach-train.py',
                           toolkit=RLToolkit.COACH,
                           toolkit_version='0.11.0',
                           framework=RLFramework.MXNET,
                           role='SageMakerRole',
                           instance_type='ml.c4.2xlarge',
                           instance_count=1)

rl_estimator.fit()

# Deploy my estimator to a SageMaker Endpoint and get a MXNetPredictor
predictor = rl_estimator.deploy(instance_type='ml.m4.xlarge',
                                initial_instance_count=1)

response = predictor.predict(data)

For more information please see The SageMaker MXNet Model Server and Deploying to TensorFlow Serving Endpoints documentation.

Working with Existing Training Jobs ¶

Attaching to existing training jobs ¶

You can attach an RL Estimator to an existing training job using the attach method.

my_training_job_name = 'MyAwesomeRLTrainingJob'
rl_estimator = RLEstimator.attach(my_training_job_name)

After attaching, if the training job has finished with job status “Completed”, it can be deployed to create a SageMaker Endpoint and return a Predictor. If the training job is in progress, attach will block and display log messages from the training job, until the training job completes.

The attach method accepts the following arguments:

training_job_name: The name of the training job to attach to.
sagemaker_session: The Session used to interact with SageMaker

RL Training Examples ¶

Amazon provides several example Jupyter notebooks that demonstrate end-to-end training on Amazon SageMaker using RL. Please refer to:

https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning

These are also available in SageMaker Notebook Instance hosted Jupyter notebooks under the sample notebooks folder.

SageMaker RL Docker Containers ¶

For more about the Docker images themselves, visit the SageMaker RL containers repository.