Amazon SageMaker AI makes extensive use of Docker containers for build and runtime tasks. SageMaker AI provides pre-built Docker images for its built-in algorithms and the supported deep learning frameworks used for training and inference. Using containers, you can train machine learning algorithms and deploy models quickly and reliably at any scale. The topics in this section show how to deploy these containers for your own use cases. For information about how to bring your own containers for use with Amazon SageMaker Studio Classic, see Custom images.
Scenarios for Running Scripts, Training Algorithms, or Deploying Models with SageMaker AIAmazon SageMaker AI always uses Docker containers when running scripts, training algorithms, and deploying models. Your level of engagement with containers depends on your use case.
The following decision tree illustrates three main scenarios: Use cases for using pre-built Docker containers with SageMaker AI; Use cases for extending a pre-built Docker container; Use case for building your own container.
Use cases for using pre-built Docker containers with SageMaker AIConsider the following use cases when using containers with SageMaker AI:
Pre-built SageMaker AI algorithm â Use the image that comes with the built-in algorithm. See Use Amazon SageMaker AI Built-in Algorithms or Pre-trained Models for more information.
Custom model with pre-built SageMaker AI container â If you train or deploy a custom model, but use a framework that has a pre-built SageMaker AI container including TensorFlow and PyTorch, choose one of the following options:
If you don't need a custom package, and the container already includes all required packages: Use the pre-built Docker image associated with your framework. For more information, see Pre-built SageMaker AI Docker images.
If you need a custom package installed into one of the pre-built containers: Confirm that the pre-built Docker image allows a requirements.txt file, or extend the pre-built container based on the following use cases.
The following are use cases for extending a pre-built Docker container:
You can't import the dependencies â Extend the pre-built Docker image associated with your framework. See Extend a Pre-built Container for more information.
You can't import the dependencies in the pre-built container and the pre-built container supports requirements.txt â Add all the required dependencies in requirements.txt. The following frameworks support using requirements.txt.
If you build or train a custom model and require custom framework that does not have a pre-built image, build a custom container.
As an example use case of training and deploying a TensorFlow model, the following guide shows how to determine which option from the previous sections of Use cases fits to the case.
Assume that you have the following requirements for training and deploying a TensorFlow model.
After you determine the type of container that you need, the following list provides details about the previously listed options.
Use a built-in SageMaker AI algorithm or framework. For most use cases, you can use the built-in algorithms and frameworks without worrying about containers. You can train and deploy these algorithms from the SageMaker AI console, the AWS Command Line Interface (AWS CLI), a Python notebook, or the Amazon SageMaker Python SDK. You can do that by specifying the algorithm or framework version when creating your Estimator. The available built-in algorithms are itemized and described in the Built-in algorithms and pretrained models in Amazon SageMaker topic. For more information about the available frameworks, see ML Frameworks and Languages. For an example of how to train and deploy a built-in algorithm using a Jupyter notebook running in a SageMaker notebook instance, see the Guide to getting set up with Amazon SageMaker AI topic.
Use pre-built SageMaker AI container images. Alternatively, you can use the built-in algorithms and frameworks using Docker containers. SageMaker AI provides containers for its built-in algorithms and pre-built Docker images for some of the most common machine learning frameworks, such as Apache MXNet, TensorFlow, PyTorch, and Chainer. For a full list of the available SageMaker Images, see Available Deep Learning Containers Images. It also supports machine learning libraries such as scikit-learn and SparkML. If you use the Amazon SageMaker Python SDK, you can deploy the containers by passing the full container URI to their respective SageMaker SDK Estimator class. For the full list of deep learning frameworks that are currently supported by SageMaker AI, see Prebuilt SageMaker AI Docker images for deep learning. For information about the scikit-learn and SparkML pre-built container images, see Accessing Docker Images for Scikit-learn and Spark ML. For more information about using frameworks with the Amazon SageMaker Python SDK, see their respective topics in Machine Learning Frameworks and Languages.
Extend a pre-built SageMaker AI container image. If you would like to extend a pre-built SageMaker AI algorithm or model Docker image, you can modify the SageMaker image to satisfy your needs. For an example, see Extending our PyTorch containers.
Adapt an existing container image: If you would like to adapt a pre-existing container image to work with SageMaker AI, you must modify the Docker container to enable either the SageMaker Training or Inference toolkit. For an example that shows how to build your own containers to train and host an algorithm, see Bring Your Own R Algorithm.
The following are common errors that you might run into when using Docker containers with SageMaker AI. Each error is followed by a solution to the error.
Error: SageMaker AI has lost the Docker daemon.
To fix this error, restart Docker using the following command.
sudo service docker restart Error: The /tmp directory of your Docker container has run out of space.
Docker containers use the / and /tmp partitions to store code. These partitions can fill up easily when using large code modules in local mode. The SageMaker AI Python SDK supports specifying a custom temp directory for your local mode root directory to avoid this issue.
To specify the custom temp directory in the Amazon Elastic Block Store volume storage, create a file at the following path ~/.sagemaker/config.yaml and add the following configuration. The directory that you specify as container_root must already exist. The SageMaker AI Python SDK will not try to create it.
local:
container_root: /home/ec2-user/SageMaker/tempWith this configuration, local mode uses the /temp directory and not the default /tmp directory.
Low space errors on SageMaker notebook instances
A Docker container that runs on SageMaker notebook instances uses the root Amazon EBS volume of the notebook instance by default. To resolve low space errors, provide the path of the Amazon EBS volume attached to the notebook instance as part of the volume parameter of Docker commands.
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