# Run a SageMaker Distributed Training Job with Model Parallelism
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Learn how to run a model-parallel training job of your own training script using the SageMaker Python SDK with the SageMaker model parallelism library.

There are three use-case scenarios for running a SageMaker training job.

1. You can use one of the pre-built AWS Deep Learning Container for TensorFlow and PyTorch. This option is recommended if it is the first time for you to use the model parallel library. To find a tutorial for how to run a SageMaker model parallel training job, see the example notebooks at [PyTorch training with Amazon SageMaker AI's model parallelism library](https://github.com/aws/amazon-sagemaker-examples/tree/main/training/distributed_training/pytorch/model_parallel).

1. You can extend the pre-built containers to handle any additional functional requirements for your algorithm or model that the pre-built SageMaker Docker image doesn't support. To find an example of how you can extend a pre-built container, see [Extend a Pre-built Container](prebuilt-containers-extend.md).

1. You can adapt your own Docker container to work with SageMaker AI using the [SageMaker Training toolkit](https://github.com/aws/sagemaker-training-toolkit). For an example, see [Adapting Your Own Training Container](https://docs.aws.amazon.com/sagemaker/latest/dg/adapt-training-container.html).

For options 2 and 3 in the preceding list, refer to [Extend a Pre-built Docker Container that Contains SageMaker's Distributed Model Parallel Library](model-parallel-sm-sdk.md#model-parallel-customize-container) to learn how to install the model parallel library in an extended or customized Docker container. 

In all cases, you launch your training job configuring a SageMaker `TensorFlow` or `PyTorch` estimator to activate the library. To learn more, see the following topics.

**Topics**
+ [Step 1: Modify Your Own Training Script Using SageMaker's Distributed Model Parallel Library](model-parallel-customize-training-script.md)
+ [Step 2: Launch a Training Job Using the SageMaker Python SDK](model-parallel-sm-sdk.md)