# Distributed training with the SageMaker AI distributed data parallelism library
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The SageMaker AI distributed data parallelism (SMDDP) library is designed for ease of use and to provide seamless integration with PyTorch.

When training a deep learning model with the SMDDP library on SageMaker AI, you can focus on writing your training script and model training. 

To get started, import the SMDDP library to use its collective operations optimized for AWS. The following topics provide instructions on what to add to your training script depending on which collective operation you want to optimize.

**Topics**
+ [Adapting your training script to use the SMDDP collective operations](data-parallel-modify-sdp-select-framework.md)
+ [Launching distributed training jobs with SMDDP using the SageMaker Python SDK](data-parallel-use-api.md)

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon SageMaker. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query sagemaker` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).