# Supported frameworks and Amazon Regions
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Before using SageMaker smart sifting data loader, check if your framework of choice is supported, that the instance types are available in your Amazon account, and that your Amazon account is in one of the supported Amazon Regions.

**Note**  
SageMaker smart sifting supports PyTorch model training with traditional data parallelism and distributed data parallelism, which makes model replicas in all GPU workers and uses the `AllReduce` operation. It doesn’t work with model parallelism techniques, including sharded data parallelism. Because SageMaker smart sifting works for data parallelism jobs, make sure that the model you train fits in each GPU memory.

## Supported Frameworks
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SageMaker smart sifting supports the following deep learning frameworks and is available through Amazon Deep Learning Containers.

**Topics**
+ [PyTorch](#train-smart-sifting-supported-frameworks-pytorch)

### PyTorch
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| Framework | Framework version | Deep Learning Container URI | 
| --- | --- | --- | 
| PyTorch | 2.1.0 | {{763104351884}}.dkr.ecr.{{region}}.amazonaws.com/pytorch-training:2.1.0-gpu-py310-cu121-ubuntu20.04-sagemaker | 

For more information about the pre-built containers, see [SageMaker AI Framework Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) in the *Amazon Deep Learning Containers GitHub repository*.

## Amazon Web Services Regions
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The [containers packaged with the SageMaker smart sifting library](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-training-compiler-containers) are available in the Amazon Web Services Regions where [Amazon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) are in service.

## Instance types
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You can use SageMaker smart sifting for any PyTorch training jobs on any instance types. We recommend that you use P4d, P4de, or P5 instances.