# Supported Frameworks and AWS Regions
<a name="distributed-model-parallel-support"></a>

Before using the SageMaker model parallelism library, check the supported frameworks and instance types, and determine if there are enough quotas in your AWS account and AWS Region.

**Note**  
To check the latest updates and release notes of the library, see the [SageMaker Model Parallel Release Notes](https://sagemaker.readthedocs.io/en/v2.199.0/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.html) in the *SageMaker Python SDK documentation*.

## Supported Frameworks
<a name="distributed-model-parallel-supported-frameworks"></a>

The SageMaker model parallelism library supports the following deep learning frameworks and is available in AWS Deep Learning Containers (DLC) or downloadable as a binary file.

PyTorch versions supported by SageMaker AI and the SageMaker model parallelism library


| PyTorch version | SageMaker model parallelism library version | `smdistributed-modelparallel` integrated DLC image URI | URL of the binary file\$1\$1 | 
| --- | --- | --- | --- | 
| v2.0.0 | smdistributed-modelparallel==v1.15.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker`  | https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-2.0.0/build-artifacts/2023-04-14-20-14/smdistributed\$1modelparallel-1.15.0-cp310-cp310-linux\$1x86\$164.whl | 
| v1.13.1 | smdistributed-modelparallel==v1.15.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker`  | https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.13.1/build-artifacts/2023-04-17-15-49/smdistributed\$1modelparallel-1.15.0-cp39-cp39-linux\$1x86\$164.whl | 
| v1.12.1 | smdistributed-modelparallel==v1.13.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.1-gpu-py38-cu113-ubuntu20.04-sagemaker`  | https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.1/build-artifacts/2022-12-08-21-34/smdistributed\$1modelparallel-1.13.0-cp38-cp38-linux\$1x86\$164.whl | 
| v1.12.0 | smdistributed-modelparallel==v1.11.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu113-ubuntu20.04-sagemaker`   | https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.0/build-artifacts/2022-08-12-16-58/smdistributed\$1modelparallel-1.11.0-cp38-cp38-linux\$1x86\$164.whl | 
| v1.11.0 | smdistributed-modelparallel==v1.10.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.11.0-gpu-py38-cu113-ubuntu20.04-sagemaker`  | https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.11.0/build-artifacts/2022-07-11-19-23/smdistributed\$1modelparallel-1.10.0-cp38-cp38-linux\$1x86\$164.whl | 
| v1.10.2 |  smdistributed-modelparallel==v1.7.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.10.2-gpu-py38-cu113-ubuntu20.04-sagemaker`  | - | 
| v1.10.0 |  smdistributed-modelparallel==v1.5.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.10.0-gpu-py38-cu113-ubuntu20.04-sagemaker`  | - | 
| v1.9.1 |  smdistributed-modelparallel==v1.4.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.9.1-gpu-py38-cu111-ubuntu20.04`  | - | 
| v1.8.1\$1 |  smdistributed-modelparallel==v1.6.0 |  `763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.8.1-gpu-py36-cu111-ubuntu18.04`  | - | 

**Note**  
The SageMaker model parallelism library v1.6.0 and later provides extended features for PyTorch. For more information, see [Core Features of the SageMaker Model Parallelism Library](model-parallel-core-features.md).

\$1\$1 The URLs of the binary files are for installing the SageMaker model parallelism library in custom containers. For more information, see [Create Your Own Docker Container with the SageMaker Distributed Model Parallel Library](model-parallel-sm-sdk.md#model-parallel-bring-your-own-container).

TensorFlow versions supported by SageMaker AI and the SageMaker model parallelism library


| TensorFlow version | SageMaker model parallelism library version | `smdistributed-modelparallel` integrated DLC image URI | 
| --- | --- | --- | 
| v2.6.0 | smdistributed-modelparallel==v1.4.0 | 763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.6.0-gpu-py38-cu112-ubuntu20.04 | 
| v2.5.1 | smdistributed-modelparallel==v1.4.0  | 763104351884.dkr.ecr.<region>.amazonaws.com/tensorflow-training:2.5.1-gpu-py37-cu112-ubuntu18.04  | 

**Hugging Face Transformers versions supported by SageMaker AI and the SageMaker distributed data parallel library**

The AWS Deep Learning Containers for Hugging Face use the SageMaker Training Containers for PyTorch and TensorFlow as their base images. To look up the Hugging Face Transformers library versions and paired PyTorch and TensorFlow versions, see the latest [Hugging Face Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) and the [Prior Hugging Face Container Versions](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#prior-hugging-face-container-versions).

## AWS Regions
<a name="distributed-model-parallel-availablity-zone"></a>

The SageMaker data parallel library is available in all of the AWS Regions where the [AWS Deep Learning Containers for SageMaker](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sagemaker-framework-containers-sm-support-only) are in service. For more information, see [Available Deep Learning Containers Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images).

## Supported Instance Types
<a name="distributed-model-parallel-supported-instance-types"></a>

The SageMaker model parallelism library requires one of the following ML instance types.


| Instance type | 
| --- | 
| ml.g4dn.12xlarge | 
| ml.p3.16xlarge | 
| ml.p3dn.24xlarge  | 
| ml.p4d.24xlarge | 
| ml.p4de.24xlarge | 

For specs of the instance types, see the **Accelerated Computing** section in the [Amazon EC2 Instance Types page](https://aws.amazon.com/ec2/instance-types/). For information about instance pricing, see [Amazon SageMaker AI Pricing](https://aws.amazon.com/sagemaker/pricing/).

If you encountered an error message similar to the following, follow the instructions at [Request a service quota increase for SageMaker AI resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure).

```
ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling
    the CreateTrainingJob operation: The account-level service limit 'ml.p3dn.24xlarge
    for training job usage' is 0 Instances, with current utilization of 0 Instances
    and a request delta of 1 Instances.
    Please contact AWS support to request an increase for this limit.
```