@@ -16,22 +16,14 @@ limitations under the License.
# Run training on Amazon SageMaker
Hugging Face and Amazon are introducing new [Hugging Face Deep Learning Containers (DLCs)](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) to make it easier than ever to train Hugging Face Transformer models in [Amazon SageMaker](https://aws.amazon.com/sagemaker/).
Hugging Face and Amazon are introducing new [Hugging Face Deep Learning Containers (DLCs)](#deep-learning-container-dlc-overview) to make it easier than ever to train Hugging Face Transformer models in [Amazon SageMaker](https://aws.amazon.com/sagemaker/).
You can find a full list of all available [Hugging Face Deep Learning Containers](#deep-learning-container-dlc-overview) at the end of this page.
To learn how to access and use the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below.
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## Deep Learning Container (DLC) overview
The Deep Learning Container are in every available where Amazon SageMaker is available. You can see the [AWS region table](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) for all AWS global infrastructure. To get an detailed overview of all included packages look [here in the release notes](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html).
| 🤗 Transformers version | 🤗 Datasets version | PyTorch/TensorFlow version | type | device | Python Version | Example `image_uri` |
@@ -194,8 +186,8 @@ You can find here a list of the official notebooks provided by Hugging Face.
| [Spot Instances and continues training](https://github.com/huggingface/notebooks/blob/master/sagemaker/05_spot_instances/sagemaker-notebook.ipynb) | End-to-End to Text-Classification example using spot instances with continued training. |
| [SageMaker Metrics](https://github.com/huggingface/notebooks/blob/master/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb) | End-to-End to Text-Classification example using SageMaker Metrics to extract and log metrics during training |
| [Distributed Training Data Parallelism Tensorflow](https://github.com/huggingface/notebooks/blob/master/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb) | End-to-End distributed binary Text-Classification example using `Keras` and `TensorFlow`
| [Distributed Seq2Seq Training with Data Parallelism and BART](https://github.com/huggingface/notebooks/blob/master/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb) | End-to-End distributed summarization example `BART-large` and 🤗 Transformers example script for `summarization` |
| [Distributed Seq2Seq Training with Data Parallelism and BART](https://github.com/huggingface/notebooks/blob/master/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb) | End-to-End distributed summarization example with `BART-large` and 🤗 Transformers example script for `summarization` |
| [Image Classification using Vision Transformer](https://github.com/huggingface/notebooks/blob/master/sagemaker/09_image_classification_vision_transformer/sagemaker-notebook.ipynb) | End-to-End image classification example with `Vision Transformers` |
The Deep Learning Container are in every available where Amazon SageMaker is available. You can see the [AWS region table](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) for all AWS global infrastructure. To get an detailed overview of all included packages look [here in the release notes](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html).
| 🤗 Transformers version | 🤗 Datasets version | PyTorch/TensorFlow version | type | device | Python Version | Example `image_uri` |
