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Merge pull request #215312 from sdgilley/sdg-content-freshness
Update distributed training metadata
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articles/machine-learning/concept-distributed-training.md

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description: Learn what type of distributed training Azure Machine Learning supports and the open source framework integrations available for distributed training.
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services: machine-learning
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ms.service: machine-learning
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author: manashgoswami
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ms.author: magoswam
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author: rtanase
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ms.author: ratanase
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ms.reviewer: sgilley
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ms.subservice: core
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ms.topic: conceptual

articles/machine-learning/how-to-train-distributed-gpu.md

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title: Distributed GPU training guide
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titleSuffix: Azure Machine Learning
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description: Learn the best practices for performing distributed training with Azure Machine Learning supported frameworks, such as MPI, Horovod, DeepSpeed, PyTorch, PyTorch Lightning, Hugging Face Transformers, TensorFlow, and InfiniBand.
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author: fuhuifang
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ms.author: fufang
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author: rtanase
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ms.author: ratanase
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ms.reviewer: sgilley
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ms.service: machine-learning
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ms.subservice: core

articles/machine-learning/v1/how-to-train-distributed-gpu.md

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title: Distributed GPU training guide (SDK v1)
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titleSuffix: Azure Machine Learning
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description: Learn the best practices for performing distributed training with Azure Machine Learning SDK (v1) supported frameworks, such as MPI, Horovod, DeepSpeed, PyTorch, PyTorch Lightning, Hugging Face Transformers, TensorFlow, and InfiniBand.
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author: fuhuifang
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ms.author: fufang
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author: rtanase
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ms.author: ratanase
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ms.reviewer: sgilley
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ms.service: machine-learning
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ms.subservice: core
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* Your Azure ML environment contains DeepSpeed and its dependencies, Open MPI, and mpi4py.
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* Create an `MpiConfiguration` with your distribution.
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### DeepSeed example
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### DeepSpeed example
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* [azureml-examples: Distributed training with DeepSpeed on CIFAR-10](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/workflows/train/deepspeed/cifar)
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