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Author: nibaccam

articles/machine-learning/concept-distributed-training.md

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+---
+title: Distributed training methods
+titleSuffix: Azure Machine Learning
+description:
+services: machine-learning
+ms.service: machine-learning
+author: nibaccam
+ms.author: nibaccam
+ms.subservice: core
+ms.topic: conceptual
+ms.date: 03/23/2020
+---
+
+# Distributed training with Azure Machine Learning
+
+What is distributed training
+
+
+Distributed training for machine learning models refers to the ability to scale trainign and share the load across multiple GPU or CPU nodes in parallel in order to facilitate and accelerate the training of machine learning models. 
+Azure Machine learning is integrated with two common open source frameworks that support distributed training, PyTorch and Tensorflow. 
+
+multiple GPUs on one machine
+
+The different types of distributed
+
+If you are looking to simply train your model without leveraging distributed training, see [Azure Machine Learning SDK for Python](#python-sdk) for the different ways to train models. 
+
+When to use distributed training
+
+Use distributed training when your data strains your single machine compute power. 
+If your data is too large to store in your local RAM 
+If your data loading becomes time consuming and cumbersome
+
+
+What does Azure Machine Learning support
+Azure Machine Learning offers estimators with PyTorch and Tensorflow which support distributed training
+
+how-to-train-tensorflow.md#distributed-training
+how-to-train-pytorch.md#distributed-training
+
+## Next steps
+
+Learn how to [Set up training environments](how-to-set-up-training-targets.md).