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articles/machine-learning/service/how-to-set-up-training-targets.md

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---
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title: Create and use compute targets for model training
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title: Use compute targets for model training
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titleSuffix: Azure Machine Learning
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description: Configure the training environments (compute targets) for machine learning model training. You can easily switch between training environments. Start training locally. If you need to scale out, switch to a cloud-based compute target.
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services: machine-learning
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ms.service: machine-learning
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ms.subservice: core
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ms.topic: conceptual
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ms.date: 10/25/2019
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ms.date: 11/21/2019
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ms.custom: seodec18
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---
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# Set up and use compute targets for model training
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>[!NOTE]
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> Code in this article was tested with Azure Machine Learning SDK version 1.0.39.
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> Code in this article was tested with Azure Machine Learning SDK version 1.0.74.
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## Compute targets for training
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You can create Azure Machine Learning Compute as a compute target at run time. The compute is automatically created for your run. The compute is deleted automatically once the run completes.
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> [!NOTE]
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> To specify the max number of nodes to use, you would normally set `node_count` to the number of nodes. There is currently (04/04/2019) a bug that prevents this from working. As a workaround, use the `amlcompute._cluster_max_node_count` property of the run configuration. For example, `run_config.amlcompute._cluster_max_node_count = 5`.
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> [!IMPORTANT]
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> Run-based creation of Azure Machine Learning compute is currently in Preview. Don't use run-based creation if you use automated hyperparameter tuning or automated machine learning. To use hyperparameter tuning or automated machine learning, create a [persistent compute](#persistent) target instead.
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