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Copy file name to clipboardExpand all lines: articles/machine-learning/how-to-train-scikit-learn.md
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@@ -8,7 +8,7 @@ ms.subservice: training
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ms.author: sgilley
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author: sdgilley
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ms.reviewer: balapv
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ms.date: 03/26/2024
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ms.date: 03/06/2025
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ms.topic: how-to
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ms.custom: sdkv2, update-code
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#Customer intent: As a Python scikit-learn developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my machine learning models at scale.
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If `DefaultAzureCredential` doesn't work for you, see [`azure-identity reference documentation`](/python/api/azure-identity/azure.identity) or [`Set up authentication`](how-to-setup-authentication.md?tabs=sdk) for more available credentials.
The result of running this script is a workspace handle that you use to manage other resources and jobs.
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> [!NOTE]
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> Creating `MLClient`will not connect the client to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call. In this article, this will happen during compute creation.
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> Creating `MLClient`won't connect the client to the workspace. The client initialization is lazy and waits for the first time it needs to make a call. In this article, this happens during compute creation.
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### Create a compute resource
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Azure Machine Learning needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
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In the following example script, we provision a Linux [`compute cluster`](./how-to-create-attach-compute-cluster.md?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. We only need a basic cluster for this example; thus, we pick a Standard_DS3_v2 model with 2 vCPU cores and 7-GB RAM to create an Azure Machine Learning compute.
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#### Create a custom environment
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To create your custom environment, you define your Conda dependencies in a YAML file. First, create a directory for storing the file. In this example, we've named the directory`env`.
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To create your custom environment, you define your Conda dependencies in a YAML file. First, create a directory for storing the file. In this example, the name is`env`.
The specification contains some usual packages (such as numpy and pip) that you use in your job.
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Next, use the YAML file to create and register this custom environment in your workspace. The environment is packaged into a Docker container at runtime.
For more information on creating and using environments, see [Create and use software environments in Azure Machine Learning](how-to-use-environments.md).
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##### [Optional] Create a custom environment with Intel® Extension for Scikit-Learn
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Want to speed up your scikit-learn scripts on Intel hardware? Try adding [Intel® Extension for Scikit-Learn](https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html) into your conda yaml file and following the subsequent steps detailed above. We'll show you how to enable these optimizations later in this example:
Want to speed up your scikit-learn scripts on Intel hardware? Try adding [Intel® Extension for Scikit-Learn](https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html) into your conda yaml file and following the subsequent steps detailed above. You'll see how to enable these optimizations later in this example:
#### [Optional] Enable Intel® Extension for Scikit-Learn optimizations for more performance on Intel hardware
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If you have installed Intel® Extension for Scikit-Learn (as demonstrated in the previous section), you can enable the performance optimizations by adding the two lines of code to the top of the script file, as shown below.
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If you installed Intel® Extension for Scikit-Learn (as demonstrated in the previous section), you can enable the performance optimizations by adding the two lines of code to the top of the script file, as shown below.
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To learn more about Intel® Extension for Scikit-Learn, visit the package's [documentation](https://intel.github.io/scikit-learn-intelex/).
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- configure the command line action itself—in this case, the command is `python train_iris.py`. You can access the inputs and outputs in the command via the `${{ ... }}` notation; and
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- configure the metadata such as the display name and experiment name; where an experiment is a container for all the iterations one does on a certain project. All the jobs submitted under the same experiment name would be listed next to each other in Azure Machine Learning studio.
Once completed, the job registers a model in your workspace (as a result of training) and output a link for viewing the job in Azure Machine Learning studio.
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Once completed, the job registers a model in your workspace (as a result of training) and outputs a link for viewing the job in Azure Machine Learning studio.
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> [!WARNING]
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> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory.
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Now that you've seen how to do a simple Scikit-learn training run using the SDK, let's see if you can further improve the accuracy of your model. You can tune and optimize our model's hyperparameters using Azure Machine Learning's [`sweep`](/python/api/azure-ai-ml/azure.ai.ml.sweep) capabilities.
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To tune the model's hyperparameters, define the parameter space in which to search during training. You do this by replacing some of the parameters (`kernel` and `penalty`) passed to the training job with special inputs from the `azure.ml.sweep` package.
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To tune the model's hyperparameters, define the parameter space in which to search during training. You tune by replacing some of the parameters (`kernel` and `penalty`) passed to the training job with special inputs from the `azure.ml.sweep` package.
Then, you configure sweep on the command job, using some sweep-specific parameters, such as the primary metric to watch and the sampling algorithm to use.
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In the following code we use random sampling to try different configuration sets of hyperparameters in an attempt to maximize our primary metric, `Accuracy`.
After you've registered your model, you can deploy it the same way as any other registered model in Azure Machine Learning. For more information about deployment, see [Deploy and score a machine learning model with managed online endpoint using Python SDK v2](how-to-deploy-managed-online-endpoint-sdk-v2.md).
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After you register your model, you can deploy it the same way as any other registered model in Azure Machine Learning. For more information about deployment, see [Deploy and score a machine learning model with managed online endpoint using Python SDK v2](how-to-deploy-managed-online-endpoint-sdk-v2.md).
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