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Train models with the Azure Machine Learning Python SDK (v1)

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APPLIES TO: Python SDK azureml v1

Learn how to attach Azure compute resources to your Azure Machine Learning workspace with SDK v1. Then you can use these resources as training and inference compute targets in your machine learning tasks.

In this article, learn how to set up your workspace to use these compute resources:

  • Your local computer
  • Remote virtual machines
  • Apache Spark pools (powered by Azure Synapse Analytics)
  • Azure HDInsight
  • Azure Batch
  • Azure Databricks - used as a training compute target only in machine learning pipelines
  • Azure Data Lake Analytics
  • Azure Container Instance
  • Azure Machine Learning Kubernetes

To use compute targets managed by Azure Machine Learning, see:

Important

Items in this article marked as "preview" are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Prerequisites

Limitations

  • Do not create multiple, simultaneous attachments to the same compute from your workspace. For example, attaching one Azure Kubernetes Service cluster to a workspace using two different names. Each new attachment will break the previous existing attachment(s).

    If you want to reattach a compute target, for example to change TLS or other cluster configuration setting, you must first remove the existing attachment.

What's a compute target?

With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine. You also use compute targets for model deployment as described in "Where and how to deploy your models".

Local computer

When you use your local computer for training, there is no need to create a compute target. Just submit the training run from your local machine.

When you use your local computer for inference, you must have Docker installed. To perform the deployment, use LocalWebservice.deploy_configuration() to define the port that the web service will use. Then use the normal deployment process as described in Deploy models with Azure Machine Learning.

Remote virtual machines

Azure Machine Learning also supports attaching an Azure Virtual Machine. The VM must be an Azure Data Science Virtual Machine (DSVM). The VM offers a curated choice of tools and frameworks for full-lifecycle machine learning development. For more information on how to use the DSVM with Azure Machine Learning, see Configure a development environment.

Tip

Instead of a remote VM, we recommend using the Azure Machine Learning compute instance. It is a fully managed, cloud-based compute solution that is specific to Azure Machine Learning. For more information, see create and manage Azure Machine Learning compute instance.

  1. Create: Azure Machine Learning cannot create a remote VM for you. Instead, you must create the VM and then attach it to your Azure Machine Learning workspace. For information on creating a DSVM, see Provision the Data Science Virtual Machine for Linux (Ubuntu).

    Warning

    Azure Machine Learning only supports virtual machines that run Ubuntu. When you create a VM or choose an existing VM, you must select a VM that uses Ubuntu.

    Azure Machine Learning also requires the virtual machine to have a public IP address.

  2. Attach: Attach the DSVM to your workspace using Azure Machine Learning studio.

    Warning

    Do not create multiple, simultaneous attachments to the same DSVM from your workspace. Each new attachment will break the previous existing attachment(s).

  3. Configure: Create a run configuration for the DSVM compute target. Docker and conda are used to create and configure the training environment on the DSVM.

    from azureml.core import ScriptRunConfig
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies

# Create environment
myenv = Environment(name="myenv")

# Specify the conda dependencies
myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])

# If no base image is explicitly specified the default CPU image "azureml.core.runconfig.DEFAULT_CPU_IMAGE" will be used
# To use GPU in DSVM, you should specify the default GPU base Docker image or another GPU-enabled image:
# myenv.docker.enabled = True
# myenv.docker.base_image = azureml.core.runconfig.DEFAULT_GPU_IMAGE

# Configure the run configuration with the Linux DSVM as the compute target and the environment defined above
src = ScriptRunConfig(source_directory=".", script="train.py", compute_target=compute, environment=myenv)

Tip

If you want to remove (detach) a VM from your workspace, use the RemoteCompute.detach() method.

Azure Machine Learning does not delete the VM for you. You must manually delete the VM using the Azure portal, CLI, or the SDK for Azure VM.

Apache Spark pools

The Azure Synapse Analytics integration with Azure Machine Learning (preview) allows you to attach an Apache Spark pool backed by Azure Synapse for interactive data exploration and preparation. With this integration, you can have a dedicated compute for data wrangling at scale. For more information, see How to attach Apache Spark pools powered by Azure Synapse Analytics.

Azure HDInsight

Azure HDInsight is a popular platform for big-data analytics. The platform provides Apache Spark, which can be used to train your model.

  1. Create: Azure Machine Learning cannot create an HDInsight cluster for you. Instead, you must create the cluster and then attach it to your Azure Machine Learning workspace. For more information, see Create a Spark Cluster in HDInsight.

    Warning

    Azure Machine Learning requires the HDInsight cluster to have a public IP address.

    When you create the cluster, you must specify an SSH user name and password. Take note of these values, as you need them to use HDInsight as a compute target.

    After the cluster is created, connect to it with the hostname <clustername>-ssh.azurehdinsight.net, where <clustername> is the name that you provided for the cluster.

  2. Attach: Attach the HDInsight cluster to your workspace using Azure Machine Learning studio.

    Warning

    Do not create multiple, simultaneous attachments to the same HDInsight from your workspace. Each new attachment will break the previous existing attachment(s).

  3. Configure: Create a run configuration for the HDI compute target.

    from azureml.core.runconfig import RunConfiguration
from azureml.core.conda_dependencies import CondaDependencies


# use pyspark framework
run_hdi = RunConfiguration(framework="pyspark")

# Set compute target to the HDI cluster
run_hdi.target = hdi_compute.name

# specify CondaDependencies object to ask system installing numpy
cd = CondaDependencies()
cd.add_conda_package('numpy')
run_hdi.environment.python.conda_dependencies = cd

Tip

If you want to remove (detach) an HDInsight cluster from the workspace, use the HDInsightCompute.detach() method.

Azure Machine Learning does not delete the HDInsight cluster for you. You must manually delete it using the Azure portal, CLI, or the SDK for Azure HDInsight.

Azure Batch

Azure Batch is used to run large-scale parallel and high-performance computing (HPC) applications efficiently in the cloud. AzureBatchStep can be used in an Azure Machine Learning Pipeline to submit jobs to an Azure Batch pool of machines.

To attach Azure Batch as a compute target, you must use the Azure Machine Learning SDK and provide the following information:

  • Azure Batch compute name: A friendly name to be used for the compute within the workspace
  • Azure Batch account name: The name of the Azure Batch account
  • Resource Group: The resource group that contains the Azure Batch account.

The following code demonstrates how to attach Azure Batch as a compute target:

from azureml.core.compute import ComputeTarget, BatchCompute
from azureml.exceptions import ComputeTargetException

# Name to associate with new compute in workspace
batch_compute_name = 'mybatchcompute'

# Batch account details needed to attach as compute to workspace
batch_account_name = "<batch_account_name>"  # Name of the Batch account
# Name of the resource group which contains this account
batch_resource_group = "<batch_resource_group>"

try:
    # check if the compute is already attached
    batch_compute = BatchCompute(ws, batch_compute_name)
except ComputeTargetException:
    print('Attaching Batch compute...')
    provisioning_config = BatchCompute.attach_configuration(
        resource_group=batch_resource_group, account_name=batch_account_name)
    batch_compute = ComputeTarget.attach(
        ws, batch_compute_name, provisioning_config)
    batch_compute.wait_for_completion()
    print("Provisioning state:{}".format(batch_compute.provisioning_state))
    print("Provisioning errors:{}".format(batch_compute.provisioning_errors))

print("Using Batch compute:{}".format(batch_compute.cluster_resource_id))

Warning

Do not create multiple, simultaneous attachments to the same Azure Batch from your workspace. Each new attachment will break the previous existing attachment(s).

Azure Databricks

Azure Databricks is an Apache Spark-based environment in the Azure cloud. It can be used as a compute target with an Azure Machine Learning pipeline.

Important

Azure Machine Learning cannot create an Azure Databricks compute target. Instead, you must create an Azure Databricks workspace, and then attach it to your Azure Machine Learning workspace. To create a workspace resource, see the Run a Spark job on Azure Databricks document.

To attach an Azure Databricks workspace from a different Azure subscription, you (your Microsoft Entra account) must be granted the Contributor role on the Azure Databricks workspace. Check your access in the Azure portal.

To attach Azure Databricks as a compute target, provide the following information:

  • Databricks compute name: The name you want to assign to this compute resource.
  • Databricks workspace name: The name of the Azure Databricks workspace.
  • Databricks access token: The access token used to authenticate to Azure Databricks. To generate an access token, see the Authentication document.

The following code demonstrates how to attach Azure Databricks as a compute target with the Azure Machine Learning SDK:

import os
from azureml.core.compute import ComputeTarget, DatabricksCompute
from azureml.exceptions import ComputeTargetException

databricks_compute_name = os.environ.get(
    "AML_DATABRICKS_COMPUTE_NAME", "<databricks_compute_name>")
databricks_workspace_name = os.environ.get(
    "AML_DATABRICKS_WORKSPACE", "<databricks_workspace_name>")
databricks_resource_group = os.environ.get(
    "AML_DATABRICKS_RESOURCE_GROUP", "<databricks_resource_group>")
databricks_access_token = os.environ.get(
    "AML_DATABRICKS_ACCESS_TOKEN", "<databricks_access_token>")

try:
    databricks_compute = ComputeTarget(
        workspace=ws, name=databricks_compute_name)
    print('Compute target already exists')
except ComputeTargetException:
    print('compute not found')
    print('databricks_compute_name {}'.format(databricks_compute_name))
    print('databricks_workspace_name {}'.format(databricks_workspace_name))
    print('databricks_access_token {}'.format(databricks_access_token))

    # Create attach config
    attach_config = DatabricksCompute.attach_configuration(resource_group=databricks_resource_group,
                                                           workspace_name=databricks_workspace_name,
                                                           access_token=databricks_access_token)
    databricks_compute = ComputeTarget.attach(
        ws,
        databricks_compute_name,
        attach_config
    )

    databricks_compute.wait_for_completion(True)

For a more detailed example, see an example notebook on GitHub.

Warning

Do not create multiple, simultaneous attachments to the same Azure Databricks from your workspace. Each new attachment will break the previous existing attachment(s).

Azure Data Lake Analytics

Azure Data Lake Analytics is a big data analytics platform in the Azure cloud. It can be used as a compute target with an Azure Machine Learning pipeline.

Create an Azure Data Lake Analytics account before using it. To create this resource, see the Get started with Azure Data Lake Analytics document.

To attach Data Lake Analytics as a compute target, you must use the Azure Machine Learning SDK and provide the following information:

  • Compute name: The name you want to assign to this compute resource.
  • Resource Group: The resource group that contains the Data Lake Analytics account.
  • Account name: The Data Lake Analytics account name.

The following code demonstrates how to attach Data Lake Analytics as a compute target:

import os
from azureml.core.compute import ComputeTarget, AdlaCompute
from azureml.exceptions import ComputeTargetException


adla_compute_name = os.environ.get(
    "AML_ADLA_COMPUTE_NAME", "<adla_compute_name>")
adla_resource_group = os.environ.get(
    "AML_ADLA_RESOURCE_GROUP", "<adla_resource_group>")
adla_account_name = os.environ.get(
    "AML_ADLA_ACCOUNT_NAME", "<adla_account_name>")

try:
    adla_compute = ComputeTarget(workspace=ws, name=adla_compute_name)
    print('Compute target already exists')
except ComputeTargetException:
    print('compute not found')
    print('adla_compute_name {}'.format(adla_compute_name))
    print('adla_resource_id {}'.format(adla_resource_group))
    print('adla_account_name {}'.format(adla_account_name))
    # create attach config
    attach_config = AdlaCompute.attach_configuration(resource_group=adla_resource_group,
                                                     account_name=adla_account_name)
    # Attach ADLA
    adla_compute = ComputeTarget.attach(
        ws,
        adla_compute_name,
        attach_config
    )

    adla_compute.wait_for_completion(True)

For a more detailed example, see an example notebook on GitHub.

Warning

Do not create multiple, simultaneous attachments to the same ADLA from your workspace. Each new attachment will break the previous existing attachment(s).

Tip

Azure Machine Learning pipelines can only work with data stored in the default data store of the Data Lake Analytics account. If the data you need to work with is in a non-default store, you can use a DataTransferStep to copy the data before training.

Azure Container Instance

Azure Container Instances (ACI) are created dynamically when you deploy a model. You cannot create or attach ACI to your workspace in any other way. For more information, see Deploy a model to Azure Container Instances.

Kubernetes

Azure Machine Learning provides you with the option to attach your own Kubernetes clusters for training and inferencing. See Configure Kubernetes cluster for Azure Machine Learning.

To detach a Kubernetes cluster from your workspace, use the following method:

compute_target.detach()

Warning

Detaching a cluster does not delete the cluster. To delete an Azure Kubernetes Service cluster, see Use the Azure CLI with AKS. To delete an Azure Arc-enabled Kubernetes cluster, see Azure Arc quickstart.

Notebook examples

See these notebooks for examples of training with various compute targets:

Learn how to run notebooks by following the article Use Jupyter notebooks to explore this service.

Next steps