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Batch Samples (#46786)
* adding code snippits for microsoft learn
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‎sdk/batch/azure-batch/samples/batch_automatic_scaling.py‎

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# coding: utf-8
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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"""Snippets extracted from articles/batch/batch-automatic-scaling.md.
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"""
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import datetime
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from azure.batch import BatchClient, models
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def autoscale_create_and_enable(batch_client: BatchClient, pool_id: str) -> None:
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# [START autoscale_pool_create_enable_python]
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# Create a pool; specify configuration
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new_pool = models.BatchPoolCreateOptions(
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id=pool_id,
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virtual_machine_configuration=models.VirtualMachineConfiguration(
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image_reference=models.BatchVmImageReference(
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publisher="Canonical",
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offer="UbuntuServer",
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sku="20.04-LTS",
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version="latest",
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),
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node_agent_sku_id="batch.node.ubuntu 20.04",
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),
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vm_size="STANDARD_D1_v2",
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target_dedicated_nodes=0,
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target_low_priority_nodes=0,
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)
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batch_client.create_pool(pool=new_pool) # Add the pool to the service client
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formula = (
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"$curTime = time();\n"
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"$workHours = $curTime.hour >= 8 && $curTime.hour < 18;\n"
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"$isWeekday = $curTime.weekday >= 1 && $curTime.weekday <= 5;\n"
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"$isWorkingWeekdayHour = $workHours && $isWeekday;\n"
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"$TargetDedicated = $isWorkingWeekdayHour ? 20:10;"
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)
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# Enable autoscale; specify the formula
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enable_options = models.BatchPoolEnableAutoScaleOptions(
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auto_scale_formula=formula,
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auto_scale_evaluation_interval=datetime.timedelta(minutes=10),
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)
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batch_client.enable_pool_auto_scale(pool_id=pool_id, enable_auto_scale_options=enable_options)
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# [END autoscale_pool_create_enable_python]

‎sdk/batch/azure-batch/samples/batch_docker_container_workloads.py‎

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# coding: utf-8
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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# Snippets extracted from articles/batch/batch-docker-container-workloads.md (Python only).
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from azure.batch import models
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def make_pool_no_prefetch(pool_id: str):
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# [START docker_pool_no_prefetch_python]
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image_ref_to_use = models.BatchVmImageReference(
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publisher='microsoft-dsvm',
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offer='ubuntu-hpc',
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sku='2204',
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version='latest')
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# Specify container configuration. This is required even though there are no prefetched images.
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container_conf = models.BatchContainerConfiguration(type='dockerCompatible')
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new_pool = models.BatchPoolCreateOptions(
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id=pool_id,
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virtual_machine_configuration=models.VirtualMachineConfiguration(
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image_reference=image_ref_to_use,
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container_configuration=container_conf,
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node_agent_sku_id='batch.node.ubuntu 22.04'),
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vm_size='STANDARD_D2S_V3',
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target_dedicated_nodes=1)
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# [END docker_pool_no_prefetch_python]
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return new_pool
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def make_pool_dockerhub_prefetch(pool_id: str):
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# [START docker_pool_dockerhub_prefetch_python]
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image_ref_to_use = models.BatchVmImageReference(
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publisher='microsoft-dsvm',
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offer='ubuntu-hpc',
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sku='2204',
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version='latest')
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# Specify container configuration, fetching the official Ubuntu container image from Docker Hub.
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container_conf = models.BatchContainerConfiguration(
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type='dockerCompatible',
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container_image_names=['ubuntu'])
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new_pool = models.BatchPoolCreateOptions(
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id=pool_id,
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virtual_machine_configuration=models.VirtualMachineConfiguration(
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image_reference=image_ref_to_use,
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container_configuration=container_conf,
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node_agent_sku_id='batch.node.ubuntu 22.04'),
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vm_size='STANDARD_D2S_V3',
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target_dedicated_nodes=1)
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# [END docker_pool_dockerhub_prefetch_python]
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return new_pool
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def make_pool_acr_prefetch():
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# [START docker_pool_acr_prefetch_python]
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image_ref_to_use = models.BatchVmImageReference(
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publisher='microsoft-dsvm',
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offer='ubuntu-hpc',
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sku='2204',
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version='latest')
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# Specify a container registry
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subscription_id = "yyyy-yyy-yyy-yyy-yyy"
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resource_group_name = "TestRG"
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user_assigned_identity_name = "testUMI"
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resource_id = (
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f"/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}"
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f"/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{user_assigned_identity_name}"
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)
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container_registry = models.ContainerRegistryReference(
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registry_server="myRegistry.azurecr.io",
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identity_reference=models.BatchNodeIdentityReference(resource_id=resource_id))
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# Create container configuration, prefetching Docker images from the container registry
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container_conf = models.BatchContainerConfiguration(
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type='dockerCompatible',
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container_image_names=["myRegistry.azurecr.io/samples/myImage"],
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container_registries=[container_registry])
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new_pool = models.BatchPoolCreateOptions(
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id="myPool",
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virtual_machine_configuration=models.VirtualMachineConfiguration(
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image_reference=image_ref_to_use,
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container_configuration=container_conf,
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node_agent_sku_id='batch.node.ubuntu 22.04'),
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vm_size='STANDARD_D2S_V3',
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target_dedicated_nodes=1)
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# [END docker_pool_acr_prefetch_python]
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return new_pool
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def make_container_task():
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# [START docker_container_task_python]
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task_id = 'sampletask'
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task_container_settings = models.BatchTaskContainerSettings(
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image_name='myimage',
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container_run_options='--rm --workdir /')
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task = models.BatchTaskCreateOptions(
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id=task_id,
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command_line='/bin/sh -c "echo \'hello world\' > $AZ_BATCH_TASK_WORKING_DIR/output.txt"',
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container_settings=task_container_settings
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)
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# [END docker_container_task_python]
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return task

‎sdk/batch/azure-batch/samples/batch_linux_nodes.py‎

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# coding: utf-8
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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# Snippets extracted from articles/batch/batch-linux-nodes.md (Python only).
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import datetime
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import getpass
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from azure.batch import BatchClient, models
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from azure.core.credentials import AzureNamedKeyCredential
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def create_pool_explicit_image():
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# [START linux_nodes_pool_create_python]
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# Specify Batch account credentials
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account = "<batch-account-name>"
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key = "<batch-account-key>"
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account_endpoint = "<batch-account-url>"
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# Pool settings
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pool_id = "LinuxNodesSamplePoolPython"
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vm_size = "STANDARD_D2_V3"
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node_count = 1
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# Initialize the Batch client
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creds = AzureNamedKeyCredential(account, key)
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client = BatchClient(endpoint=account_endpoint, credential=creds)
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# Configure the start task for the pool
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start_task = models.BatchStartTask(
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command_line="printenv AZ_BATCH_NODE_STARTUP_DIR",
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user_identity=models.UserIdentity(
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auto_user=models.AutoUserSpecification(
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elevation_level=models.ElevationLevel.ADMIN,
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scope=models.AutoUserScope.POOL,
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)
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),
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)
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# Create an ImageReference which specifies the Marketplace
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# virtual machine image to install on the nodes
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ir = models.BatchVmImageReference(
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publisher="canonical",
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offer="0001-com-ubuntu-server-focal",
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sku="20_04-lts",
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version="latest")
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# Create the VirtualMachineConfiguration, specifying
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# the VM image reference and the Batch node agent
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# to install on the node
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vmc = models.VirtualMachineConfiguration(
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image_reference=ir,
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node_agent_sku_id="batch.node.ubuntu 20.04")
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# Create the unbound pool
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new_pool = models.BatchPoolCreateOptions(
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id=pool_id,
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vm_size=vm_size,
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target_dedicated_nodes=node_count,
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virtual_machine_configuration=vmc,
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start_task=start_task,
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)
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# Create pool in the Batch service
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client.create_pool(pool=new_pool)
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# [END linux_nodes_pool_create_python]
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return client
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def vm_config_from_supported_images(client: BatchClient):
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# [START linux_nodes_image_reference_python]
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# Get the list of supported images from the Batch service
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images = list(client.list_supported_images())
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# Obtain the desired image reference
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image = None
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for img in images:
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if (img.image_reference.publisher and img.image_reference.publisher.lower() == "canonical" and
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img.image_reference.offer and img.image_reference.offer.lower() == "0001-com-ubuntu-server-focal" and
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img.image_reference.sku and img.image_reference.sku.lower() == "20_04-lts"):
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image = img
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break
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if image is None:
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raise RuntimeError('invalid image reference for desired configuration')
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# Create the VirtualMachineConfiguration, specifying the VM image
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# reference and the Batch node agent to be installed on the node
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vmc = models.VirtualMachineConfiguration(
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image_reference=image.image_reference,
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node_agent_sku_id=image.node_agent_sku_id)
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# [END linux_nodes_image_reference_python]
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return vmc
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def ssh_create_user_demo_inputs():
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# [START linux_nodes_ssh_user_python]
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# Specify your own account credentials
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batch_account_name = ''
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batch_account_key = ''
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batch_account_url = ''
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# Specify the ID of an existing pool containing Linux nodes
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# currently in the 'idle' state
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pool_id = ''
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# Specify the username and prompt for a password
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username = 'linuxuser'
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password = getpass.getpass()
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# Create a BatchClient
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creds = AzureNamedKeyCredential(batch_account_name, batch_account_key)
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batch_client = BatchClient(endpoint=batch_account_url, credential=creds)
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# Create the user that will be added to each node in the pool
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expiry = datetime.datetime.utcnow() + datetime.timedelta(days=30)
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user = models.BatchNodeUserCreateOptions(
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name=username,
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password=password,
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is_admin=True,
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expiry_time=expiry,
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)
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# Get the list of nodes in the pool and add the user to each
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nodes = batch_client.list_nodes(pool_id=pool_id)
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for node in nodes:
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batch_client.create_node_user(pool_id=pool_id, node_id=node.id, user=user)
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login_settings = batch_client.get_node_remote_login_settings(pool_id=pool_id, node_id=node.id)
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print(f"{node.id}: ssh {username}@{login_settings.remote_login_ip_address} -p {login_settings.remote_login_port}")
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# [END linux_nodes_ssh_user_python]

‎sdk/batch/azure-batch/samples/batch_sig_images.py‎

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# coding: utf-8
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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# Snippets extracted from articles/batch/batch-sig-images.md.
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from azure.batch import BatchClient, models
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from azure.identity import DefaultAzureCredential
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def create_sig_pool():
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# [START sig_create_pool_python]
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# Specify Batch account credentials
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account_endpoint = "https://{batch-account-name}.{region}.batch.azure.com"
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# Pool settings
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pool_id = "LinuxNodesSamplePoolPython"
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vm_size = "STANDARD_D2_V3"
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node_count = 1
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# Initialize the Batch client with Microsoft Entra ID authentication
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client = BatchClient(endpoint=account_endpoint, credential=DefaultAzureCredential())
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# Configure the start task for the pool
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start_task = models.BatchStartTask(
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command_line="printenv AZ_BATCH_NODE_STARTUP_DIR",
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user_identity=models.UserIdentity(
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auto_user=models.AutoUserSpecification(
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elevation_level=models.ElevationLevel.ADMIN,
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scope=models.AutoUserScope.POOL,
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)
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),
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)
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# Create an image reference that points to an Azure Compute Gallery image.
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ir = models.BatchVmImageReference(
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virtual_machine_image_id=(
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"/subscriptions/{sub id}/resourceGroups/{resource group name}"
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"/providers/Microsoft.Compute/galleries/{gallery name}"
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"/images/{image definition name}/versions/{version id}"
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)
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)
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# Create the VirtualMachineConfiguration
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vmc = models.VirtualMachineConfiguration(
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image_reference=ir,
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node_agent_sku_id="batch.node.ubuntu 22.04",
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)
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# Create the unbound pool
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new_pool = models.BatchPoolCreateOptions(
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id=pool_id,
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vm_size=vm_size,
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target_dedicated_nodes=node_count,
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virtual_machine_configuration=vmc,
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start_task=start_task,
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)
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# Create pool in the Batch service
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client.create_pool(pool=new_pool)
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# [END sig_create_pool_python]
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if __name__ == "__main__":
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create_sig_pool()

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