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migrate-to-v2-resource-compute.md

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title titleSuffix description services ms.service ms.subservice ms.topic author ms.author ms.date ms.reviewer ms.custom monikerRange
Upgrade compute management to v2
Azure Machine Learning
Upgrade compute management from v1 to v2 of Azure Machine Learning SDK
machine-learning
machine-learning
mldata
reference
vijetajo
vijetaj
04/15/2024
franksolomon
migration
azureml-api-1 || azureml-api-2

Upgrade compute management to v2

The compute management functionally remains unchanged with the v2 development platform.

This article gives a comparison of scenarios in SDK v1 and SDK v2.

Create compute instance

  • SDK v1

    import datetime
import time

from azureml.core.compute import ComputeTarget, ComputeInstance
from azureml.core.compute_target import ComputeTargetException

# Compute Instances need to have a unique name across the region.
# Here, we create a unique name with current datetime
ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M")

compute_config = ComputeInstance.provisioning_configuration(
        vm_size='STANDARD_DS3_V2'
    )
    instance = ComputeInstance.create(ws, ci_basic_name , compute_config)
    instance.wait_for_completion(show_output=True)

  • SDK v2

    # Compute Instances need to have a unique name across the region.
# Here, we create a unique name with current datetime
from azure.ai.ml.entities import ComputeInstance, AmlCompute
import datetime

ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M")
ci_basic = ComputeInstance(name=ci_basic_name, size="STANDARD_DS3_v2", idle_time_before_shutdown_minutes="30")
ml_client.begin_create_or_update(ci_basic)

Create compute cluster

  • SDK v1

    from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

# Choose a name for your CPU cluster
cpu_cluster_name = "cpucluster"
compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS3_V2',
                                                           max_nodes=4)
cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
cpu_cluster.wait_for_completion(show_output=True)

  • SDK v2

    from azure.ai.ml.entities import AmlCompute
cpu_cluster_name = "cpucluster"
cluster_basic = AmlCompute(
    name=cpu_cluster_name,
    type="amlcompute",
    size="STANDARD_DS3_v2",
    max_instances=4
)
ml_client.begin_create_or_update(cluster_basic)

Mapping of key functionality in SDK v1 and SDK v2

Functionality in SDK v1 Rough mapping in SDK v2
Method/API in SDK v1 (use links to ref docs) Method/API in SDK v2 (use links to ref docs)

Next steps