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Merge pull request #212831 from sdgilley/sdg-migration
add compute migration
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---
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title: 'Migrate compute management from SDK v1 to v2'
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titleSuffix: Azure Machine Learning
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description: Migrate compute management from v1 to v2 of Azure Machine Learning SDK
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services: machine-learning
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ms.service: machine-learning
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ms.subservice: mldata
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ms.topic: reference
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author: vijetajo
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ms.author: vijetaj
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ms.date: 09/28/2022
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ms.reviewer: sgilley
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ms.custom: migration
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---
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# Migrate compute management from SDK v1 to v2
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The compute management functionally remains unchanged with the v2 development platform.
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This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
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## Create compute instance
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* SDK v1
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```python
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import datetime
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import time
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from azureml.core.compute import ComputeTarget, ComputeInstance
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from azureml.core.compute_target import ComputeTargetException
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# Compute Instances need to have a unique name across the region.
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# Here we create a unique name with current datetime
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ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M")
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compute_config = ComputeInstance.provisioning_configuration(
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vm_size='STANDARD_DS3_V2'
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)
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instance = ComputeInstance.create(ws, ci_basic_name , compute_config)
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instance.wait_for_completion(show_output=True)
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```
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* SDK v2
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```python
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# Compute Instances need to have a unique name across the region.
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# Here we create a unique name with current datetime
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from azure.ai.ml.entities import ComputeInstance, AmlCompute
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import datetime
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ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M")
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ci_basic = ComputeInstance(name=ci_basic_name, size="STANDARD_DS3_v2")
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ml_client.begin_create_or_update(ci_basic)
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```
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## Create compute cluster
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* SDK v1
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```python
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from azureml.core.compute import ComputeTarget, AmlCompute
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from azureml.core.compute_target import ComputeTargetException
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# Choose a name for your CPU cluster
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cpu_cluster_name = "cpucluster"
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compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS3_V2',
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max_nodes=4)
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cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
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cpu_cluster.wait_for_completion(show_output=True)
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```
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* SDK v2
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```python
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from azure.ai.ml.entities import AmlCompute
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cpu_cluster_name = "cpucluster"
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cluster_basic = AmlCompute(
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name=cpu_cluster_name,
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type="amlcompute",
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size="STANDARD_DS3_v2",
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max_instances=4
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)
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ml_client.begin_create_or_update(cluster_basic)
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```
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## Mapping of key functionality in SDK v1 and SDK v2
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|Functionality in SDK v1|Rough mapping in SDK v2|
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|-|-|
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|[Method/API in SDK v1 (use links to ref docs)](/python/api/azureml-core/azureml.core.compute.amlcompute(class))|[Method/API in SDK v2 (use links to ref docs)](/python/api/azure-ai-ml/azure.ai.ml.entities.amlcompute)|
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## Next steps
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* [Create and manage an Azure Machine Learning compute instance](how-to-create-manage-compute-instance.md)
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* [Create an Azure Machine Learning compute cluster](how-to-create-attach-compute-cluster.md)

articles/machine-learning/toc.yml

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- name: "Workspace"
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displayName: migration, v1, v2
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href: migrate-to-v2-resource-workspace.md
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- name: "Compute"
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displayName: migration, v1, v2
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href: migrate-to-v2-resource-compute.md
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- name: "Datastore"
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displayName: migration, v1, v2
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href: migrate-to-v2-resource-datastore.md

articles/machine-learning/v1/toc.yml

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- name: "Workspace"
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displayName: migration, v1, v2
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href: ../migrate-to-v2-resource-workspace.md
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- name: "Compute"
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displayName: migration, v1, v2
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href: ../migrate-to-v2-resource-compute.md
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- name: "Datastore"
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displayName: migration, v1, v2
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href: ../migrate-to-v2-resource-datastore.md

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