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ignore/doc-qa/how-to-set-up-training-targets/Local.py

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@@ -5,7 +5,7 @@
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print("SDK version:", azureml.core.VERSION)
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#<local_env>
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#<run_local>
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from azureml.core.runconfig import RunConfiguration
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# Edit a run configuration property on the fly.
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# Choose a specific Python environment by pointing to a Python path. For example:
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# run_config.environment.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'
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#</local_env>
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#</run_local>
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from azureml.core import Workspace
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ws = Workspace.from_config()

ignore/doc-qa/how-to-set-up-training-targets/amlcompute.py

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exp = Experiment(workspace=ws, name=experiment_name)
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#<amlcompute_temp>
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#<run_temp_compute>
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from azureml.core.compute import ComputeTarget, AmlCompute
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# First, list the supported VM families for Azure Machine Learning Compute
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# AmlCompute is created in the same region as your workspace
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# Set the VM size for AmlCompute from the list of supported_vmsizes
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run_temp_compute.amlcompute.vm_size = 'STANDARD_D2_V2'
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#</amlcompute_temp>
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#</run_temp_compute>
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# Submit the experiment using the run configuration

ignore/doc-qa/how-to-set-up-training-targets/amlcompute2.py

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exp = Experiment(workspace=ws, name=experiment_name)
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#<cpu_basic>
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#<cpu_cluster>
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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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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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#</cpu_basic>
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#</cpu_cluster>
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#<aml_runconfig>
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#<run_amlcompute>
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from azureml.core.runconfig import RunConfiguration
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from azureml.core.conda_dependencies import CondaDependencies
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from azureml.core.runconfig import DEFAULT_CPU_IMAGE
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# Specify CondaDependencies obj, add necessary packages
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run_amlcompute.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])
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#</aml_runconfig>
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#</run_amlcompute>
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# Submit the experiment using the run configuration
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#<amlcompute_submit>

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