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49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
# Code for Azure Machine Learning Compute - Run-based creation
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# Check core SDK version number
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import azureml.core
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print("SDK version:", azureml.core.VERSION)
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from azureml.core import Workspace
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ws = Workspace.from_config()
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# Set up an experiment
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from azureml.core import Experiment
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experiment_name = 'my-experiment'
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script_folder= "./"
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exp = Experiment(workspace=ws, name=experiment_name)
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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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print(AmlCompute.supported_vmsizes(workspace=ws))
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from azureml.core.runconfig import RunConfiguration
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# Create a new runconfig object
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run_temp_compute = RunConfiguration()
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# Signal that you want to use AmlCompute to execute the script
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run_temp_compute.target = "amlcompute"
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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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#</run_temp_compute>
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# Submit the experiment using the run configuration
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from azureml.core import ScriptRunConfig
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src = ScriptRunConfig(source_directory = script_folder, script = 'train.py', run_config = run_temp_compute)
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run = exp.submit(src)
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run.wait_for_completion(show_output = True)
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