code for amlcompute section

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

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+# Code for Azure Machine Learning Compute - Run-based creation
+
+# Check core SDK version number
+import azureml.core
+
+print("SDK version:", azureml.core.VERSION)
+
+
+from azureml.core import Workspace
+ws = Workspace.from_config()
+
+
+# Set up an experiment 
+from azureml.core import Experiment
+experiment_name = 'my-experiment'
+script_folder= "./"
+
+exp = Experiment(workspace=ws, name=experiment_name)
+
+
+#<amlcompute_temp>
+from azureml.core.compute import ComputeTarget, AmlCompute
+
+# First, list the supported VM families for Azure Machine Learning Compute
+print(AmlCompute.supported_vmsizes(workspace=ws))
+
+from azureml.core.runconfig import RunConfiguration
+# Create a new runconfig object
+run_temp_compute = RunConfiguration()
+
+# Signal that you want to use AmlCompute to execute the script
+run_temp_compute.target = "amlcompute"
+
+# AmlCompute is created in the same region as your workspace
+# Set the VM size for AmlCompute from the list of supported_vmsizes
+run_temp_compute.amlcompute.vm_size = 'STANDARD_D2_V2'
+#</amlcompute_temp>
+
+
+# Submit the experiment using the run configuration
+from azureml.core import ScriptRunConfig
+
+src = ScriptRunConfig(source_directory = script_folder, script = 'train.py', run_config = run_temp_compute)
+run = exp.submit(src)
+run.wait_for_completion(show_output = True)
+
+
+

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

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+# Code for Azure Machine Learning Compute - Persistent compute
+
+# Check core SDK version number
+import azureml.core
+
+print("SDK version:", azureml.core.VERSION)
+
+from azureml.core import Workspace
+ws = Workspace.from_config()
+
+
+# Set up an experiment 
+from azureml.core import Experiment
+experiment_name = 'my-experiment'
+script_folder= "./"
+
+exp = Experiment(workspace=ws, name=experiment_name)
+
+#<cpu_basic>
+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"
+
+# Verify that cluster does not exist already
+try:
+    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
+    print('Found existing cluster, use it.')
+except ComputeTargetException:
+    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
+                                                           max_nodes=4)
+    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
+
+cpu_cluster.wait_for_completion(show_output=True)
+#</cpu_basic>
+
+#<aml_runconfig>
+from azureml.core.runconfig import RunConfiguration
+from azureml.core.conda_dependencies import CondaDependencies
+from azureml.core.runconfig import DEFAULT_CPU_IMAGE
+
+# Create a new runconfig object
+run_amlcompute = RunConfiguration()
+
+# Use the cpu_cluster you created above. 
+run_amlcompute.target = cpu_cluster
+
+# Enable Docker
+run_amlcompute.environment.docker.enabled = True
+
+# Set Docker base image to the default CPU-based image
+run_amlcompute.environment.docker.base_image = DEFAULT_CPU_IMAGE
+
+# Use conda_dependencies.yml to create a conda environment in the Docker image for execution
+run_amlcompute.environment.python.user_managed_dependencies = False
+
+# Auto-prepare the Docker image when used for execution (if it is not already prepared)
+run_amlcompute.auto_prepare_environment = True
+
+# Specify CondaDependencies obj, add necessary packages
+run_amlcompute.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])
+#</aml_runconfig>
+
+# Submit the experiment using the run configuration
+#<amlcompute_submit>
+from azureml.core import ScriptRunConfig
+
+src = ScriptRunConfig(source_directory = script_folder, script = 'train.py', run_config = run_amlcompute)
+run = exp.submit(src)
+run.wait_for_completion(show_output = True)
+#</amlcompute_submit>