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52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
# Code for Remote virtual machines
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compute_target_name = "attach-dsvm"
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#<run_dsvm>
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import azureml.core
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from azureml.core.runconfig import RunConfiguration, DEFAULT_CPU_IMAGE
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from azureml.core.conda_dependencies import CondaDependencies
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run_dsvm = RunConfiguration(framework = "python")
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# Set the compute target to the Linux DSVM
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run_dsvm.target = compute_target_name
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# Use Docker in the remote VM
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run_dsvm.environment.docker.enabled = True
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# Use the CPU base image
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# To use GPU in DSVM, you must also use the GPU base Docker image "azureml.core.runconfig.DEFAULT_GPU_IMAGE"
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run_dsvm.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE
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print('Base Docker image is:', run_dsvm.environment.docker.base_image)
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# Prepare the Docker and conda environment automatically when they're used for the first time
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run_dsvm.prepare_environment = True
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# Specify the CondaDependencies object
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run_dsvm.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])
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#</run_dsvm>
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hdi_compute.name = "blah"
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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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# use pyspark framework
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hdi_run_config = RunConfiguration(framework="pyspark")
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# Set compute target to the HDI cluster
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hdi_run_config.target = hdi_compute.name
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# specify CondaDependencies object to ask system installing numpy
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cd = CondaDependencies()
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cd.add_conda_package('numpy')
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hdi_run_config.environment.python.conda_dependencies = cd
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#<run_hdi>
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from azureml.core.runconfig import RunConfiguration
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# Configure the HDInsight run
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# Load the runconfig object from the myhdi.runconfig file generated in the previous attach operation
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run_hdi = RunConfiguration.load(project_object = project, run_name = 'myhdi')
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# Ask the system to prepare the conda environment automatically when it's used for the first time
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run_hdi.auto_prepare_environment = True> |