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25 lines
1.2 KiB
Bash
25 lines
1.2 KiB
Bash
#!/bin/bash
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# This script configures the environment to
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# 1. Use the configured AzureML Workspace with azureml.core.Workspace.from_config()
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# 2. Set the default MLflow Tracking Server to be the AzureML managed one
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############## START CONFIGURATION #################
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# Provide the required *AzureML* workspace information
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region="" # example: westus2
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subscriptionId="" # example: bcb65f42-f234-4bff-91cf-9ef816cd9936
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resourceGroupName="" # example: dev-rg
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workspaceName="" # example: myazuremlws
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# Optional config directory
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configLocation="/databricks/config.json"
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############### END CONFIGURATION #################
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# Drop the workspace configuration on the cluster
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sudo touch $configLocation
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sudo echo {\\"subscription_id\\": \\"${subscriptionId}\\", \\"resource_group\\": \\"${resourceGroupName}\\", \\"workspace_name\\": \\"${workspaceName}\\"} > $configLocation
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# Set the MLflow Tracking URI
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trackingUri="adbazureml://${region}.experiments.azureml.net/history/v1.0/subscriptions/${subscriptionId}/resourceGroups/${resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/${workspaceName}"
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sudo echo export MLFLOW_TRACKING_URI=${trackingUri} >> /databricks/spark/conf/spark-env.sh
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