Azure Machine Learning (Azure ML)#
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RAPIDS can be deployed at scale using Azure Machine Learning Service and easily scales up to any size needed. We have written a detailed guide with helper scripts to get everything deployed, but the high level procedure is:
1. Create. Create your Azure Resource Group.
2. Workspace. Within the Resource Group, create an Azure Machine Learning service Workspace.
3. Config. Within the Workspace, download the
config.json file and verify that subscription_id, resource_group, and workspace_name are set correctly for your environment.
4. Quota. Within your Workspace, check your Usage + Quota to ensure you have enough quota to launch your desired cluster size.
5. Clone. From your local machine, clone the RAPIDS demonstration code and helper scripts.
6. Run Utility. Run the RAPIDS helper utility script to initialize the Azure Machine Learning service Workspace:
$ ./start_azureml.py\ --config=[CONFIG_PATH]\ --vm_size=[VM_SIZE]\ --node_count=[NUM_NODES]
[CONFIG_PATH] = the path to the config file you downloaded in step three.
7. Start. Open your browser to
http://localhost:8888 and get started!