Installation

Install the Azure ML Python SDK:

pip install azureml-sdk

Create Workspace#

Please prepare your Azure subscription, resource group, and decide your Workspace name in order to create Workspace. If you don't have subscription, please generate it for free.

from azureml.core import Workspace

ws = Workspace.create(name='<my_workspace_name>', # provide a name for your workspace
                      subscription_id='<azure-subscription-id>', # provide your subscription ID
                      resource_group='<myresourcegroup>', # provide a resource group name
                      create_resource_group=True,
                      location='<NAME_OF_REGION>') # e.g. 'westeurope' or 'eastus2' or 'westus2' or 'southeastasia'.

# write out the workspace details to a configuration file: .azureml/config.json
ws.write_config(path='.azureml')

info

You can easily access this later with

from azureml.core import Workspace
ws = Workspace.from_config()

Create Compute Target#

The following example creates a compute target in your workspace with:

VM type: CPU
VM size: STANDARD_D2_V2
Cluster size: up to 4 nodes
Idle time: 2400s before the node scales down automatically

Modify this code to update to GPU, or to change the SKU of your VMs.

from azureml.core import Workspace
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

ws = Workspace.from_config() # automatically looks for a directory .azureml/

# name for your cluster
cpu_cluster_name = "cpu-cluster"

try:
    # check if cluster already exists
    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
    print('Found existing cluster, use it.')
except ComputeTargetException:
    # if not, create it
    compute_config = AmlCompute.provisioning_configuration(
        vm_size='STANDARD_D2_V2',
        max_nodes=4, 
        idle_seconds_before_scaledown=2400,)
    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
    cpu_cluster.wait_for_completion(show_output=True)

info

You can access this later with

from azureml.core import ComputeTarget
cpu_cluster = ComputeTarget(ws, 'cpu-cluster')