azureml-defaults

import os from azureml.core import Workspace, Experiment, Environment # NOTE: For local execution, ensure you have a config.json or environment variables. # You can create a config.json by connecting to your workspace in Azure portal # and downloading the config.json file. # Example for connecting to an existing workspace try: ws = Workspace.from_config() # Looks for config.json in current directory or parent directories print(f"Workspace name: {ws.name}, Resource Group: {ws.resource_group}, Location: {ws.location}") # Create a simple experiment exp = Experiment(workspace=ws, name="my-first-azureml-experiment") print(f"Experiment name: {exp.name}") # Start a run (this example won't submit to a remote compute, just demonstrates API) with exp.start_logging() as run: run.log('hello_world', 'true') print("Run logged 'hello_world'") print(f"Run ID: {run.id}") except Exception as e: print(f"Error connecting to workspace or running experiment: {e}") print("Please ensure you are authenticated (e.g., 'az login') or have a valid config.json.") print("You might need to set AZUREML_CR_NAME, AZUREML_CR_RESOURCE_GROUP, AZUREML_CR_SUBSCRIPTION_ID in environment variables.") # Example of creating a workspace (requires Azure CLI login and permissions) # from azureml.core.authentication import AzureCliAuthentication # try: # cli_auth = AzureCliAuthentication() # ws = Workspace.create( # name='myworkspacename', # subscription_id=os.environ.get('AZURE_SUBSCRIPTION_ID', 'YOUR_SUB_ID'), # resource_group='myresourcegroup', # create_resource_group=True, # location='eastus', # auth=cli_auth # ) # print(f"Created workspace: {ws.name}") # except Exception as e: # print(f"Could not create workspace: {e}")