The compute cluster is resized from 0 to 1 node. The very first run will take 5-10 minutes to complete. At first, you'll see a status of Queued or Preparing. The output from your script will contain a link to the studio that looks something like this:įollow the link. Monitor your code in the cloud in the studio Select std_log.txt to view the output of your job. When the status of the job is Completed, select Output + logs at the top of the page. In the page that opens, you'll see the job status. Use the link at the bottom of these warnings to view your output. You may see some warnings starting with Failure while loading azureml_run_type_providers. Create a Python file called run-hello.py and copy/paste the following code into that file: # get-started/run-hello.pyįrom re import Workspace, Experiment, Environment, ScriptRunConfigĮxperiment = Experiment(workspace=ws, name='day1-experiment-hello')Ĭonfig = ScriptRunConfig(source_directory='./src', script='hello.py', compute_target='cpu-cluster') at the end of get-started folder to create a new file. You use the control script to control how and where your machine learning code is run. Create a control scriptĪ control script allows you to run your hello.py script on different compute resources. Close the tab and select Terminate to close the session. You'll see the output of the script in the terminal window that opens. Select Save and run script in terminal to run the script. Wait about a minute for state to change to Running. If you have previously stopped your compute instance, start it now with the Start compute tool to the right of the compute dropdown. Running code locally has the benefit of interactive debugging of code. You can run your code locally, which in this case means on the compute instance. Your project folder structure will now look like: Switch the File type to Python (.py)*.Ĭopy this code into your file: # src/hello.py To the right of the src folder, use the. 
Use the Edit location link if the file location is not correct. to create another folder under get-started. To the right of the folder name, use the.In the Files toolbar, select +, then select Create new folder.Sign in to the Azure Machine Learning studio and select your workspace if prompted.First create a few folders and the script: This tutorial will use the compute instance as your development computer. Name it 'cpu-cluster' to match the code in this tutorial. Complete Create resources you need to get started to create a workspace and compute instance to use in this tutorial series.Submit and run the "Hello world!" script.
Understand the Azure Machine Learning concepts in the control script.Create a Python control script to submit "Hello world!" to Azure Machine Learning.Create and run a "Hello world!" Python script.
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