title: Restrict the number of nodes used by an Azure Machine Learning pipeine
body: <p>I have written a pipeline that I want to run on a remote compute cluster within Azure Machine Learning. My aim is to process a large amount of historical data, and to do this I will need to run the pipeline on a large number of input parameter combinations.</p>
  
  <p>Is there a way to restrict the number of nodes that the pipeline uses on the cluster? By default it will use all the nodes available to the cluster, and I would like to restrict it so that it only uses a pre-defined maximum. This allows me to leave the rest of the cluster free for other users.</p>
  
  <p>My current code to start the pipeline looks like this:</p>
  
  <pre><code># Setup the pipeline
  steps = [data_import_step] # Contains PythonScriptStep
  pipeline = Pipeline(workspace=ws, steps=steps)
  pipeline.validate()
  
  # Big long list of historical dates that I want to process data for
  dts = pd.date_range('2019-01-01', '2020-01-01', freq='6H', closed='left')
  # Submit the pipeline job
  for dt in dts:
      pipeline_run = Experiment(ws, 'my-pipeline-run').submit(
          pipeline,
          pipeline_parameters={
              'import_datetime': dt.strftime('%Y-%m-%dT%H:00'),
          }
      )
  </code></pre>