Running Azure Machine Learning Service pipeline locally - azure-machine-learning-workbench

Question

I'm using Azure Machine Learning Service with the azureml-sdk python library.
I'm using azureml.core version 1.0.8
I'm following this https://learn.microsoft.com/en-us/azure/machine-learning/service/how-to-create-your-first-pipeline tutorial.
I've got it working when I use Azure Compute resources. But I would like to run it locally.
I get the following error
raise ErrorResponseException(self._deserialize, response)
azureml.pipeline.core._restclients.aeva.models.error_response.ErrorResponseException: (BadRequest) Response status code does not indicate success: 400 (Bad Request).
Trace id: [uuid], message: Can't build command text for [train.py], moduleId [uuid] executionId [id]: Assignment for parameter Target is not specified
My code looks like:
run_config = RunConfiguration()
compute_target = LocalTarget()
run_config.target = LocalTarget()
run_config.environment.python.conda_dependencies = CondaDependencies(conda_dependencies_file_path='environment.yml')
run_config.environment.python.interpreter_path = 'C:/Projects/aml_test/.conda/envs/aml_test_env/python.exe'
run_config.environment.python.user_managed_dependencies = True
run_config.environment.docker.enabled = False
trainStep = PythonScriptStep(
script_name="train.py",
compute_target=compute_target,
source_directory='.',
allow_reuse=False,
runconfig=run_config
)
steps = [trainStep]
# Build the pipeline
pipeline = Pipeline(workspace=ws, steps=[steps])
pipeline.validate()
experiment = Experiment(ws, 'Test')
# Fails, locally, works on Azure Compute
run = experiment.submit(pipeline)
# Works both locally and on Azure Compute
src = ScriptRunConfig(source_directory='.', script='train.py', run_config=run_config)
run = experiment.submit(src)
The train.py is a very simple self contained script only dependent on numpy that approximates pi.

Answer 1

Local compute cannot be used with ML Pipelines. Please see this article.

Answer 2

Training on you local machine (for instance during development) is possible and very easy according to the documentation: how-to-set-up-training-targets
I did this on my windows computer as follows:
Define the local environment:
sklearn_env = Environment("user-managed-env")
sklearn_env.python.user_managed_dependencies = True
# You can choose a specific Python environment by pointing to a Python path
sklearn_env.python.interpreter_path = r'C:\Dev\tutorial\venv\Scripts\python.exe'
And compute_target='local' seems to be the magic word to direct a script to my local environment.
src = ScriptRunConfig(source_directory=script_folder,
script='train_iris.py',
arguments=[dataset.as_named_input('iris')],
compute_target='local',
environment=sklearn_env)
I will then need to make sure that my local environment has all the dependencies that the script needs.
Additionally I needed to install these packages on my local machine:
azureml-defaults
packaging