Using local compute with an Estimator in Azure ML

Question

Is it possible to use a local compute for the TensorFlow estimator? Provisioning a virtual machine for a training run takes an enormous amount of time, and I would like to be able to try a few runs locally until my configuration is stable.

https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/service/how-to-train-tensorflow.md

It is possible to do this with ScriptRunConfig by creating an empty RunConfiguration. The documentation claims that it is possible to create a local ComputeTarget, but the documentation on how to do this is missing:

https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/service/how-to-set-up-training-targets.md#local

Local computer

Create and attach: There's no need to create or attach a compute target to use your local computer as the training environment.

Configure: When you use your local computer as a compute target, the training code is run in your development environment. If that environment already has the Python packages you need, use the user-managed environment.

[!code-python]

Answer 1

Create an empty RunConfiguration and write this code: 

from azureml.core.runconfig import RunConfiguration

# Edit a run configuration property on the fly.

run_local = RunConfiguration()

run_local.environment.python.user_managed_dependencies = True