Azure Machine Learning Studio, Python 3.10

Remco Coppens 25 Reputation points
2024-04-20T12:35:02.2666667+00:00

Hi,

I am trying to train my Deep Learning model in Azure ML studio through the Visual Studio Code extension. My code is build upon Python 3.10, which is needed for several packages. However I cannot seem to get my Azure ML Studio compute to run on this version of Python, as it remains in its default of 3.8.5.

Setting an environment does not work as well, at least the way I tried it. As this process is not well documented elsewhere and nowhere on the internet I can find a solution to my problem.

Thank you in advance for your help!

Best regards,
Remco Coppens

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
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Accepted answer
  1. romungi-MSFT 42,761 Reputation points Microsoft Employee
    2024-04-22T09:24:56.4333333+00:00

    @Remco Coppens I believe you use the compute instance while using the VS code extension. If this is the case, did you try to enable SSH to your compute instance to login and try to upgrade the python version or set the required version to use by default?

    If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

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  1. Amira Bedhiafi 16,146 Reputation points
    2024-04-21T19:18:41.2166667+00:00

    Have you tried creating a custom environment? Maybe you need to specify explicitly Python 3.10 and configure your training job to use this environment.

    You can define an environment in Azure ML using the Python SDK.

    https://learn.microsoft.com/en-us/azure/machine-learning/concept-environments?view=azureml-api-2

    from azureml.core import Environment
    from azureml.core.conda_dependencies import CondaDependencies
    # Create a Python environment for AzureML
    env = Environment('custom-python3.10-env')
    # Build conda dependencies
    conda_dep = CondaDependencies()
    conda_dep.set_python_version('3.10')
    conda_dep.add_conda_package('numpy')  # Add other necessary packages
    conda_dep.add_pip_package('tensorflow')  # Assuming TensorFlow is required
    conda_dep.add_pip_package('keras')  # Add any additional pip packages
    # Add the dependencies to the environment
    env.python.conda_dependencies = conda_dep
    # Register the environment to reuse later env.register(workspace=your_workspace)
    

    How to use it ?

    from azureml.core import Experiment, ScriptRunConfig
    
    # Set up the script run configuration
    src = ScriptRunConfig(source_directory='./your_script_folder',
                          script='train.py',  # Your training script
                          compute_target='your-compute-target',  # Your compute target
                          environment=env)  # Use the custom environment
    
    # Submit the experiment
    experiment = Experiment(workspace=your_workspace, name='MyExperiment')
    run = experiment.submit(config=src)
    run.wait_for_completion(show_output=True)
    
    
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