"PermissionError: [Errno 13] Permission denied" when trying to access a local file for a conda environment

AJV 11 Reputation points

After Azure ML Studio blocking me from using any compute due to an as-of-yet unresolved authentication error, I moved to using a Jupyter Notebook on my local workstation to try to configure my experiments locally then send the job to an Azure compute cluster. I have two lines of Python that tries to create an environment class by accessing a .yml file on my local computer:

yml_path = r"C:\Users\me\Desktop\azure_training\training_env"
pytorch_env = Environment.from_conda_specification(name='pytorch-1.11-gpu', file_path=yml_path)

This causes the following error:

PermissionError: [Errno 13] Permission denied: 'C:\Users\me\Desktop\azure_training\training_env'

I am unsure of what is causing this. When the file doesn't need to be private, I have solved permission denied issues in the past that resulted from locally run tools such as PostgreSQL by going to the file's properties>>security and adding the user "Everyone" with full control. I tried doing that in this case, but it had no impact. I still get permission denied even though "Everyone" has full control over the file.

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
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  1. Ramr-msft 17,731 Reputation points

    @AJV Thanks for the question. if you don't want to "bake" your personal access token (essentially a password) into your Conda environment file, is to follow Use private Python packages - Azure Machine Learning | Microsoft Learn and connect the authenticated feed with your workspace.

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