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.
