Question 105736 in Azure Question posted in
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How do I print environment packages in AzureML

I am trying to print a .yml file for an environment. I can do it for this environment:

env = Environment.get(workspace = ws, name = 'AzureML-AutoML', version=115)
print(env)
print(env.python.conda_dependencies.serialize_to_string())

>>>Environment(Name: AzureML-AutoML,
Version: 115)
channels:
- anaconda
- conda-forge
- pytorch
dependencies:
- python=3.7.9
- pip=20.2.4
- pip:
  - azureml-core==1.42.0
  - azureml-mlflow==1.42.0
...

When I use the same code for this other environment I get an error:

infer_env = 'AzureML-pytorch-1.10-ubuntu18.04-py37-cpu-inference'
curated_pytorch_infer_env = Environment.get(workspace = ws, name = infer_env, version=4)

print(curated_pytorch_infer_env)
print(curated_pytorch_infer_env.python.conda_dependencies.serialize_to_string())

>>> AttributeError: 'NoneType' object has no attribute 'serialize_to_string'

How do I get/print a .yml file for ‘curated_pytorch_infer_env’?

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2

Answers


  1. 0

    To resolve AttributeError: 'NoneType' object has no attribute 'serialize_to_string' try following ways:

    1. According to Use curated environments:

    Note: The name prefix AzureML is reserved for curated environments. Don’t use it for your own environment.

    You can try this code snippet taken from the document:

    envs = Environment.list(workspace=ws)

for env in envs:
    if env.startswith("AzureML"):
        print("Name",env)
        if envs[env].python.conda_dependencies is not None:
            print("packages", envs[env].python.conda_dependencies.serialize_to_string())

    2. Try upgrading to the latest version i.e. PyTorch 1.12

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  2. 0

    You can submit a job with the environment that would do conda export in a subprocess. Probably this might work https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py#azureml-core-environment-environment-from-existing-conda-environment

    Environment name is returned in env.get_image_details(ws) response.

    Easiest way might be to pull corresponding image and run it locally to conda export

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