In our company I use Azure ML and I have the following issue. I specify a conda_requirements.yaml file with the PyTorch estimator class, like so (... are placeholders so that I do not have to type everything out):
from azureml.train.dnn import PyTorch
est = PyTorch(source_directory=’.’, script_params=..., compute_target=..., entry_script=..., conda_dependencies_file_path=’conda_requirements.yaml’, environment_variables=..., framework_version=’1.1’)
The conda_requirements.yaml (shortened version of the pip part) looks like this:
This successfully builds on Azure. Now in order to reuse the resulting docker image in that case, I use the
custom_docker_image parameter to pass to the
from azureml.train.estimator import Estimator
est = Estimator(source_directory=’.’, script_params=..., compute_target=..., entry_script=..., custom_docker_image=’<container registry name>.azurecr.io/azureml/azureml_c3a4f...’, environment_variables=...)
But now Azure somehow seems to rebuild the image again and when I run the experiment it cannot install torch. So it seems to only install the conda dependencies and not the pip dependencies, but actually I do not want Azure to rebuild the image. Can I solve this somehow?
I attempted to somehow build a docker image from my Docker file and then add to the registry. I can do az login and according to https://docs.microsoft.com/en-us/azure/container-registry/container-registry-authentication I then should also be able to do an acr login and push. This does not work.
Even using the credentials from
az acr credential show –name <container registry name>
and then doing a
docker login <container registry name>.azurecr.io –u <username from credentials above> -p <password from credentials above>
does not work.
The error message is authentication required even though I used
successfully. Would also be happy if someone could explain that to me in addition to how to reuse docker images when using Azure ML.