Open SamAchten opened 3 years ago
Can you provide the full code sample? In principle, you should not do any file system mounting: you can specify input datasets as job arguments and AzureML will do all the mounting for you so that you can access them within your jobs.
Hello,
We are trying to mount an Azure Storage account in Azure ML. This works perfectly fine, until we start a child run. In the logs of the child run, we can see the following: Set Dataset input__c79bd306's target path to /mnt/batch/tasks/shared/LS_root/jobs/ml-studio-01/azureml/train_classification_model_20210909_fr_1631216080_e3eca838/wd/input__c79bd306_f7faa3c3-938e-4cfc-950b-c91c9827dfa4
But when we try to access the mount, we get the following error: '/mnt/batch/tasks/shared/LS_root/jobs/ml-studio-01/azureml/train_classification_model_20210909_fr_1631216080_e3eca838/wd/input__c79bd306_f7faa3c3-938e-4cfc-950b-c91c9827dfa4': No such file or directory
The code to start the child run can be found below. Thank you for your help.
child_config = ScriptRunConfig(source_directory='.', script='src/main_child.py', arguments=arguments, environment=environment, docker_runtime_config=DockerConfiguration(use_docker=True), compute_target=compute_target) run.submit_child(child_config)