Azure / Azure/MachineLearningNotebooks
AzureML Compute Job failed to start
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- Jupyter Notebook
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Description
Unable to use dockerfile to create an Environment in the `explain-model-on-amlcompute.ipynb` notebook.
The following is the code
```
from azureml.core import Workspace, Environment
myenv = Environment(name="rapidsenv")
myenv.register(workspace=ws)
myenv.docker.enabled=True
dockerfile = r"""
FROM rapidsai/rapidsai-cloud-ml:0.17-cuda11.0-base-ubuntu18.04-py3.8
RUN echo "success" # I'd like to install different packages here
"""
myenv.docker.base_image = None
myenv.docker.base_dockerfile = dockerfile
myenv.python.user_managed_dependencies=True
from azureml.core import Run
from azureml.core import ScriptRunConfig
src = ScriptRunConfig(source_directory=project_folder,
script='train_explain.py',
compute_target=gpu_cluster,
environment=myenv)
run = experiment.submit(config=src)
run
```
This is what shows up on the Portal.
```
AzureMLCompute job failed.
JobContainerConfigFailed: Container configuration failed unexpectedly
JobContainerConfigFailed: Container configuration failed unexpectedly
err: Docker exec failure when attempting to execute 'containerSetup' task. Error: exit status 126. Indicates a command was found but not executable, likely due to permission issues or missing dependencies.
Reason: Docker exec failure when attempting to execute 'containerSetup' task. Error: exit status 126. Indicates a command was found but not executable, likely due to permission issues or missing dependencies.
Info: Failed to prepare an environment for the job execution: Job environment preparation failed on 10.0.0.4 with err exit status 1.
```
How do I resolve this issue?
Guide de contribution
Aucun guide de contribution indexé pour ce dépôt
Piste de recherche
Commencez par explain-model-on-amlcompute.ipynb et exécutez l’exemple fourni de Environment et ScriptRunConfig sur la cible de calcul GPU. Utilisez les messages JobContainerConfigFailed et containerSetup exit-status-126 du Portal pour suivre la préparation de l’environnement ; c’est terminé lorsque le travail du notebook démarre correctement avec l’environnement basé sur Dockerfile.
Rédigé par le modèle d'indexation à partir du texte de l'issue.
Évaluation
- Stack technique
- azure, docker, jupyter-notebook, python
- Domaine
- cloud, devops, machine-learning
- Type d'issue
- Bug
- Difficulté
- 4/5
- Temps estimé
- 3-5 jours
- Activité
- À l'abandon
- Clarté
- Plutôt claire
- Accessibilité débutants
- 35/100