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job.yml
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job.yml
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$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
# name of the experiment, use this to structure your training results
experiment_name: nebula-mnist-example
display_name: nebula-mnist-example
code: .
# compute cluster on which the command above should be run
# note: before you can use the compute, it needs to be created first in your AzureML workspace
compute: gpu-v100-1GPU-cluster
# compute resources
resources:
# number of nodes in the cluster above
instance_count: 1
# environment in which the command above should be run in
# note: It is recommended to use an environment which either is the ACPT image or at least inherits from it. The ACPT
# image has many frameworks related to PyTorch pre-installed, hence reduces efforts to create your own
# environment. To learn more about environments in AML, see
# https://learn.microsoft.com/en-us/azure/machine-learning/concept-environments
#
# for the latest ACPT image using PyTorch 1.12, CUDA 11.6 on Python 3.9
# environment: azureml:acpt-pytorch-1.12-py39-cuda11.6@latest
environment: azureml:AzureML-acpt-pytorch-2.0-cuda11.7:3
# for a specific version
#environment: azureml:AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu:3
# for the latest version of a custom environment: (====install Nebula in dockerfile environment=====)
#environment: azureml:ACPT-Extended@latest
# for a specific Docker image (has to be compatible with Azure ML):
#environment:
# image: mcr.microsoft.com/azureml/curated/acpt-pytorch-1.12-py39-cuda11.6-gpu:2
# command that should be run by the job (====install Nebula with comand OR use dockerfile environment=====)
command: >-
python train.py --save-model