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Please follow instructions to install Azure CLI 2.0, configure default location, create and configure default resource group and storage account.

Data Deployment

  • Download convolutional.py sample script into the current folder:

For GNU/Linux users:

wget "https://raw.githubusercontent.com/Azure/BatchAI/master/recipes/TensorFlow/TensorFlow-GPU/convolutional.py?token=AcZzrZcCveHaaevWYBtN9wYREYDOJvY-ks5Z4c4QwA%3D%3D" -O convolutional.py
  • Create an Azure File Share with tensorflow_samples folder and upload convolutional.py into it:
az storage share create --name batchaisample
az storage directory create --share-name batchaisample --name tensorflow_samples
az storage file upload --share-name batchaisample --source convolutional.py --path tensorflow_samples

Cluster

For this recipe we need one node GPU cluster (min node = max node = 1) of Standard_NC6 size (one GPU) with standard Ubuntu LTS (UbuntuLTS) or Ubuntu DSVM (UbuntuDSVM) image and Azure File share batchaisample mounted at $AZ_BATCHAI_MOUNT_ROOT/external.

Cluster Creation Command

For GNU/Linux users:

az batchai cluster create -n nc6 -i UbuntuDSVM -s Standard_NC6 --min 1 --max 1 --afs-name batchaisample --afs-mount-path external -u $USER -k ~/.ssh/id_rsa.pub

For Windows users:

az batchai cluster create -n nc6 -i UbuntuDSVM -s Standard_NC6 --min 1 --max 1 --afs-name batchaisample --afs-mount-path external -u <user_name> -p <password>

Job

The job creation parameters are in job.json:

  • An input directory with ID SCRIPT to allow the job to find the sample script via environment variable $AZ_BATCHAI_INPUT_SCRIPT;
  • stdOutErrPathPrefix specifies that the job should use file share for standard output and error streams;
  • nodeCount defining how many nodes will be used for the job execution;
  • path and parameters for running convolutional.py;
  • tensorflow/tensorflow:1.1.0-gpu docker image will be used for job execution.

Note, you can delete the docker image information to run the job directly on DSVM.

Job Creation Command

az batchai job create -n tensorflow --cluster-name nc6 -c job.json

Note, the job will start running when the cluster finished allocation and initialization of the node.