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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 and extract preprocessed MNIST database:

For GNU/Linux users:

wget "https://batchaisamples.blob.core.windows.net/samples/mnist_dataset_original.zip?st=2017-09-29T18%3A29%3A00Z&se=2099-12-31T08%3A00%3A00Z&sp=rl&sv=2016-05-31&sr=b&sig=Qc1RA3zsXIP4oeioXutkL1PXIrHJO0pHJlppS2rID3I%3D" -O mnist_dataset_original.zip
unzip mnist_dataset_original.zip
  • Download mnist_replica.py sample script into the current folder:

For GNU/Linux users:

wget "https://raw.githubusercontent.com/Azure/BatchAI/master/recipes/TensorFlow/TensorFlow-GPU-Distributed/mnist_replica.py?token=AcZzrcpJGDHCUzsCyjlWiKVNfBuDdkqwks5Z4dPrwA%3D%3D" -O mnist_replica.py
  • Create an Azure File Share with mnist_dataset and tensorflow_samples folders and upload MNIST database and convolutional.py into them:
az storage share create --name batchaisample
az storage directory create --share-name batchaisample --name mnist_dataset
az storage file upload --share-name batchaisample --source t10k-images-idx3-ubyte.gz --path mnist_dataset
az storage file upload --share-name batchaisample --source t10k-labels-idx1-ubyte.gz --path mnist_dataset
az storage file upload --share-name batchaisample --source train-images-idx3-ubyte.gz --path mnist_dataset
az storage file upload --share-name batchaisample --source train-labels-idx1-ubyte.gz --path mnist_dataset
az storage directory create --share-name batchaisample --name tensorflow_samples
az storage file upload --share-name batchaisample --source mnist_replica.py --path tensorflow_samples

Cluster

For this recipe we need two nodes GPU cluster (min node = max node = 2) 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 2 --max 2 --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 2 --max 2 --afs-name batchaisample --afs-mount-path external -u <user_name> -p <password>

Job

The job creation parameters are in job.json:

  • Two input directories with IDs SCRIPT and DATASET to allow the job to find the sample script and MNIST Database via environment variables $AZ_BATCHAI_INPUT_SCRIPT and $AZ_BATCHAI_INPUT_DATASET;
  • stdOutErrPathPrefix specifies that the job should use file share for standard output and error streams;
  • An output directory with ID MODEL to allow job to find the output directory for the model via $AZ_BATCHAI_OUTPUT_MODEL environment variable;
  • nodeCount defining how many nodes will be used for the job execution;
  • path to mnist_replica.py and parameters for master, workers and parameter server;
  • 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 distibuted_tensorflow --cluster-name nc6 -c job.json

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