Keras+Theano-GPU

This recipe shows how to run Keras with the Theano backend on GPUs using N-series Azure VM instances in an Azure Batch compute pool.

Configuration

Please see refer to this set of sample configuration files for this recipe.

The pool configuration should enable the following properties:

vm_size must be a GPU enabled VM size. Because Keras is a GPU-accelerated compute application, you should choose a GPU compute accelerated VM instance size.
vm_configuration is the VM configuration. Please select an appropriate platform_image with GPU as supported by Batch Shipyard.

The global configuration should set the following properties:

docker_images array must have a reference to a valid Keras+Theano GPU-enabled Docker image. alfpark/keras:gpu can be used for this recipe.

The jobs configuration should set the following properties within the tasks array which should have a task definition containing:

docker_image should be the name of the Docker image for this container invocation, e.g., alfpark/keras:gpu
command should contain the command to pass to the Docker run invocation. For the alfpark/keras:gpu Docker image and to run the MNIST convolutional example, the command would simply be: "python -u /keras/examples/mnist_cnn.py"
gpus can be set to all, however, it is implicitly enabled by Batch Shipyard when executing on a GPU-enabled compute pool and can be omitted.

The Dockerfile for the Docker image can be found here.

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