batch-shipyard/recipes/Chainer-GPU/README.md

Chainer-GPU

This recipe shows how to run Chainer 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.

Pool Configuration

The pool configuration should enable the following properties:

• vm_size must be a GPU enabled VM size. Because Chainer 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.

Global Configuration

The global configuration should set the following properties:

• docker_images array must have a reference to a valid Caffe GPU-enabled Docker image. The official chainer Docker image can be used for this recipe.

Jobs Configuration

The jobs configuration should set the following properties within the tasks array to run the MNIST MLP example. This array should have a task definition containing:

• docker_image should be the name of the Docker image for this container invocation, e.g., chainer/chainer
• resource_files array should be populated if you want Azure Batch to handle the download of the training file from the web endpoint:
  • file_path is the local file path which should be set to train_mnist.py
  • blob_source is the remote URL of the file to retrieve: https://raw.githubusercontent.com/pfnet/chainer/master/examples/mnist/train_mnist.py
• command should contain the command to pass to the Docker run invocation. For the chainer/chainer Docker image and to run the MNIST MLP example, the command would be: python -u train_mnist.py -g 0
• 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.

Note that you could have inlined the download in the command itself provided the Docker image has programs to fetch content from the required source.