TensorFlow-GPU

TensorFlow-GPU

This recipe shows how to run TensorFlow on GPUs using N-series Azure VM instances in an Azure Batch compute pool.

Execution under both Docker and Singularity are shown in this recipe.

Configuration

Please see refer to the set of sample configuration files for this recipe. The directory docker will contain the Docker-based execution while the singularity directory will contain the Singularity-based execution configuration.

Pool Configuration

The pool configuration should enable the following properties:

• vm_size must be a GPU enabled VM size. Because TensorFlow 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. VM instance size.

Global Configuration

Docker-based

The global configuration should set the following properties:

• docker_images array must have a reference to a valid TensorFlow GPU-enabled Docker image. The official Google TensorFlow GPU Docker images can be used for this recipe (e.g., tensorflow/tensorflow:latest-gpu)

Singularity-based

The global configuration should set the following properties:

• singularity_images array must have a reference to a valid TensorFlow GPU-enabled Docker image. The Docker Hub Google TensorFlow GPU Docker images on can be used for this recipe (e.g., docker://tensorflow/tensorflow:latest-gpu)

Jobs Configuration

Docker-based

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

• docker_image should be the name of the Docker image for this container invocation that matches the global configuration Docker image, e.g., tensorflow/tensorflow:latest-gpu
• 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 convolutional.py
  • blob_source is the remote URL of the file to retrieve: https://raw.githubusercontent.com/tensorflow/models/master/tutorials/image/mnist/convolutional.py
• command should contain the command to pass to the Docker run invocation. To run the MNIST convolutional example, the command would be: python -u convolutional.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.

Singularity-based

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

• singularity_image should be the name of the Singularity image for this container invocation that matches the global configuration image, e.g., docker://tensorflow/tensorflow:latest-gpu
• 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 convolutional.py
  • blob_source is the remote URL of the file to retrieve: https://raw.githubusercontent.com/tensorflow/models/master/tutorials/image/mnist/convolutional.py
• command should contain the command to pass to the Docker run invocation. To run the MNIST convolutional example, the command would be: python -u convolutional.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.

Tensorboard

If you would like to tunnel Tensorboard to your local machine, use the jobs-tb.yaml file instead. This requires that a pool SSH user was added, and ssh or ssh.exe is available. This configuration will output summary data to the directory specified in the --log_dir parameter. After the job is submitted, you can start the remote Tensorboard instance with the command:

shipyard misc tensorboard

Which will output some text similar to the following:

>> Please connect to Tensorboard at http://localhost:6006/

>> Note that Tensorboard may take a while to start if the Docker image is
>> not present. Please keep retrying the URL every few seconds.

>> Terminate your session with CTRL+C

>> If you cannot terminate your session cleanly, run:
     shipyard pool ssh --nodeid tvm-1518333292_4-20170428t151941z sudo docker kill 9e7879b8

With a web browser, navigate to http://localhost:6006/ where Tensorboard will be displayed.

Note that the task does not have to be completed for Tensorboard to be run, it can be running while Tensorboard is running.