batch-shipyard/recipes/CNTK-CPU-OpenMPI

CNTK-CPU-OpenMPI

This recipe shows how to run CNTK on CPUs on one or many compute nodes via MPI.

Configuration

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

Pool Configuration

The pool configuration should enable the following properties:

• inter_node_communication_enabled must be set to true
• max_tasks_per_node must be set to 1 or omitted

Global Configuration

The global configuration should set the following properties:

• docker_images array must have a reference to a valid CNTK CPU-enabled Docker image. For singlenode (non-MPI) jobs, you can use the official Microsoft CNTK Docker images. For MPI jobs, you will need to use Batch Shipyard compatible Docker images which can be found in the alfpark/cntk repository. Images denoted with refdata tag suffixes found in can be used for this recipe which contains reference data for MNIST and CIFAR-10 examples. If you do not need this reference data then you can use the images without the refdata suffix on the image tag.

Non-MPI Jobs Configuration (SingleNode)

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., microsoft/cntk:2.1-cpu-python3.5
• command should contain the command to pass to the Docker run invocation. For the microsoft/cntk:2.1-cpu-python3.5 Docker image and to run the MNIST convolutional example on a single CPU, the command would be: "/bin/bash -c \"source /cntk/activate-cntk && cd /cntk/Examples/Image/DataSets/MNIST && python -u install_mnist.py && cd /cntk/Examples/Image/Classification/ConvNet/Python && python -u ConvNet_MNIST.py\""

MPI Jobs Configuration (MultiNode)

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. For this example, this should be alfpark/cntk:2.1-cpu-py35-refdata. Please note that the docker_images in the Global Configuration should match this image name.
• command should contain the command to pass to the Docker run invocation. For this example, we will run the MNIST convolutional example with Data augmentation in the alfpark/cntk:2.1-cpu-py35-refdata Docker image. The application command to run would be: "/cntk/run_cntk.sh -s /cntk/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py -- --datadir /cntk/Examples/Image/DataSets/CIFAR-10 --outputdir $AZ_BATCH_TASK_WORKING_DIR/output"
  • run_cntk.sh has two parameters
    • -s for the Python script to run
    • -w for the working directory (not required for this example to run)
    • -- parameters specified after this are given verbatim to the Python script
• multi_instance property must be defined
  • num_instances should be set to pool_specification_vm_count_dedicated, pool_specification_vm_count_low_priority, pool_current_dedicated, or pool_current_low_priority
  • coordination_command should be unset or null. For pools with native container support, this command should be supplied if a non-standard sshd is required.
  • resource_files should be unset or the array can be empty

Dockerfile and supplementary files

The Dockerfile for the Docker image can be found here.

You must agree to the following licenses prior to use:

• CNTK License
• CNTK 1-bit SGD License