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Quickstart: Launch Distributed Training Workloads

Introduction

Distributed Training is the ability to split the training of a model among multiple processors. Each processor is called a worker node. Worker nodes work in parallel to speed up model training. Distributed Training should not be confused with multi-GPU training. Multi-GPU training is the allocation of more than a single GPU to your workload which runs on a single container.

Getting Distributed Training to work is more complex than multi-GPU training as it requires syncing of data and timing between the different workers. However, it is often a necessity when multi-GPU training no longer applies; typically when you require more GPUs than exist on a single node. There are a number of Deep Learning frameworks that support Distributed Training. Horovod is a good example.

Run:AI provides the ability to run, manage, and view Distributed Training workloads. The following is a Quickstart document for such a scenario.

Prerequisites

To complete this Quickstart you must have:

Step by Step Walkthrough

Setup

  • Login to the Projects area of the Run:AI Administration user interface at https://app.run.ai/projects
  • Add a Project named "team-a"
  • Allocate 2 GPUs to the Project

Run Training Distributed Workload

  • At the command-line run:
runai config project team-a
runai submit-mpi dist --processes=2 -g 1 \
        -i gcr.io/run-ai-demo/quickstart-distributed
  • We named the Job dist
  • The Job is assigned to team-a
  • There will be two worker processes (--processes=2), each allocated with a single GPU (-g 1)
  • The Job is based on a sample docker image gcr.io/run-ai-demo/quickstart-distributed.
  • The image contains a startup script that runs a deep learning Horovod-based workload.

Follow up on the Job's status by running:

    runai list jobs

The result:

mceclip11.png

The Run:AI scheduler ensures that all processes can run together. You can see the list of workers as well as the main "launcher" process by running:

    runai describe job dist

You will see two worker processes (pods) their status and on which node they run:

mceclip12.png

To see the merged logs of all pods run:

    runai logs dist

Finally, you can delete the distributed training workload by running:

    runai delete dist

Run an Interactive Distributed Workload

It is also possible to run a distributed training Job as "interactive". This is useful if you want to test your distributed training Job before committing on a long, unattended training session. To run such a session use:

runai submit-mpi dist-int --processes=2 -g 1 \
        -i gcr.io/run-ai-demo/quickstart-distributed --interactive \
        --command -- sh -c sleep infinity 

When the workers are running run:

    runai bash dist-int

This will provide shell access to the launcher process. From there, you can run your distributed workload. For Horovod version smaller than 0.17.0 run:

horovodrun -np $RUNAI_MPI_NUM_WORKERS \
        python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model=resnet20 --num_batches=1000000 --data_name cifar10 \
        --data_dir /cifar10 --batch_size=64 --variable_update=horovod

For Horovod version 0.17.0 or later, add the -hostfile flag as follows:

horovodrun -np $RUNAI_MPI_NUM_WORKERS -hostfile /etc/mpi/hostfile \
        python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model=resnet20 --num_batches=1000000 --data_name cifar10 \
        --data_dir /cifar10 --batch_size=64 --variable_update=horovod 

The environment variable RUNAI_MPI_NUM_WORKERS is passed by Run:AI and contains the number of worker processes provided to the runai submit-mpi command (in the above example the value is 2).

See Also

  • The source code of the image used in this Quickstart document is in Github
  • For a full list of the submit-mpi options see runai submit-mpi

Last update: August 6, 2021