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. Several Deep Learning frameworks 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:

Run:ai software installed on your Kubernetes cluster. See: Installing Run:ai on a Kubernetes Cluster
During the installation, you have installed the Kubeflow MPI Operator as specified here
Run:ai CLI installed on your machine. See: Installing the Run:ai Command-Line Interface

Step by Step Walkthrough¶

Setup¶

Login to the Projects area of the Run:ai user interface.
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-dist mpi --workers=2 -g 1 \
        -i gcr.io/run-ai-demo/quickstart-distributed:v0.3.0 -e RUNAI_SLEEP_SECS=60

We named the Job dist
The Job is assigned to team-a
There will be two worker pods (--workers=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:v0.3.0.
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:

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

    runai describe job dist

You will see two worker pods, their status, and on which node they run:

To see the merged logs of all pods run:

    runai logs dist

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

    runai delete job 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-dist mpi dist-int --workers=2 -g 1 \
        -i gcr.io/run-ai-demo/quickstart-distributed:v0.3.0 --interactive \
        -- 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 workers provided to the runai submit-dist mpi command (in the above example the value is 2).