MPI Operator

The MPI Operator makes it easy to run allreduce-style distributed training on Kubernetes. Please check out this blog post for an introduction to MPI Operator and its industry adoption.

Installation

You can deploy the operator with default settings by running the following commands:

• Latest Development Version

kubectl apply --server-side -f https://raw.githubusercontent.com/kubeflow/mpi-operator/master/deploy/v2beta1/mpi-operator.yaml

• Release Version

kubectl apply --server-side -f https://raw.githubusercontent.com/kubeflow/mpi-operator/v0.8.2/deploy/v2beta1/mpi-operator.yaml

Alternatively, follow the getting started guide to deploy Kubeflow.

An alpha version of MPI support was introduced with Kubeflow 0.2.0. You must be using a version of Kubeflow newer than 0.2.0.

You can check whether the MPI Job custom resource is installed via:

kubectl get crd

The output should include mpijobs.kubeflow.org like the following:

NAME                                       AGE
...
mpijobs.kubeflow.org                       4d
...

If it is not included, you can add it as follows using kustomize:

git clone https://github.com/kubeflow/mpi-operator
cd mpi-operator
kustomize build manifests/overlays/kubeflow | kubectl apply -f -

Note that since Kubernetes v1.14, kustomize became a subcommand in kubectl. Since Kubernetes v1.21, you can also use:

kubectl apply -k manifests/overlays/kubeflow

kubectl kustomize manifests/overlays/kubeflow | kubectl apply -f -

Creating an MPI Job

You can create an MPI job by defining an MPIJob config file. See TensorFlow benchmark example config file for launching a multi-node TensorFlow benchmark training job. You may change the config file based on your requirements.

cat examples/v2beta1/tensorflow-benchmarks/tensorflow-benchmarks.yaml

Deploy the MPIJob resource to start training:

kubectl apply -f examples/v2beta1/tensorflow-benchmarks/tensorflow-benchmarks.yaml

Monitoring an MPI Job

Once the MPIJob resource is created, you should now be able to see the created pods matching the specified number of GPUs. You can also monitor the job status from the status section. Here is sample output when the job is successfully completed.

kubectl get -o yaml mpijobs tensorflow-benchmarks

apiVersion: kubeflow.org/v2beta1
kind: MPIJob
metadata:
  creationTimestamp: "2019-07-09T22:15:51Z"
  generation: 1
  name: tensorflow-benchmarks
  namespace: default
  resourceVersion: "5645868"
  selfLink: /apis/kubeflow.org/v1alpha2/namespaces/default/mpijobs/tensorflow-benchmarks
  uid: 1c5b470f-a297-11e9-964d-88d7f67c6e6d
spec:
  runPolicy:
    cleanPodPolicy: Running
  mpiReplicaSpecs:
    Launcher:
      replicas: 1
      template:
        spec:
          containers:
          - command:
            - mpirun
            - --allow-run-as-root
            - -np
            - "2"
            - -bind-to
            - none
            - -map-by
            - slot
            - -x
            - NCCL_DEBUG=INFO
            - -x
            - LDLIBRARYPATH
            - -x
            - PATH
            - -mca
            - pml
            - ob1
            - -mca
            - btl
            - ^openib
            - python
            - scripts/tfcnnbenchmarks/tfcnnbenchmarks.py
            - --model=resnet101
            - --batch_size=64
            - --variable_update=horovod
            image: mpioperator/tensorflow-benchmarks:latest
            name: tensorflow-benchmarks
    Worker:
      replicas: 1
      template:
        spec:
          containers:
          - image: mpioperator/tensorflow-benchmarks:latest
            name: tensorflow-benchmarks
            resources:
              limits:
                nvidia.com/gpu: 2
  slotsPerWorker: 2
status:
  completionTime: "2019-07-09T22:17:06Z"
  conditions:
  - lastTransitionTime: "2019-07-09T22:15:51Z"
    lastUpdateTime: "2019-07-09T22:15:51Z"
    message: MPIJob default/tensorflow-benchmarks is created.
    reason: MPIJobCreated
    status: "True"
    type: Created
  - lastTransitionTime: "2019-07-09T22:15:54Z"
    lastUpdateTime: "2019-07-09T22:15:54Z"
    message: MPIJob default/tensorflow-benchmarks is running.
    reason: MPIJobRunning
    status: "False"
    type: Running
  - lastTransitionTime: "2019-07-09T22:17:06Z"
    lastUpdateTime: "2019-07-09T22:17:06Z"
    message: MPIJob default/tensorflow-benchmarks successfully completed.
    reason: MPIJobSucceeded
    status: "True"
    type: Succeeded
  replicaStatuses:
    Launcher:
      succeeded: 1
    Worker: {}
  startTime: "2019-07-09T22:15:51Z"

Training should run for 100 steps and takes a few minutes on a GPU cluster. You can inspect the logs to see the training progress. When the job starts, access the logs from the launcher pod:

PODNAME=$(kubectl get pods -l training.kubeflow.org/job-name=tensorflow-benchmarks,training.kubeflow.org/job-role=launcher -o name)
kubectl logs -f ${PODNAME}

TensorFlow:  1.14
Model:       resnet101
Dataset:     imagenet (synthetic)
Mode:        training
SingleSess:  False
Batch size:  128 global
             64 per device
Num batches: 100
Num epochs:  0.01
Devices:     ['horovod/gpu:0', 'horovod/gpu:1']
NUMA bind:   False
Data format: NCHW
Optimizer:   sgd
Variables:   horovod
...
40	images/sec: 154.4 +/- 0.7 (jitter = 4.0)	8.280 40	images/sec: 154.4 +/- 0.7 (jitter = 4.1)	8.482 50	images/sec: 154.8 +/- 0.6 (jitter = 4.0)	8.397 50	images/sec: 154.8 +/- 0.6 (jitter = 4.2)	8.450 60	images/sec: 154.5 +/- 0.5 (jitter = 4.1)	8.321 60	images/sec: 154.5 +/- 0.5 (jitter = 4.4)	8.349 70	images/sec: 154.5 +/- 0.5 (jitter = 4.0)	8.433 70	images/sec: 154.5 +/- 0.5 (jitter = 4.4)	8.430 80	images/sec: 154.8 +/- 0.4 (jitter = 3.6)	8.199 80	images/sec: 154.8 +/- 0.4 (jitter = 3.8)	8.404 90	images/sec: 154.6 +/- 0.4 (jitter = 3.7)	8.418 90	images/sec: 154.6 +/- 0.4 (jitter = 3.6)	8.459 100	images/sec: 154.2 +/- 0.4 (jitter = 4.0)	8.372 100	images/sec: 154.2 +/- 0.4 (jitter = 4.0)	8.542 
 total images/sec: 308.27

For a sample that uses Intel MPI, see:

cat examples/v2beta1/pi/pi-intel.yaml

For a sample that uses MPICH, see:

cat examples/v2beta1/pi/pi-mpich.yaml

Exposed Metrics

| Metric name | Metric type | Description | Labels | | ----------- | ----------- | ----------- | ------ | |mpi\operator\jobs\created\total | Counter | Counts number of MPI jobs created | | |mpi\operator\jobs\successful\total | Counter | Counts number of MPI jobs successful | | |mpi\operator\jobs\failed\total | Counter | Counts number of MPI jobs failed| | |mpi\operator\job\_info | Gauge | Information about MPIJob | launcher=<launcher-pod-name>
namespace=<job-namespace> |

Join Metrics

With kube-state-metrics, one can join metrics by labels. For example kubepodinfo on(pod,namespace) groupleft labelreplace(mpioperatorjob_infos, "pod", "$0", "launcher", ".")

Docker Images

We push Docker images of mpioperator on Dockerhub for every release. You can use the following Dockerfile to build the image yourself:

• mpi-operator

make RELEASEVERSION=dev IMAGENAME=registry.example.com/mpi-operator images

This will produce an image with the tag registry.example.com/mpi-operator:dev.

Contributing

Learn more in CONTRIBUTING.