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.
You can deploy the operator with default settings by running the following commands:
git clone https://github.com/kubeflow/mpi-operator
cd mpi-operator
kubectl apply -f deploy/v2beta1/mpi-operator.yamlAlternatively, 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 so you can also run the following command instead:
Since Kubernetes v1.21, you can use:
kubectl apply -k manifests/overlays/kubeflowkubectl kustomize base | kubectl apply -f -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.yaml
Deploy the MPIJob resource to start training:
kubectl apply -f examples/v2beta1/tensorflow-benchmarks.yaml
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
- LD_LIBRARY_PATH
- -x
- PATH
- -mca
- pml
- ob1
- -mca
- btl
- ^openib
- python
- scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.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 mpi_job_name=tensorflow-benchmarks,mpi_role_type=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/pi/pi-intel.yaml| 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> |
With kube-state-metrics, one can join metrics by labels.
For example kube_pod_info * on(pod,namespace) group_left label_replace(mpi_operator_job_infos, "pod", "$0", "launcher", ".*")
We push Docker images of mpioperator on Dockerhub for every release. You can use the following Dockerfile to build the image yourself:
Alternative, you can build the image using make:
make RELEASE_VERSION=dev IMAGE_NAME=registry.example.com/mpi-operator imagesThis will produce an image with the tag registry.example.com/mpi-operator:dev.
Learn more in CONTRIBUTING.