The MPI Operator makes it easy to run allreduce-style distributed training.
If you haven’t already done so please 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:
cd ${KSONNET_APP}
ks pkg install kubeflow/mpi-job
ks generate mpi-operator mpi-operator
ks apply ${ENVIRONMENT} -c mpi-operator
Alternatively, you can deploy the operator with default settings without using ksonnet by running the following from the repo:
kubectl create -f deploy/You can create an MPI job by defining an MPIJob config file. See Tensorflow benchmark example config file for launching a multi-node TensorFlow benchmark training job. You may change the config file based on your requirements.
cat examples/tensorflow-benchmarks.yaml
Deploy the MPIJob resource to start training:
kubectl create -f examples/tensorflow-benchmarks.yaml
Once the MPIJob resource is created, you should now be able to see the created pods matching the specified number of GPUs. You can also monitor the job status from the status section. Here is sample output when the job is successfully completed.
kubectl get -o yaml mpijobs tensorflow-benchmarks-16
apiVersion: kubeflow.org/v1alpha1
kind: MPIJob
metadata:
clusterName: ""
creationTimestamp: 2019-01-07T20:32:12Z
generation: 1
name: tensorflow-benchmarks-16
namespace: default
resourceVersion: "185051397"
selfLink: /apis/kubeflow.org/v1alpha1/namespaces/default/mpijobs/tensorflow-benchmarks-16
uid: 8dc8c044-127d-11e9-a419-02420bbe29f3
spec:
gpus: 16
template:
metadata:
creationTimestamp: null
spec:
containers:
- image: mpioperator/tensorflow-benchmarks:latest
name: tensorflow-benchmarks
resources: {}
status:
launcherStatus: Succeeded
Training should run for 100 steps and takes a few minutes on a GPU cluster. You can inspect the logs to see the training progress. When the job starts, access the logs from the launcher pod:
PODNAME=$(kubectl get pods -l mpi_job_name=tensorflow-benchmarks-16,mpi_role_type=launcher -o name)
kubectl logs -f ${PODNAME}
TensorFlow: 1.10
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']
Data format: NCHW
Optimizer: sgd
Variables: horovod
...
40 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.146
40 images/sec: 132.1 +/- 0.0 (jitter = 0.1) 9.182
50 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.071
50 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.210
60 images/sec: 132.2 +/- 0.0 (jitter = 0.2) 9.180
60 images/sec: 132.2 +/- 0.0 (jitter = 0.2) 9.055
70 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.005
70 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.096
80 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.231
80 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.197
90 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.201
90 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.089
100 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.183
----------------------------------------------------------------
total images/sec: 264.26
----------------------------------------------------------------
100 images/sec: 132.1 +/- 0.0 (jitter = 0.2) 9.044
----------------------------------------------------------------
total images/sec: 264.26
----------------------------------------------------------------
Docker images are built and pushed automatically to mpioperator on Dockerhub. You can use the following Dockerfiles to build the images yourself: