MPI Training
Alpha
This Kubeflow component has alpha status with limited support. See the Kubeflow versioning policies. The Kubeflow team is interested in your feedback about the usability of the feature.This guide walks you through using MPI for training.
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:
git clone https://github.com/kubeflow/mpi-operator
cd mpi-operator
kubectl create -f deploy/v1alpha2/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/manifests
kustomize build 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:
kubectl kustomize base | 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/v1alpha2/tensorflow-benchmarks.yaml
Deploy the MPIJob
resource to start training:
kubectl create -f examples/v1alpha2/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/v1alpha2
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:
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
Docker Images
Docker images are built and pushed automatically to mpioperator on Dockerhub. You can use the following Dockerfiles to build the images yourself:
Feedback
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.