This operator has been merged into Kubeflow Training Operator. This repository is not maintained and has been archived.
MXNet Operator provides a Kubernetes custom resource MXJob that makes it easy to
run distributed or non-distributed Apache MXNet (incubating) jobs (training and tuning)
and other extended framework like BytePS jobs on Kubernetes.
Using a Custom Resource Definition (CRD) gives users the ability to create
and manage Apache MXNet jobs just like built-in K8S resources.
kustomize build manifests/overlays/v1 | kubectl apply -f -
Check that the Apache MXNet custom resource is installed via:
kubectl get crd
The output should include mxjobs.kubeflow.org like the following:
NAME AGE
...
mxjobs.kubeflow.org 4d
...
Check that the Apache MXNet operator is running via:
kubectl get pods
The output should include mxnet-operaror-xxx like the following:
NAME READY STATUS RESTARTS AGE
mxnet-operator-d466b46bc-xbqvs 1/1 Running 0 4m37s
You create a training job by defining a MXJob with MXTrain mode and then creating it with.
kubectl create -f examples/train/mx_job_dist_gpu_v1.yaml
Each replicaSpec defines a set of Apache MXNet processes.
The mxReplicaType defines the semantics for the set of processes.
The semantics are as follows:
scheduler
- A job must have 1 and only 1 scheduler
- The pod must contain a container named mxnet
- The overall status of the
MXJobis determined by the exit code of the mxnet container- 0 = success
- 1 || 2 || 126 || 127 || 128 || 139 = permanent errors:
- 1: general errors
- 2: misuse of shell builtins
- 126: command invoked cannot execute
- 127: command not found
- 128: invalid argument to exit
- 139: container terminated by SIGSEGV(Invalid memory reference)
- 130 || 137 || 143 = retryable error for unexpected system signals:
- 130: container terminated by Control-C
- 137: container received a SIGKILL
- 143: container received a SIGTERM
- 138 = reserved in tf-operator for user specified retryable errors
- others = undefined and no guarantee
worker
- A job can have 0 to N workers
- The pod must contain a container named mxnet
- Workers are automatically restarted if they exit
server
- A job can have 0 to N servers
- parameter servers are automatically restarted if they exit
For each replica you define a template which is a K8S PodTemplateSpec. The template allows you to specify the containers, volumes, etc... that should be created for each replica.
TVM is a end to end deep learning compiler stack, you can easily run AutoTVM with mxnet-operator. You can create a auto tuning job by define a type of MXTune job and then creating it with
kubectl create -f examples/tune/mx_job_tune_gpu_v1.yaml
Before you use the auto-tuning example, there is some preparatory work need to be finished in advance.
To let TVM tune your network, you should create a docker image which has TVM module.
Then, you need a auto-tuning script to specify which network will be tuned and set the auto-tuning parameters.
For more details, please see tutorials.
Finally, you need a startup script to start the auto-tuning program. In fact, mxnet-operator will set all the parameters as environment variables and the startup script need to reed these variable and then transmit them to auto-tuning script.
We provide an example under examples/tune/, tuning result will be saved in a log file like resnet-18.log in the example we gave. You can refer it for details.
MXNet Operator supports training with GPUs.
Please verify your image is available for distributed training with GPUs.
For example, if you have the following, MXNet Operator will arrange the pod to nodes to satisfy the GPU limit.
command: ["python"]
args: ["/incubator-mxnet/example/image-classification/train_mnist.py","--num-epochs","1","--num-layers","2","--kv-store","dist_device_sync","--gpus","0"]
resources:
limits:
nvidia.com/gpu: 1
To get the status of your job
kubectl get -o yaml mxjobs $JOBHere is sample output for an example job
apiVersion: kubeflow.org/v1
kind: MXJob
metadata:
creationTimestamp: 2021-03-24T15:37:27Z
generation: 1
name: mxnet-job
namespace: default
resourceVersion: "5123435"
selfLink: /apis/kubeflow.org/v1/namespaces/default/mxjobs/mxnet-job
uid: xx11013b-4a28-11e9-s5a1-704d7bb912f91
spec:
cleanPodPolicy: All
jobMode: MXTrain
mxReplicaSpecs:
Scheduler:
replicas: 1
restartPolicy: Never
template:
metadata:
creationTimestamp: null
spec:
containers:
- image: mxjob/mxnet:gpu
name: mxnet
ports:
- containerPort: 9091
name: mxjob-port
resources: {}
Server:
replicas: 1
restartPolicy: Never
template:
metadata:
creationTimestamp: null
spec:
containers:
- image: mxjob/mxnet:gpu
name: mxnet
ports:
- containerPort: 9091
name: mxjob-port
resources: {}
Worker:
replicas: 1
restartPolicy: Never
template:
metadata:
creationTimestamp: null
spec:
containers:
- args:
- /incubator-mxnet/example/image-classification/train_mnist.py
- --num-epochs
- "10"
- --num-layers
- "2"
- --kv-store
- dist_device_sync
- --gpus
- "0"
command:
- python
image: mxjob/mxnet:gpu
name: mxnet
ports:
- containerPort: 9091
name: mxjob-port
resources:
limits:
nvidia.com/gpu: "1"
status:
completionTime: 2021-03-24T09:25:11Z
conditions:
- lastTransitionTime: 2021-03-24T15:37:27Z
lastUpdateTime: 2021-03-24T15:37:27Z
message: MXJob mxnet-job is created.
reason: MXJobCreated
status: "True"
type: Created
- lastTransitionTime: 2021-03-24T15:37:27Z
lastUpdateTime: 2021-03-24T15:37:29Z
message: MXJob mxnet-job is running.
reason: MXJobRunning
status: "False"
type: Running
- lastTransitionTime: 2021-03-24T15:37:27Z
lastUpdateTime: 2021-03-24T09:25:11Z
message: MXJob mxnet-job is successfully completed.
reason: MXJobSucceeded
status: "True"
type: Succeeded
mxReplicaStatuses:
Scheduler: {}
Server: {}
Worker: {}
startTime: 2021-03-24T15:37:29ZThe first thing to note is the RuntimeId. This is a random unique
string which is used to give names to all the K8s resouces
(e.g Job controllers & services) that are created by the MXJob.
As with other K8S resources status provides information about the state of the resource.
phase - Indicates the phase of a job and will be one of
- Creating
- Running
- CleanUp
- Failed
- Done
state - Provides the overall status of the job and will be one of
- Running
- Succeeded
- Failed
For each replica type in the job, there will be a ReplicaStatus that
provides the number of replicas of that type in each state.
For each replica type, the job creates a set of K8s Job Controllers named
${REPLICA-TYPE}-${RUNTIME_ID}-${INDEX}
For example, if you have 2 servers and the runtime id is "76n0", then MXJob
will create the following two jobs:
server-76no-0
server-76no-1
Please refer to the this document for contributing guidelines.
Please check out Kubeflow community page for more information on how to get involved in our community.
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