TensorFlow Training (TFJob)

Using TFJob to train a model with TensorFlow

This page describes TFJob for training a machine learning model with TensorFlow.

What is TFJob?

TFJob is a Kubernetes custom resource that you can use to run TensorFlow training jobs on Kubernetes. The Kubeflow implementation of TFJob is in tf-operator.

A TFJob is a resource with a YAML representation like the one below (edit to use the container image and command for your own training code):

apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  generateName: tfjob
  namespace: kubeflow
spec:
  tfReplicaSpecs:
    PS:
      replicas: 1
      restartPolicy: OnFailure
      template:
        spec:
          containers:
          - name: tensorflow
            image: gcr.io/your-project/your-image
            command:
              - python
              - -m
              - trainer.task
              - --batch_size=32
              - --training_steps=1000
    Worker:
      replicas: 3
      restartPolicy: OnFailure
      template:
        spec:
          containers:
          - name: tensorflow
            image: gcr.io/your-project/your-image
            command:
              - python
              - -m
              - trainer.task
              - --batch_size=32
              - --training_steps=1000

If you want to give your TFJob pods access to credentials secrets, such as the GCP credentials automatically created when you do a GKE-based Kubeflow installation, you can mount and use a secret like this:

apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  generateName: tfjob
  namespace: kubeflow
spec:
  tfReplicaSpecs:
    PS:
      replicas: 1
      restartPolicy: OnFailure
      template:
        spec:
          containers:
          - name: tensorflow
            image: gcr.io/your-project/your-image
            command:
              - python
              - -m
              - trainer.task
              - --batch_size=32
              - --training_steps=1000
            env:
            - name: GOOGLE_APPLICATION_CREDENTIALS
              value: "/etc/secrets/user-gcp-sa.json"
            volumeMounts:
            - name: sa
              mountPath: "/etc/secrets"
              readOnly: true
          volumes:
          - name: sa
            secret:
              secretName: user-gcp-sa
    Worker:
      replicas: 1
      restartPolicy: OnFailure
      template:
        spec:
          containers:
          - name: tensorflow
            image: gcr.io/your-project/your-image
            command:
              - python
              - -m
              - trainer.task
              - --batch_size=32
              - --training_steps=1000
            env:
            - name: GOOGLE_APPLICATION_CREDENTIALS
              value: "/etc/secrets/user-gcp-sa.json"
            volumeMounts:
            - name: sa
              mountPath: "/etc/secrets"
              readOnly: true
          volumes:
          - name: sa
            secret:
              secretName: user-gcp-sa

If you are not familiar with Kubernetes resources please refer to the page Understanding Kubernetes Objects.

What makes TFJob different from built in controllers is the TFJob spec is designed to manage distributed TensorFlow training jobs.

A distributed TensorFlow job typically contains 0 or more of the following processes

  • Chief The chief is responsible for orchestrating training and performing tasks like checkpointing the model.
  • Ps The ps are parameter servers; these servers provide a distributed data store for the model parameters.
  • Worker The workers do the actual work of training the model. In some cases, worker 0 might also act as the chief.
  • Evaluator The evaluators can be used to compute evaluation metrics as the model is trained.

The field tfReplicaSpecs in TFJob spec contains a map from the type of replica (as listed above) to the TFReplicaSpec for that replica. TFReplicaSpec consists of 3 fields

  • replicas The number of replicas of this type to spawn for this TFJob.
  • template A PodTemplateSpec that describes the pod to create for each replica.

    • The pod must include a container named tensorflow.
  • restartPolicy Determines whether pods will be restarted when they exit. The allowed values are as follows

    • Always means the pod will always be restarted. This policy is good for parameter servers since they never exit and should always be restarted in the event of failure.
    • OnFailure means the pod will be restarted if the pod exits due to failure.

      • A non-zero exit code indicates a failure.
      • An exit code of 0 indicates success and the pod will not be restarted.
      • This policy is good for chief and workers.
    • ExitCode means the restart behavior is dependent on the exit code of the tensorflow container as follows:

      • Exit code 0 indicates the process completed successfully and will not be restarted.

      • The following exit codes indicate a permanent error and the container will not be restarted:

        • 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)
      • The following exit codes indicate a retryable error and the container will be restarted:

        • 130: container terminated by SIGINT (keyboard Control-C)
        • 137: container received a SIGKILL
        • 143: container received a SIGTERM
      • Exit code 138 corresponds to SIGUSR1 and is reserved for user-specified retryable errors.

      • Other exit codes are undefined and there is no guarantee about the behavior.

      For background information on exit codes, see the GNU guide to termination signals and the Linux Documentation Project.

    • Never means pods that terminate will never be restarted. This policy should rarely be used because Kubernetes will terminate pods for any number of reasons (e.g. node becomes unhealthy) and this will prevent the job from recovering.

Quick start

Submitting a TensorFlow training job

Note: Before submitting a training job, you should have deployed kubeflow to your cluster. Doing so ensures that the TFJob custom resource is available when you submit the training job.

Running the MNist example

Kubeflow ships with an example suitable for running a simple MNist model.

git clone https://github.com/kubeflow/tf-operator
cd tf-operator/examples/v1/mnist_with_summaries
# Deploy the event volume
kubectl apply -f tfevent-volume
# Submit the TFJob
kubectl apply -f tf_job_mnist.yaml

Monitor the job (see the detailed guide below):

kubectl -n kubeflow get tfjob mnist -o yaml

Delete it

kubectl -n kubeflow delete tfjob mnist

Customizing the TFJob

Typically you can change the following values in the TFJob yaml file:

  1. Change the image to point to the docker image containing your code
  2. Change the number and types of replicas
  3. Change the resources (requests and limits) assigned to each resource
  4. Set any environment variables

    • For example, you might need to configure various environment variables to talk to datastores like GCS or S3
  5. Attach PVs if you want to use PVs for storage.

Using GPUs

To use GPUs your cluster must be configured to use GPUs.

To attach GPUs specify the GPU resource on the container in the replicas that should contain the GPUs; for example.

apiVersion: "kubeflow.org/v1"
kind: "TFJob"
metadata:
  name: "tf-smoke-gpu"
spec:
  tfReplicaSpecs:
    PS:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
          - args:
            - python
            - tf_cnn_benchmarks.py
            - --batch_size=32
            - --model=resnet50
            - --variable_update=parameter_server
            - --flush_stdout=true
            - --num_gpus=1
            - --local_parameter_device=cpu
            - --device=cpu
            - --data_format=NHWC
            image: gcr.io/kubeflow/tf-benchmarks-cpu:v20171202-bdab599-dirty-284af3
            name: tensorflow
            ports:
            - containerPort: 2222
              name: tfjob-port
            resources:
              limits:
                cpu: '1'
            workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure
    Worker:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
          - args:
            - python
            - tf_cnn_benchmarks.py
            - --batch_size=32
            - --model=resnet50
            - --variable_update=parameter_server
            - --flush_stdout=true
            - --num_gpus=1
            - --local_parameter_device=cpu
            - --device=gpu
            - --data_format=NHWC
            image: gcr.io/kubeflow/tf-benchmarks-gpu:v20171202-bdab599-dirty-284af3
            name: tensorflow
            ports:
            - containerPort: 2222
              name: tfjob-port
            resources:
              limits:
                nvidia.com/gpu: 1
            workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure

Follow TensorFlow’s instructions for using GPUs.

Monitoring your job

To get the status of your job

kubectl get -o yaml tfjobs ${JOB}

Here is sample output for an example job

apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"kubeflow.org/v1","kind":"TFJob","metadata":{"annotations":{},"name":"mnist","namespace":"kubeflow"},"spec":{"cleanPodPolicy":"None","tfReplicaSpecs":{"Worker":{"replicas":1,"restartPolicy":"Never","template":{"spec":{"containers":[{"command":["python","/var/tf_mnist/mnist_with_summaries.py","--log_dir=/train","--learning_rate=0.01","--batch_size=150"],"image":"gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0","name":"tensorflow","volumeMounts":[{"mountPath":"/train","name":"training"}]}],"volumes":[{"name":"training","persistentVolumeClaim":{"claimName":"tfevent-volume"}}]}}}}}}
  creationTimestamp: "2019-07-16T02:44:38Z"
  generation: 1
  name: mnist
  namespace: kubeflow
  resourceVersion: "10429537"
  selfLink: /apis/kubeflow.org/v1/namespaces/kubeflow/tfjobs/mnist
  uid: a77b9fb4-a773-11e9-91fe-42010a960094
spec:
  cleanPodPolicy: None
  tfReplicaSpecs:
    Worker:
      replicas: 1
      restartPolicy: Never
      template:
        spec:
          containers:
          - command:
            - python
            - /var/tf_mnist/mnist_with_summaries.py
            - --log_dir=/train
            - --learning_rate=0.01
            - --batch_size=150
            image: gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
            name: tensorflow
            volumeMounts:
            - mountPath: /train
              name: training
          volumes:
          - name: training
            persistentVolumeClaim:
              claimName: tfevent-volume
status:
  completionTime: "2019-07-16T02:45:23Z"
  conditions:
  - lastTransitionTime: "2019-07-16T02:44:38Z"
    lastUpdateTime: "2019-07-16T02:44:38Z"
    message: TFJob mnist is created.
    reason: TFJobCreated
    status: "True"
    type: Created
  - lastTransitionTime: "2019-07-16T02:45:20Z"
    lastUpdateTime: "2019-07-16T02:45:20Z"
    message: TFJob mnist is running.
    reason: TFJobRunning
    status: "True"
    type: Running
  replicaStatuses:
    Worker:
      running: 1
  startTime: "2019-07-16T02:44:38Z"

Conditions

A TFJob has a TFJobStatus, which has an array of TFJobConditions through which the TFJob has or has not passed. Each element of the TFJobCondition array has six possible fields:

  • The lastUpdateTime field provides the last time this condition was updated.
  • The lastTransitionTime field provides the last time the condition transitioned from one status to another.
  • The message field is a human readable message indicating details about the transition.
  • The reason field is a unique, one-word, CamelCase reason for the condition’s last transition.
  • The status field is a string with possible values “True”, “False”, and “Unknown”.
  • The type field is a string with the following possible values:
    • TFJobCreated means the tfjob has been accepted by the system, but one or more of the pods/services has not been started.
    • TFJobRunning means all sub-resources (e.g. services/pods) of this TFJob have been successfully scheduled and launched and the job is running.
    • TFJobRestarting means one or more sub-resources (e.g. services/pods) of this TFJob had a problem and is being restarted.
    • TFJobSucceeded means the job completed successfully.
    • TFJobFailed means the job has failed.

Success or failure of a job is determined as follows

  • If a job has a chief success or failure is determined by the status of the chief.
  • If a job has no chief success or failure is determined by the workers.
  • In both cases the TFJob succeeds if the process being monitored exits with exit code 0.
  • In the case of non-zero exit code the behavior is determined by the restartPolicy for the replica.
  • If the restartPolicy allows for restarts then the process will just be restarted and the TFJob will continue to execute.
    • For the restartPolicy ExitCode the behavior is exit code dependent.
    • If the restartPolicy doesn’t allow restarts a non-zero exit code is considered a permanent failure and the job is marked failed.

tfReplicaStatuses

tfReplicaStatuses provides a map indicating the number of pods for each replica in a given state. There are three possible states

  • Active is the number of currently running pods.
  • Succeeded is the number of pods that completed successfully.
  • Failed is the number of pods that completed with an error.

Events

During execution, TFJob will emit events to indicate whats happening such as the creation/deletion of pods and services. Kubernetes doesn’t retain events older than 1 hour by default. To see recent events for a job run

kubectl describe tfjobs ${JOB}

which will produce output like

Name:         mnist
Namespace:    kubeflow
Labels:       <none>
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"kubeflow.org/v1","kind":"TFJob","metadata":{"annotations":{},"name":"mnist","namespace":"kubeflow"},"spec":{"cleanPodPolicy...
API Version:  kubeflow.org/v1
Kind:         TFJob
Metadata:
  Creation Timestamp:  2019-07-16T02:44:38Z
  Generation:          1
  Resource Version:    10429537
  Self Link:           /apis/kubeflow.org/v1/namespaces/kubeflow/tfjobs/mnist
  UID:                 a77b9fb4-a773-11e9-91fe-42010a960094
Spec:
  Clean Pod Policy:  None
  Tf Replica Specs:
    Worker:
      Replicas:        1
      Restart Policy:  Never
      Template:
        Spec:
          Containers:
            Command:
              python
              /var/tf_mnist/mnist_with_summaries.py
              --log_dir=/train
              --learning_rate=0.01
              --batch_size=150
            Image:  gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
            Name:   tensorflow
            Volume Mounts:
              Mount Path:  /train
              Name:        training
          Volumes:
            Name:  training
            Persistent Volume Claim:
              Claim Name:  tfevent-volume
Status:
  Completion Time:  2019-07-16T02:45:23Z
  Conditions:
    Last Transition Time:  2019-07-16T02:44:38Z
    Last Update Time:      2019-07-16T02:44:38Z
    Message:               TFJob mnist is created.
    Reason:                TFJobCreated
    Status:                True
    Type:                  Created
    Last Transition Time:  2019-07-16T02:45:20Z
    Last Update Time:      2019-07-16T02:45:20Z
    Message:               TFJob mnist is running.
    Reason:                TFJobRunning
    Status:                True
    Type:                  Running
  Replica Statuses:
    Worker:
      Running:  1
  Start Time:  2019-07-16T02:44:38Z
Events:
  Type    Reason                   Age    From         Message
  ----    ------                   ----   ----         -------
  Normal  SuccessfulCreatePod      8m6s   tf-operator  Created pod: mnist-worker-0
  Normal  SuccessfulCreateService  8m6s   tf-operator  Created service: mnist-worker-0

Here the events indicate that the pods and services were successfully created.

TensorFlow Logs

Logging follows standard K8s logging practices.

You can use kubectl to get standard output/error for any pods that haven’t been deleted.

First find the pod created by the job controller for the replica of interest. Pods will be named

${JOBNAME}-${REPLICA-TYPE}-${INDEX}

Once you’ve identified your pod you can get the logs using kubectl.

kubectl logs ${PODNAME}

The CleanPodPolicy in the TFJob spec controls deletion of pods when a job terminates. The policy can be one of the following values

  • The Running policy means that only pods still running when a job completes (e.g. parameter servers) will be deleted immediately; completed pods will not be deleted so that the logs will be preserved. This is the default value.
  • The All policy means all pods even completed pods will be deleted immediately when the job finishes.
  • The None policy means that no pods will be deleted when the job completes.

If your cluster takes advantage of Kubernetes cluster logging then your logs may also be shipped to an appropriate data store for further analysis.

Stackdriver on GKE

See the guide to logging and monitoring for instructions on getting logs using Stackdriver.

As described in the guide to logging and monitoring, it’s possible to fetch the logs for a particular replica based on pod labels.

Using the Stackdriver UI you can use a query like

resource.type="k8s_container"
resource.labels.cluster_name="${CLUSTER}"
metadata.userLabels.tf_job_name="${JOB_NAME}"
metadata.userLabels.tf-replica-type="${TYPE}"
metadata.userLabels.tf-replica-index="${INDEX}"

Alternatively using gcloud

QUERY="resource.type=\"k8s_container\" "
QUERY="${QUERY} resource.labels.cluster_name=\"${CLUSTER}\" "
QUERY="${QUERY} metadata.userLabels.tf_job_name=\"${JOB_NAME}\" "
QUERY="${QUERY} metadata.userLabels.tf-replica-type=\"${TYPE}\" "
QUERY="${QUERY} metadata.userLabels.tf-replica-index=\"${INDEX}\" "
gcloud --project=${PROJECT} logging read  \
     --freshness=24h \
     --order asc  ${QUERY}        

Troubleshooting

Here are some steps to follow to troubleshoot your job

  1. Is a status present for your job? Run the command

    kubectl -n ${NAMESPACE} get tfjobs -o yaml ${JOB_NAME}
    • If the resulting output doesn’t include a status for your job then this typically indicates the job spec is invalid.

    • If the TFJob spec is invalid there should be a log message in the tf operator logs

      kubectl -n ${KUBEFLOW_NAMESPACE} logs `kubectl get pods --selector=name=tf-job-operator -o jsonpath='{.items[0].metadata.name}'` 
      
      • KUBEFLOW_NAMESPACE Is the namespace you deployed the TFJob operator in.
    • Check the events for your job to see if the pods were created

    • There are a number of ways to get the events; if your job is less than 1 hour old then you can do

      kubectl -n ${NAMESPACE} describe tfjobs -o yaml ${JOB_NAME}
      
    • The bottom of the output should include a list of events emitted by the job; e.g.

    Events:
    Type     Reason                          Age                From         Message
    ----     ------                          ----               ----         -------
    Warning  SettedPodTemplateRestartPolicy  19s (x2 over 19s)  tf-operator  Restart policy in pod template will be overwritten by restart policy in replica spec
    Normal   SuccessfulCreatePod             19s                tf-operator  Created pod: tfjob2-worker-0
    Normal   SuccessfulCreateService         19s                tf-operator  Created service: tfjob2-worker-0
    Normal   SuccessfulCreatePod             19s                tf-operator  Created pod: tfjob2-ps-0
    Normal   SuccessfulCreateService         19s                tf-operator  Created service: tfjob2-ps-0
    • Kubernetes only preserves events for 1 hour (see kubernetes/kubernetes#52521)

      • Depending on your cluster setup events might be persisted to external storage and accessible for longer periods
      • On GKE events are persisted in stackdriver and can be accessed using the instructions in the previous section.
    • If the pods and services aren’t being created then this suggests the TFJob isn’t being processed; common causes are

      • The TFJob spec is invalid (see above)
      • The TFJob operator isn’t running
  2. Check the events for the pods to ensure they are scheduled.

    • There are a number of ways to get the events; if your pod is less than 1 hour old then you can do

      kubectl -n ${NAMESPACE} describe pods ${POD_NAME}
      
    • The bottom of the output should contain events like the following

    Events:
    Type    Reason                 Age   From                                                  Message
    ----    ------                 ----  ----                                                  -------
    Normal  Scheduled              18s   default-scheduler                                     Successfully assigned tfjob2-ps-0 to gke-jl-kf-v0-2-2-default-pool-347936c1-1qkt
    Normal  SuccessfulMountVolume  17s   kubelet, gke-jl-kf-v0-2-2-default-pool-347936c1-1qkt  MountVolume.SetUp succeeded for volume "default-token-h8rnv"
    Normal  Pulled                 17s   kubelet, gke-jl-kf-v0-2-2-default-pool-347936c1-1qkt  Container image "gcr.io/kubeflow/tf-benchmarks-cpu:v20171202-bdab599-dirty-284af3" already present on machine
    Normal  Created                17s   kubelet, gke-jl-kf-v0-2-2-default-pool-347936c1-1qkt  Created container
    Normal  Started                16s   kubelet, gke-jl-kf-v0-2-2-default-pool-347936c1-1qkt  Started container
    • Some common problems that can prevent a container from starting are
      • Insufficient resources to schedule the pod
      • The pod tries to mount a volume (or secret) that doesn’t exist or is unavailable
      • The docker image doesn’t exist or can’t be accessed (e.g due to permission issues)
    • If the containers start; check the logs of the containers following the instructions in the previous section.

    More information