TensorFlow Training (TFJob)
This page describes TFJob for training a machine learning model with TensorFlow.
What is TFJob?
TFJob is a Kubernetes
custom resource
to run TensorFlow training jobs on Kubernetes. The Kubeflow implementation of
TFJob is in training-operator.
Note: TFJob doesn’t work in a user namespace by default because of Istio automatic sidecar injection. In order to get TFJob running, it needs annotation sidecar.istio.io/inject: "false" to disable it for TFJob pods.
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: your-user-namespace
spec:
tfReplicaSpecs:
PS:
replicas: 1
restartPolicy: OnFailure
template:
metadata:
annotations:
sidecar.istio.io/inject: "false"
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:
metadata:
annotations:
sidecar.istio.io/inject: "false"
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: your-user-namespace
spec:
tfReplicaSpecs:
PS:
replicas: 1
restartPolicy: OnFailure
template:
metadata:
annotations:
sidecar.istio.io/inject: "false"
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:
metadata:
annotations:
sidecar.istio.io/inject: "false"
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.
- The pod must include a container named
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
tensorflowcontainer as follows:Exit code
0indicates 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 errors2: misuse of shell builtins126: command invoked cannot execute127: command not found128: invalid argument to exit139: 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 SIGKILL143: container received a SIGTERM
Exit code
138corresponds 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.
Installing TensorFlow Operator
If you haven’t already done so please follow the Getting Started Guide to deploy Kubeflow.
By default,
TFJobOperator will be deployed as a controller in training operator.
If you want to install a standalone version of the training operator without Kubeflow, see the kubeflow/training-operator’s README.
Verify that TFJob support is included in your Kubeflow deployment
Check that the TensorFlow custom resource is installed:
kubectl get crd
The output should include tfjobs.kubeflow.org like the following:
NAME CREATED AT
...
tfjobs.kubeflow.org 2021-09-06T18:33:58Z
...
Check that the Training operator is running via:
kubectl get pods -n kubeflow
The output should include training-operaror-xxx like the following:
NAME READY STATUS RESTARTS AGE
training-operator-d466b46bc-xbqvs 1/1 Running 0 4m37s
Running the Mnist example
See the manifests for the distributed MNIST example. You may change the config file based on your requirements.
Deploy the TFJob resource to start training:
kubectl create -f https://raw.githubusercontent.com/kubeflow/training-operator/master/examples/tensorflow/simple.yaml
Monitor the job (see the detailed guide below):
kubectl -n kubeflow get tfjob tfjob-simple -o yaml
Delete it
kubectl -n kubeflow delete tfjob tfjob-simple
Customizing the TFJob
Typically you can change the following values in the TFJob yaml file:
Change the image to point to the docker image containing your code
Change the number and types of replicas
Change the resources (requests and limits) assigned to each resource
Set any environment variables
- For example, you might need to configure various environment variables to talk to datastores like GCS or S3
Attach PVs if you want to use PVs for storage.
Using GPUs
To use GPUs your cluster must be configured to use GPUs.
- Nodes must have GPUs attached.
- The Kubernetes cluster must recognize the
nvidia.com/gpuresource type. - GPU drivers must be installed on the cluster.
- For more information:
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 -n kubeflow get -o yaml tfjobs tfjob-simple
Here is sample output for an example job
apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
creationTimestamp: "2021-09-06T11:48:09Z"
generation: 1
name: tfjob-simple
namespace: kubeflow
resourceVersion: "5764004"
selfLink: /apis/kubeflow.org/v1/namespaces/kubeflow/tfjobs/tfjob-simple
uid: 3a67a9a9-cb89-4c1f-a189-f49f0b581e29
spec:
tfReplicaSpecs:
Worker:
replicas: 2
restartPolicy: OnFailure
template:
spec:
containers:
- command:
- python
- /var/tf_mnist/mnist_with_summaries.py
image: gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
name: tensorflow
status:
completionTime: "2021-09-06T11:49:30Z"
conditions:
- lastTransitionTime: "2021-09-06T11:48:09Z"
lastUpdateTime: "2021-09-06T11:48:09Z"
message: TFJob tfjob-simple is created.
reason: TFJobCreated
status: "True"
type: Created
- lastTransitionTime: "2021-09-06T11:48:12Z"
lastUpdateTime: "2021-09-06T11:48:12Z"
message: TFJob kubeflow/tfjob-simple is running.
reason: TFJobRunning
status: "False"
type: Running
- lastTransitionTime: "2021-09-06T11:49:30Z"
lastUpdateTime: "2021-09-06T11:49:30Z"
message: TFJob kubeflow/tfjob-simple successfully completed.
reason: TFJobSucceeded
status: "True"
type: Succeeded
replicaStatuses:
Worker:
succeeded: 2
startTime: "2021-09-06T11:48:10Z"
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
TFJobhas 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
TFJobhave been successfully scheduled and launched and the job is running. - TFJobRestarting means one or more sub-resources (e.g. services/pods)
of this
TFJobhad a problem and is being restarted. - TFJobSucceeded means the job completed successfully.
- TFJobFailed means the job has failed.
- TFJobCreated means the
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
TFJobsucceeds 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
TFJobwill 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 -n kubeflow describe tfjobs tfjob-simple
which will produce output like
Name: tfjob-simple
Namespace: kubeflow
Labels: <none>
Annotations: <none>
API Version: kubeflow.org/v1
Kind: TFJob
Metadata:
Creation Timestamp: 2021-09-06T11:48:09Z
Generation: 1
Managed Fields:
API Version: kubeflow.org/v1
Fields Type: FieldsV1
fieldsV1:
f:spec:
.:
f:tfReplicaSpecs:
.:
f:Worker:
.:
f:replicas:
f:restartPolicy:
f:template:
.:
f:spec:
Manager: kubectl-create
Operation: Update
Time: 2021-09-06T11:48:09Z
API Version: kubeflow.org/v1
Fields Type: FieldsV1
fieldsV1:
f:spec:
f:runPolicy:
.:
f:cleanPodPolicy:
f:successPolicy:
f:tfReplicaSpecs:
f:Worker:
f:template:
f:metadata:
f:spec:
f:containers:
f:status:
.:
f:completionTime:
f:conditions:
f:replicaStatuses:
.:
f:Worker:
.:
f:succeeded:
f:startTime:
Manager: manager
Operation: Update
Time: 2021-09-06T11:49:30Z
Resource Version: 5764004
Self Link: /apis/kubeflow.org/v1/namespaces/kubeflow/tfjobs/tfjob-simple
UID: 3a67a9a9-cb89-4c1f-a189-f49f0b581e29
Spec:
Tf Replica Specs:
Worker:
Replicas: 2
Restart Policy: OnFailure
Template:
Spec:
Containers:
Command:
python
/var/tf_mnist/mnist_with_summaries.py
Image: gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
Name: tensorflow
Status:
Completion Time: 2021-09-06T11:49:30Z
Conditions:
Last Transition Time: 2021-09-06T11:48:09Z
Last Update Time: 2021-09-06T11:48:09Z
Message: TFJob tfjob-simple is created.
Reason: TFJobCreated
Status: True
Type: Created
Last Transition Time: 2021-09-06T11:48:12Z
Last Update Time: 2021-09-06T11:48:12Z
Message: TFJob kubeflow/tfjob-simple is running.
Reason: TFJobRunning
Status: False
Type: Running
Last Transition Time: 2021-09-06T11:49:30Z
Last Update Time: 2021-09-06T11:49:30Z
Message: TFJob kubeflow/tfjob-simple successfully completed.
Reason: TFJobSucceeded
Status: True
Type: Succeeded
Replica Statuses:
Worker:
Succeeded: 2
Start Time: 2021-09-06T11:48:10Z
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreatePod 7m9s tfjob-controller Created pod: tfjob-simple-worker-0
Normal SuccessfulCreatePod 7m8s tfjob-controller Created pod: tfjob-simple-worker-1
Normal SuccessfulCreateService 7m8s tfjob-controller Created service: tfjob-simple-worker-0
Normal SuccessfulCreateService 7m8s tfjob-controller Created service: tfjob-simple-worker-1
Normal ExitedWithCode 5m48s (x3 over 5m48s) tfjob-controller Pod: kubeflow.tfjob-simple-worker-1 exited with code 0
Normal ExitedWithCode 5m48s tfjob-controller Pod: kubeflow.tfjob-simple-worker-0 exited with code 0
Normal TFJobSucceeded 5m48s tfjob-controller TFJob kubeflow/tfjob-simple successfully completed.
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.job-name="${JOB_NAME}"
metadata.userLabels.replica-type="${TYPE}"
metadata.userLabels.replica-index="${INDEX}"
Alternatively using gcloud
QUERY="resource.type=\"k8s_container\" "
QUERY="${QUERY} resource.labels.cluster_name=\"${CLUSTER}\" "
QUERY="${QUERY} metadata.userLabels.job-name=\"${JOB_NAME}\" "
QUERY="${QUERY} metadata.userLabels.replica-type=\"${TYPE}\" "
QUERY="${QUERY} metadata.userLabels.replica-index=\"${INDEX}\" "
gcloud --project=${PROJECT} logging read \
--freshness=24h \
--order asc ${QUERY}
Troubleshooting
Here are some steps to follow to troubleshoot your job
Is a status present for your job? Run the command
kubectl -n ${USER_NAMESPACE} get tfjobs -o yaml ${JOB_NAME}USER_NAMESPACE is the namespace created for your user profile.
If the resulting output doesn’t include a status for your job then this typically indicates the job spec is invalid.
If the
TFJobspec is invalid there should be a log message in the tf operator logskubectl -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
TFJoboperator in.
- KUBEFLOW_NAMESPACE Is the namespace you deployed the
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 ${USER_NAMESPACE} describe tfjobs ${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 ---- ------ ---- ---- ------- Normal SuccessfulCreatePod 90s tfjob-controller Created pod: tfjob2-worker-0 Normal SuccessfulCreatePod 90s tfjob-controller Created pod: tfjob2-ps-0 Normal SuccessfulCreateService 90s tfjob-controller Created service: tfjob2-worker-0 Normal SuccessfulCreateService 90s tfjob-controller Created service: tfjob2-ps-0Kubernetes 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
TFJobisn’t being processed; common causes are- The
TFJobspec is invalid (see above) - The
TFJoboperator isn’t running
- The
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 ${USER_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 containerSome 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
- Explore the
TFJobreference documentation. - See how to run a job with gang-scheduling.
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