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KEP-2170: Adding validation webhook for v2 trainjob #2307
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Thank you for taking this, and moving this forward.
And Sorry for the delay.
Namespace: new.Namespace, | ||
Name: new.Spec.RuntimeRef.Name, |
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Have you ever seen the isseus when we use the old object names?
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Why do we get new
object here and not old
?
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Here I am validating updated object instead of the existing one
@@ -140,3 +143,115 @@ func (j *JobSet) ReconcilerBuilders() []runtime.ReconcilerBuilder { | |||
}, | |||
} | |||
} | |||
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func (j *JobSet) Validate(oldObj, newObj *kubeflowv2.TrainJob, runtimeInfo *runtime.Info) (admission.Warnings, field.ErrorList) { |
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It seems that there are some conflicts between @andreyvelich PR and this.
@akshaychitneni Could you consult with @andreyvelich, then which PRs should we merge into the main, first.
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I rebased with @andreyvelich's changes
@@ -31,7 +31,7 @@ func Setup(mgr ctrl.Manager, runtimes map[string]runtime.Runtime) (string, error | |||
return kubeflowv2.TrainingRuntimeKind, err | |||
} | |||
if err := setupWebhookForTrainJob(mgr, runtimes); err != nil { | |||
return "TrainJob", err | |||
return kubeflowv2.TrainJobKind, err |
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Nice catch.
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Cool! This is what I imagined architechture in my KubeflowJobPipeline framework design phase.
failedCtrlName, err := controllerv2.SetupControllers(mgr, runtimes) | ||
gomega.ExpectWithOffset(1, err).NotTo(gomega.HaveOccurred(), "controller", failedCtrlName) | ||
gomega.ExpectWithOffset(1, failedCtrlName).To(gomega.BeEmpty()) | ||
if startControllers { |
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Have you ever seen any issues like null pointer when we start the controllers for webhook testing, right?
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I think I have seen but we might not need to start the controllers just to validate create/update requests and leave to reconciler tests to cover reconciliation
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Thank you for this effort @akshaychitneni!
I left initial comments.
// JobExporter is the Job name for the exporter. | ||
JobExporter string = "exporter" | ||
|
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Please can we implement the validation for exporter in the future once we design it as part of: #2245 ?
We should discuss whether we want to use sidecar container or another ReplicatedJob for model checkpointing.
cc @saileshd1402 @akshaychitneni @tenzen-y
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Ack. Makes sense
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@akshaychitneni Please can you remove the values from your PR that we will not use for now (e.g. JobExporter).
return r.framework.RunComponentBuilderPlugins(ctx, jobSetTemplate.DeepCopy(), info, trainJob) | ||
} | ||
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func (r *TrainingRuntime) runtimeInfo( |
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Should this be part of Runtime interface:
type Runtime interface { |
And should we name this API more explicit (e.g.
getRuntimeInfo()
or initializeRuntimeInfo()
) ?
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I think it should be part of trainingRuntime as it depends on config from trainJob/trainingRuntume resources
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Yeah, but the Info object will be used for every runtime that we register with our manager.
What is the main motivation to create this helper function to construct the Info object for the TrainingRuntime ?
Namespace: new.Namespace, | ||
Name: new.Spec.RuntimeRef.Name, |
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Why do we get new
object here and not old
?
numProcPerNodePath := specPath.Child("trainer").Child("numProcPerNode") | ||
if runtimeInfo.MLPolicy.MPI != nil { | ||
if _, err := strconv.Atoi(*newJobObj.Spec.Trainer.NumProcPerNode); err != nil { | ||
allErrs = append(allErrs, field.Invalid(numProcPerNodePath, newJobObj.Spec.Trainer.NumProcPerNode, "should have an int value")) |
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I think so, is this value compatible with the k8s API conventions: https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/api-conventions.md ?
numProcPerNodePath := specPath.Child("trainer").Child("numProcPerNode") | ||
if runtimeInfo.RuntimePolicy.MLPolicy.Torch != nil && newObj.Spec.Trainer.NumProcPerNode != nil { | ||
allowedStringValList := []string{"auto", "cpu", "gpu"} | ||
numProcPerNode := *newObj.Spec.Trainer.NumProcPerNode |
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@akshaychitneni @tenzen-y Can't we use CEL for that validation since we just validate values for .nProcPerNode
parameter ?
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I have included CEL validation for this path in trainingRuntimes #2313 but CEL can't be added here for trainJob config as it is requires referenced trainingRuntime config to validate
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Hmm, I see. Do we mean that in TrainJob NumProcPerNode can be different depends on the runtimeRef ?
E.g. for MPI we accept only int
values, but for Torch we accept auto
, cpu
, gpu
, and int
values.
return nil, nil | ||
} | ||
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if newObj.Spec.ModelConfig != nil && newObj.Spec.ModelConfig.Input != nil { |
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I think, for now we should check the initContainers in JobSet, as I mentioned here: https://github.com/kubeflow/training-operator/blob/master/pkg/runtime.v2/framework/plugins/jobset/builder.go#L87-L89
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I am checking the initContainers here https://github.com/kubeflow/training-operator/pull/2307/files#diff-935da6e0f990201db2f6ddf15c768526f70993d5a2408814013e96e3fedd5ebfR165. The condition here is only to check presence to initializer job if input modelconfig or dataset config is present in the trainJob
gomega.Expect(k8sClient.DeleteAllOf(ctx, &kubeflowv2.TrainJob{}, client.InNamespace(ns.Name))).To(gomega.Succeed()) | ||
}) | ||
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ginkgo.When("Creating TrainJob", func() { |
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@tenzen-y @akshaychitneni What is right way to test our validations with integration or unit tests ?
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I think integration tests might be helpful in this case as functioning of trainjob webhook relies on dependent objects like trainingRuntime
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fixing runtime Signed-off-by: Akshay Chitneni <[email protected]>
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Adds validation webhook for v2 trainjob.
Relates to #2209
What this PR does / why we need it:
Which issue(s) this PR fixes (optional, in
Fixes #<issue number>, #<issue number>, ...
format, will close the issue(s) when PR gets merged):Fixes # #2209
Checklist: