[RayJob] Fix RayJob status reconciliation #1539

astefanutti · 2023-10-17T13:10:45Z

Why are these changes needed?

This PR fixes a number of issues related to the reconciliation of the RayJob status.

It also sets RayJob resources as owners of the Kubernetes Jobs that submit Ray jobs via the CLI.

It also addresses the issue of systematic failure upon retry of the job submission reported in #1480, by disabling submission retry altogether, until a proper solution is found.
The simplest solution would be to expose the job override option to the CLI, though this requires more thoughts and can be addressed in a separate PR:
https://github.com/ray-project/ray/blob/56affb7e4b5af8b1da7a322fdc6ecc7e2258d522/dashboard/modules/job/job_manager.py#L919

Related issue numbers

Fixes #1478.
Fixes #1480.

Checks

I've made sure the tests are passing.
Testing Strategy
- Unit tests
- Manual tests
- This PR is not tested :(

astefanutti · 2023-10-17T13:20:46Z

@z103cb @kevin85421 could you please review?

For the submission id issue upon retry, pointed out in #1480, I suggest we create a separate issue so we can take it from there.

astefanutti · 2023-10-18T11:07:20Z

@z103cb I've added a RayJob with cluster selector sample in 2a56347 as you suggested, and that covers the use cases related to your issues.

z103cb

@astefanutti thank you for adding the example. This PR is LGTM. I have valided that this PR fixes the underlying issues.

kevin85421

Thank you for the contribution! Would you mind adding some tests once we achieve the consensus for the code change? Thanks!

ray-operator/main.go

kevin85421 · 2023-10-19T07:58:26Z

ray-operator/main.go

@@ -126,15 +131,25 @@ func main() {
 		LeaderElectionID:       "ray-operator-leader",
 	}

+	selectorsByObject, err := cacheSelectors()


Would you mind explaining why do we need this?

Currently the informer for batch Jobs resources watches all the Jobs, cluster-wide most of the time. This is not needed, as KubeRay is only interested in the Jobs it creates, and it can raise scalability concerns both for KubeRay (size of the informer cache) and the API server / etcd (watch events).

controller-runtime provide that mechanism to narrow the scope of the informer caches.

What's the difference between

cache.MultiNamespacedCacheBuilder(watchNamespaces)

and

cache.MultiNamespacedCacheBuilder(watchNamespaces)(config, opts)

?

As controller-runtime currently wires the cache options, using:

cache.MultiNamespacedCacheBuilder(watchNamespaces)(config, opts)

is the only way to get the SelectorsByObject option set on the cache when it's instantiated by the manager. That may be changed once this option is exposed at the manager options level.

I need to take a look at what is SelectorsByObject before I resolve this thread.

This was a helpful thread!

Currently the informer for batch Jobs resources watches all the Jobs, cluster-wide most of the time. This is not needed, as KubeRay is only interested in the Jobs it creates, and it can raise scalability concerns both for KubeRay (size of the informer cache) and the API server / etcd (watch events).

controller-runtime provide that mechanism to narrow the scope of the informer caches.

It would be great to include a summary of this as code comment.

Inlined as code comment. PTAL.

Currently the informer for batch Jobs resources watches all the Jobs, cluster-wide most of the time.

I want to double-check this statement. Are you saying that the informer still watches K8s Jobs from all namespaces even if we specify only one namespace in watchNamespaces?

I meant that when the watch-namespace CLI option is not provided, which I assumed is the most general case, then the informer watches all the Kubernetes Jobs for all namespaces.

kevin85421 · 2023-10-19T08:01:23Z

ray-operator/controllers/ray/rayjob_controller.go

 		},
 	}

 	// Set the ownership in order to do the garbage collection by k8s.
-	if err := ctrl.SetControllerReference(rayClusterInstance, job, r.Scheme); err != nil {
+	if err := ctrl.SetControllerReference(rayJobInstance, job, r.Scheme); err != nil {


We might implement retry logic for RayJob in the future. If RayJob retries, it will spawn a new RayCluster and a new K8s Job. Therefore, the controller for the K8s Job should be the RayCluster.

I think this will require more thoughts, depending on the evolution of the retry mechanism, and the RayCluster reuse case. Yet at the moment, as a user, I find it surprising that the Job is not cascade-deleted when I delete the RayJob.

Yet at the moment, as a user, I find it surprising that the Job is not cascade-deleted when I delete the RayJob.

Really? It should be deleted. If not, it is a bug. RayJob is the controller reference of the RayCluster, and the RayCluster is the controller reference of the Kubernetes Job. Hence, I expected that the K8s Job should be removed if RayJob is deleted.

Not in the case the RayJob does not own the RayCluster, that is when it's created with a clusterSelector field.

I conducted an experiment, and I do not observe the behavior.

Not in the case the RayJob does not own the RayCluster, that is when it's created with a clusterSelector field.

Got it. We do not encourage users to use that.

I may deprecate it in the future.

You can ignore the comment at https://github.com/ray-project/kuberay/pull/1539/files#r1365854531. I hadn't seen your reply when I wrote mine.

kevin85421 · 2023-10-19T08:05:05Z

ray-operator/controllers/ray/rayjob_controller.go

-			Template: submitterTemplate,
+			// The `ray job submit` command fails when submission is retried with the same job ID.
+			// Let's disable backoff retry, until job submission retry effectively works.
+			BackoffLimit: pointer.Int32(0),


We can reduce it, but there will still be cases where the RayCluster doesn't receive the request from the K8s Job.

This may depending on the evolution of the retry mechanism longer term, but pragmatically, in the short term, I guess it depends on how likely pre-submission failures are compared to (recoverable) post-submission ones. If the former are more likely, then it'd probably be useful to keep retrying. So maybe a middle ground is to reduce the number of retries from the default, six, to something like two or three. Still it feels like it should be possible to re-submit a job, leveraging the override option from the Ray job manager.

So maybe a middle ground is to reduce the number of retries from the default, six, to something like two or three.

Makes sense.

Still it feels like it should be possible to re-submit a job, leveraging the override option from the Ray job manager.

I cannot get the point. Would you mind explaining it?

Still it feels like it should be possible to re-submit a job, leveraging the override option from the Ray job manager.

I cannot get the point. Would you mind explaining it?

If the submitter job fails "post-submission", i.e., after the first successful ray job submit command attempt, any subsequent retry will fail with the Job with submission_id [...] already exists. error. If the command could be changed to ray job submit --overwrite=true, then it'll fix the issue.

It seems the Ray Job manager has an overwrite option: https://github.com/ray-project/ray/blob/56affb7e4b5af8b1da7a322fdc6ecc7e2258d522/dashboard/modules/job/job_manager.py#L919, though it's not currently exposed in the API and CLI.

We can consider exposing an overwrite argument in Ray, but we'll need to think about the design and questions about what happens to the old job. For now I agree with disabling retries.

The default submitter pod template has RestartPolicyNever, do you happen to know why it was still getting retried before this PR?

I replied here: #1539 (review)

The default submitter pod template has RestartPolicyNever, do you happen to know why it was still getting retried before this PR?

@architkulkarni RestartPolicy controls the kubelet behaviour when a container exits within a Pod. With RestartPolicyNever the Pod is failed, while with RestartPolicyAlways for example, the container is restarted in-place. BackoffLimit on the hand controls the Job controller behaviour on Pod failure, specifically the maximum number of time a Job Pod should be recreated before failing the Job. So you can have RestartPolicyNever and still have the ray job submission retried when BackoffLimit (which defaults to 6) is not zero.

kevin85421 · 2023-10-19T08:16:14Z

ray-operator/config/samples/ray-job.cluster-selector.yaml

+metadata:
+  name: rayjob-cluster-selector
+spec:
+  clusterSelector:


We do not encourage users to use clusterSelector. The better option is to use ray job submit to submit a ray job to a RayCluster.

OK, so I guess this sample, updated without clusterSelector, would not add much value compared to the existing ones, and should be removed. @z103cb you OK with that?

z103cb · 2023-10-20T07:32:57Z

OK, so I guess this sample, updated without clusterSelector, would not add much value compared to the existing ones, and should be removed. @z103cb you OK with that?

I think for as long the option exists in the operator (i.e is not officially deprecated), we should keep this there. Perhaps adding adding a comment in the sample to the effect "Please don't use this, we will deperecate it in the future" would be OK.

@kevin85421 is the encouragement documented somewhere in the operator? I am wondering if I have missed it.

z103cb · 2023-10-20T07:37:51Z

I conducted an experiment, and I do not observe the behavior.

The issue is not related to the "submit job with cluster". In my own experiments that works fine. The issue is around submitting a ray job against a running cluster using the "cluster selector" option. I have documeted the ways to reproduce the bug(s) this PR addresses in the linked issues. I think is best to address the bug, so things work as expected for the user.

The usefullness of the "job with cluster selector" is debatable, but until is officially deprecated the feature should work.

kevin85421

Reply https://github.com/ray-project/kuberay/pull/1539/files#r1366631875:

We specify the submission ID to prevent the same job from being submitted twice in certain edge cases. Any job submission with a duplicate ID will be rejected.

The Kubernetes Job backoff limit aims to ensure that each Ray job is submitted to the RayCluster exactly once. KubeRay has very limited information about the failure: is the failure related to the accessibility of the Ray head?

Case 1: If the failure is not related to the accessibility of the Ray head, the possible causes may be:
- Case 1-1 application-level: Users' Ray scripts have some bugs. In this case, we should not submit a new job with different submission ID or overwrite it.
- Case 1-2 system-level: For example, a job fails due to the crash of a Ray worker Pod. Users should handle the retry from the application-level logic.

Your sentences are mostly clear. Here's a slightly refined version:

Case 2: The Ray head is inaccessible.
- Case 2-1: Ray head becomes inaccessible before receiving the job submission request: This situation might arise from a network issue between the K8s Job and the Ray head. The K8s Job's backoffLimit is designed to handle such cases.
- Case 2-2: Ray head crashes after receiving the job submission: Since GCS fault tolerance only supports Ray Serve, the job's information will be lost if the Ray head crashes. Consequently, using the same submission ID will not lead to a rejection. This can be relieved by the backoffLimit or we may implement a retry to delete RayCluster + Job and restart a new RayCluster + Job.
- Case 2-3: The network between Ray head and K8s Job is disconnected after the Ray head begins executing the job: Job submissions with the same submission ID will be rejected to ensure that only one job runs in the cluster at any given time.

kevin85421

Hi @astefanutti @z103cb,

Could you please

(1) Remove the sample YAML: This encourages users to use clusterSelector, which we discourage and may deprecate in the future. Including it in the YAML could complicate potential deprecation.

(2) Set the BackoffLimit to non-zero.

I don't want to introduce any new changes related to clusterSelector, such as changing the controller reference or checking the length of ClusterSelector, but the points from you guys also make sense. I am fine with the related code changes, but please remove the sample YAML before the community has a consensus of RayJob in the future.

I will start working on RayJob next week. I hope to begin once this PR is merged to avoid any potential conflicts you might have to resolve. Thanks!

architkulkarni

Thanks for the fix! My main question is about the decision to only reconcile if the job is pending or running.

Also, could you say more about the cause of #1478 "When submitting a KubeRay job (ray job CRD) to be executed on a previously provisioned cluster, the ray job status is not set to v1alpha1.JobStatusSucceeded." and how this PR fixes it?

architkulkarni · 2023-10-20T17:11:59Z

ray-operator/config/samples/ray-job.cluster-selector.yaml

+  clusterSelector:
+    ray.io/cluster: long-running-cluster
+  entrypoint: python /home/ray/samples/sample_code.py
+#  entrypoint: python /home/ray/samples/fail_fast.py


Suggested change

# entrypoint: python /home/ray/samples/fail_fast.py

architkulkarni · 2023-10-20T17:25:30Z

ray-operator/controllers/ray/rayjob_controller.go

-			Template: submitterTemplate,
+			// The `ray job submit` command fails when submission is retried with the same job ID.
+			// Let's disable backoff retry, until job submission retry effectively works.
+			BackoffLimit: pointer.Int32(0),


We can consider exposing an overwrite argument in Ray, but we'll need to think about the design and questions about what happens to the old job. For now I agree with disabling retries.

The default submitter pod template has RestartPolicyNever, do you happen to know why it was still getting retried before this PR?

architkulkarni · 2023-10-20T20:53:47Z

ray-operator/main.go

@@ -126,15 +131,25 @@ func main() {
 		LeaderElectionID:       "ray-operator-leader",
 	}

+	selectorsByObject, err := cacheSelectors()


This was a helpful thread!

Currently the informer for batch Jobs resources watches all the Jobs, cluster-wide most of the time. This is not needed, as KubeRay is only interested in the Jobs it creates, and it can raise scalability concerns both for KubeRay (size of the informer cache) and the API server / etcd (watch events).

controller-runtime provide that mechanism to narrow the scope of the informer caches.

It would be great to include a summary of this as code comment.

architkulkarni · 2023-10-20T20:56:47Z

ray-operator/controllers/ray/rayjob_controller.go

+	}
+	// Otherwise only reconcile the RayJob upon new events for watched resources
+	// to avoid infinite reconciliation.
+	return ctrl.Result{}, nil


I remember there was some previous discussion at #850 saying a controller should always unconditionally reconcile at some frequency (there it was every few minutes)

What are your thoughts on this?

This only changes the behaviour for RayJobs in terminal state. To be on the safe side, this keeps the requeueing / polling logic for RayJobs in "RayCluster deployment" phase, as well as for RayJobs in running state.

Generally, it's a desired property from the performance / scalability standpoint to have controllers converging into a stationary state, that reacts to events, rather than entering an infinite loop, with a polling rate as small as RayJobDefaultRequeueDuration, which defaults to 3s.

For terminal states, I think it's safe to assume that no events are going to be missed, otherwise it's a bug that can be fixed. And to mitigate any discrepancy, the SyncPeriod manager option could be changed from the default, 10 hours, to something of an order of magnitude lower, even down to a few 10s of minutes.

z103cb

LGTM

astefanutti · 2023-10-23T08:34:24Z

@kevin85421 @z103cb

(1) Remove the sample YAML: This encourages users to use clusterSelector, which we discourage and may deprecate in the future. Including it in the YAML could complicate potential deprecation.

The sample has been removed.

(2) Set the BackoffLimit to non-zero.

The BackoffLimit is set to 2, so a same job submission is attempted 3 times at the maximum.

astefanutti · 2023-10-23T09:34:09Z

We specify the submission ID to prevent the same job from being submitted twice in certain edge cases. Any job submission with a duplicate ID will be rejected.

The Kubernetes Job backoff limit aims to ensure that each Ray job is submitted to the RayCluster exactly once. KubeRay has very limited information about the failure: is the failure related to the accessibility of the Ray head?

@kevin85421 if the ray job submit command would return a specific code upon execution with an already submitted id (like already exists), one solution would be to set a Pod failure policy in the submitter Job, .e.g.:

apiVersion: batch/v1
kind: Job
spec:
  template:
    spec:
      restartPolicy: Never
      containers:
      - name: ray-job-submitter
        command:
        - ray
        - job
        - submit
  podFailurePolicy:
    rules:
    - action: FailJob
      onExitCodes:
        containerName:
        operator: In
        values: [<already exists code>]

So the Job would not be retry at all in that case we know it's unrecoverable.

I can check what are the Ray job CLI return codes if you think that's worth exploring.

astefanutti · 2023-10-23T09:44:21Z

Also, could you say more about the cause of #1478 "When submitting a KubeRay job (ray job CRD) to be executed on a previously provisioned cluster, the ray job status is not set to v1alpha1.JobStatusSucceeded." and how this PR fixes it?

@architkulkarni the cause was that early return statement, removed with 383aaf1, that was preventing the status update from happening, so the Id in the Job status, which is relied on in the remaining logic, was never persisted.

kevin85421 · 2023-10-23T17:00:56Z

if the ray job submit command would return a specific code upon execution with an already submitted id (like already exists), one solution would be to set a Pod failure policy in the submitter Job, .e.g.:

@astefanutti Thank you for this recommendation! This feature was first introduced in K8s 1.25 as an alpha feature, so this would be a breaking change for KubeRay. We may consider to revisit this after 3 K8s major releases.

kevin85421 · 2023-10-23T17:09:21Z

ray-operator/controllers/ray/rayjob_controller.go

@@ -324,7 +327,14 @@ func (r *RayJobReconciler) Reconcile(ctx context.Context, request ctrl.Request)
 			}
 		}
 	}
-	return ctrl.Result{RequeueAfter: RayJobDefaultRequeueDuration}, nil
+
+	if isJobPendingOrRunning(rayJobInstance.Status.JobStatus) {


Is there a possibility that JobStatus is empty at this point, causing it to return ctrl.Result{}, nil?

The only path, other than when the RayJob is in a terminal state, is the reconciliation cycle where the Kubernetes Job is created. However, now that this Job is watched, further reconciliation is guaranteed.

That being said, I've added an extra change in f7b639b that initialises the Job status, instead of eventually have it updated once the ray job has started. This is generally a best practice, and it'll make sure periodic reconciliation will be triggered when isJobPendingOrRunning is true.

ray-operator/main.go

architkulkarni · 2023-10-23T17:26:59Z

if the ray job submit command would return a specific code upon execution with an already submitted id (like already exists), one solution would be to set a Pod failure policy in the submitter Job, .e.g.:

@astefanutti Thank you for this recommendation! This feature was first introduced in K8s 1.25 as an alpha feature, so this would be a breaking change for KubeRay. We may consider to revisit this after 3 K8s major releases.

I agree. Right now the Ray job CLI doesn't return any special error codes (the CLI code just raises a Python exception, which I believe is return code 1), but I think we can update Ray to return a special error code here and the change should be relatively uncontroversial

architkulkarni

Looks good to me! (pending Kai-Hsun's last question about JobStatus being empty)

kevin85421 · 2023-10-23T17:38:20Z

I agree. Right now the Ray job CLI doesn't return any special error codes (the CLI code just raises a Python exception, which I believe is return code 1), but I think we can update Ray to return a special error code here and the change should be relatively uncontroversial

Ideally, this should be the case, but I believe there's a significant challenge for Ray to return a meaningful exit code at this moment. For example, if the Ray C++ binary exits the process with a non-zero exit code, not all code paths in Ray's Python code catch that exit code.

architkulkarni · 2023-10-23T18:02:10Z

That's true for propagating error codes in general, but here we're talking about just returning an error for the specific case of submitting a job_id which has already been submitted (which we already have a check for). We could have the job manager raise a special exception here instead of ValueError, and make the HTTP job server could check for that exception and return a special HTTP response code, and the CLI could check for that and exit with a special error code.

kevin85421 · 2023-10-23T19:53:19Z

That's true for propagating error codes in general, but here we're talking about just returning an error for the specific case of submitting a job_id which has already been submitted (which we already have a check for). We could have the job manager raise a special exception here instead of ValueError, and make the HTTP job server could check for that exception and return a special HTTP response code, and the CLI could check for that and exit with a special error code.

Understood. I hadn't noticed that. I thought that we wanted to use exit codes to categorize all cases as mentioned in #1539 (review).

kevin85421

LGTM. Thank @astefanutti for this contribution! I will merge this PR. Would you mind opening a follow-up PR to add tests for this PR?

blublinsky · 2023-10-25T07:55:58Z

LGTM

astefanutti · 2023-10-25T07:57:27Z

LGTM. Thank @astefanutti for this contribution! I will merge this PR. Would you mind opening a follow-up PR to add tests for this PR?

@kevin85421 Thanks. Sure, I'll work on the tests ASAP.

This PR adds e2e tests for the operator, following up #1539 (comment). This e2e testing enables to assert Kuberay resources, in a way that cannot be achieved with existing tests (envtest). The e2e tests execution output is post-processed using gotestfmt: The logs and events are gathered and uploaded are part of the workflow run:

This PR adds e2e test following up #1539 and leverages #1575. It also factorises configuration across the RayJob e2e tests.

This PR adds e2e test following up ray-project#1539 and leverages ray-project#1575. It also factorises configuration across the RayJob e2e tests.

kevin85421 self-assigned this Oct 18, 2023

astefanutti force-pushed the pr-01 branch from d12c1a9 to 62519ac Compare October 18, 2023 07:16

z103cb approved these changes Oct 18, 2023

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kevin85421 assigned architkulkarni Oct 18, 2023

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astefanutti added 2 commits October 23, 2023 10:02

[RayJob]: Fix missing RayJob status update

383aaf1

[RayJob]: Do not update RayJob ShutdownAfterJobFinishes spec field

12198b6

astefanutti force-pushed the pr-01 branch 2 times, most recently from 6316bcd to 7629061 Compare October 23, 2023 08:16

[RayJob]: Set RayJob as the owner of the batch Job

456188f

astefanutti force-pushed the pr-01 branch from 7629061 to 0aebab5 Compare October 23, 2023 08:23

z103cb approved these changes Oct 23, 2023

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architkulkarni approved these changes Oct 23, 2023

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[RayJob]: Avoid polling Jobs status indefinitely

ff8ee44

astefanutti added 2 commits October 24, 2023 14:19

[RayJob]: Reduce max job submission attempts to 3

241dfbb

[RayJob]: Init Job status to Pending

f7b639b

astefanutti force-pushed the pr-01 branch from 0aebab5 to f7b639b Compare October 24, 2023 14:26

kevin85421 approved these changes Oct 24, 2023

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kevin85421 merged commit 528abc3 into ray-project:master Oct 24, 2023
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astefanutti deleted the pr-01 branch October 24, 2023 18:22

astefanutti mentioned this pull request Oct 26, 2023

[CI]: Kuberay operator e2e tests #1575

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kevin85421 pushed a commit to kevin85421/kuberay that referenced this pull request Nov 2, 2023

[RayJob] Fix RayJob status reconciliation (ray-project#1539)

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kevin85421 added a commit that referenced this pull request Nov 2, 2023

[RayJob] Fix RayJob status reconciliation (#1539) (#1605)

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astefanutti mentioned this pull request Nov 8, 2023

[RayJob]: Add RayJob with RayCluster spec e2e test #1636

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architkulkarni pushed a commit that referenced this pull request Nov 14, 2023

[RayJob]: Add RayJob with RayCluster spec e2e test (#1636)

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This PR adds e2e test following up #1539 and leverages #1575. It also factorises configuration across the RayJob e2e tests.

[RayJob] Fix RayJob status reconciliation #1539

[RayJob] Fix RayJob status reconciliation #1539

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astefanutti commented Oct 17, 2023 • edited Loading

Why are these changes needed?

Related issue numbers

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