Comments (3)
Hi @abatilo, thank you for opening the issue. You may have some misunderstanding for GCS FT. You can read https://docs.ray.io/en/latest/cluster/kubernetes/user-guides/kuberay-gcs-ft.html#kuberay-gcs-ft for more details. Currently, the only use case of GCS FT is for Ray Serve high availability.
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Thanks for responding @kevin85421. My original question still stands then. Is there no way to have the jobs running in progress survive after the Ray head gets restarted? I see that there's a way to resume a trainer from previous state but do I always have to re-submit that if the Ray head is gone?
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My current understanding is that Ray Train provides some degree of fault tolerance.
- If a Ray worker Pod crashes, Ray Train will launch new Ray tasks or actors, allowing the job to continue running.
- If the Ray head crashes, the driver process, which is typically running on the Ray head of the Ray job, will also crash. Therefore, the Ray tasks or actors will also be garbage collected automatically. In most cases, Ray tasks or actors are fate-sharing with the driver process. Only detached actors do not share this fate with the driver.
- In this case, users need to write the fault tolerance logic at the application level, specifically in their Ray Python script.
if (checkpoint exists): read checkpoint else: start from scratch train the model
- In this case, users need to write the fault tolerance logic at the application level, specifically in their Ray Python script.
The long-running Ray job is currently on our roadmap of the next release. We are currently working on:
- Retry mechanism in both RayJob CRD /
ray job submit
. - Best practice for checkpointing.
If you are interested in this topic, you can reach out to me on Ray Slack (my handle is 'Kai-Hsun Chen (ray team)'). We can discuss your requirements and ensure there are no feature gaps for your use cases.
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Related Issues (20)
- [Doc] KubeRay configuration
- [Regression from v1.1] RayCluster Condition should surface resource quota issue correctly HOT 8
- [Bug] Resume the `Replicas` field in type HeadGroupSpec struct HOT 1
- [Bug] RayJob does not shut down the submitter pod properly HOT 1
- [Feature] Should --num-cpu be based on CPU requests instead of limits? HOT 14
- [Feature] scheduled jobs, workflows, and incremental learning HOT 1
- [Feature] Support setting QPS and Burst in configuration and command line flags
- [Feature] Remove `//nolint:gosec` to allow rule G115 after the false positive issue is solved
- [Bug] CI error: actions/upload-artifact version deprecation
- [Bug] Old RayServices not deleted after operator update to 1.2.1 HOT 8
- [Bug] Submit a task with kuberay-operator, deliberately let the task fail, found that the submitter will repeatedly create three times, how to turn off this option? HOT 2
- [Bug] kuberay cannot run long tasks, and service disconnection occurs. HOT 5
- [Feature] Consider setting AUTOSCALER_CONSERVE_GPU_NODES by default in Ray autoscaler HOT 2
- [Bug] Autoscaler sideacr crashes, bringing down head pod, if request exceeds max pod replicas
- [Proposal] Let kubectl-plugin import KubeRay types from ray-operator HOT 1
- [Bug] Bubble ImagePullErr and ImagePullBackoff to the Ray CRD HOT 1
- [Feature][kubectl-plugin] Run port-forward in a goroutine and retry if the connection fails HOT 2
- [Doc] Update Ray doc to say every release between PR ray#44658 and ray #47721 doesn't work for long-running job
- [Feature] Support standard k8 spec for ray-job-submitter pod in Ray Job CRD HOT 9
- [Bug] can not set submitter pod restart times HOT 2
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