feat: enable pytorch elastic training fashion based on torch elastic #267
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| // EnableElastic decides whether torch elastic is enabled for job. | ||
| // +optional | ||
| EnableElastic bool `json:"enableElastic"` |
| MaxReplicas *int32 `json:"maxReplicas,omitempty"` | ||
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| RDZVBackend string `json:"rdzvBackend,omitempty"` | ||
| RdzvEndpoint string `json:"rdzvEndpoint"` |
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is rdzvEndpoint required and must not omitempty?
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Yes, RDZVBackend and RdzvEndpoint are both required when EnableElastic is true.
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@wanziyu hi wanziyu, thanks for your contribution! before merge your PR to master branch, you should sign-off your commit first. |
pkg/job_controller/api/v1/types.go
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| LastReplicas int32 `json:"lastReplicas,omitempty"` | ||
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| // Continue represents whether the job needs to continue scaling. | ||
| Continue bool `json:"continue,omitempty"` |
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will user amend this continue field manually? if not, who will pause the scaling progress
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The torchelastic controller will amend this continue field. When continue is set to false in the last control loop, the controller will pause the scaling progress.
| @@ -162,11 +162,28 @@ func (jc *JobController) FilterServicesForReplicaType(services []*v1.Service, re | |||
| return result, nil | |||
| } | |||
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| // calculateServiceSliceSize compare max pod index with desired replicas and return larger size | |||
| func calculateServiceSliceSize(services []*v1.Service, replicas int) int { | |||
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is it necessary? in GetServiceSlices function, serviceSlices will be re-allocated when index> size
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If calculateServiceSliceSize is not used, there will exist an error "index out of range" when scaling in workers and services (ie., update worker replicas from 2 to 1).
…-elastic Signed-off-by: wanziyu <[email protected]>
Signed-off-by: wanziyu <[email protected]>
Signed-off-by: wanziyu <[email protected]>
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@@ Coverage Diff @@
## master #267 +/- ##
==========================================
+ Coverage 28.93% 29.12% +0.18%
==========================================
Files 88 89 +1
Lines 5985 6260 +275
==========================================
+ Hits 1732 1823 +91
- Misses 4000 4174 +174
- Partials 253 263 +10
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| ctrl "sigs.k8s.io/controller-runtime" | ||
| ) | ||
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| func SetupWithManager(mgr ctrl.Manager) error { |
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may torchelasticcontroller implementation be a sub-module of /pytorch, directory structure will looks cleaner.
| jobStatus.ElasticStatus[rtype].ElasticCondition = apiv1.ElasticStop | ||
| } | ||
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| func updateElasticStatusForContinueJob(pytorchJob *training.PyTorchJob, currentReplicas, newReplicas int32, rtype apiv1.ReplicaType) { |
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updateElasticStatusForContinueJob ... updateElasticStatusForMaxMetricJob has some code duplication that can be further abstracted.
| return true, nil | ||
| } | ||
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| func (ts *TorchElasticController) restartWorkerInKruiseProtocol(job *trainingv1alpha1.PyTorchJob, pod *corev1.Pod, expectedWorldSize, expectedGeneration string) (completed bool, err error) { |
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reuse restartWorkerInKruiseProtocol under /pytorch package
Signed-off-by: wanziyu <[email protected]>
Ⅰ. Describe what this PR does
The PR designs elastic training APIs, adds a torch-elastic controller and implements elastic training control flow on torch-elastic controller and pytorch controller. Currently, the scaling algorithm is based on the real-time batch training latency collected from running pod logs.
II. Does this pull request fix one issue?
#251