[ML] Fail model deployment if all allocations cannot be provided#88656
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dimitris-athanasiou merged 3 commits intoelastic:masterfrom Jul 21, 2022
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When we cannot scale up (autoscaling is disabled) we should fail requests to start a trained model deployment whose allocations cannot be provided.
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Pinging @elastic/ml-core (Team:ML) |
benwtrent
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Jul 20, 2022
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| if (maxLazyMLNodes <= nodes.size() | ||
| && trainedModelAssignment.isSatisfied(nodes.stream().map(DiscoveryNode::getId).collect(Collectors.toSet())) == false) { | ||
| String msg = "Could not start deployment because there are not enough resources to provide all requested allocations"; | ||
| logger.debug(() -> format("[%s] %s", modelId, msg)); | ||
| exception = new ElasticsearchStatusException(msg, RestStatus.TOO_MANY_REQUESTS); | ||
| return true; | ||
| } |
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We should also check to make sure that the current node size is less than the max node size (vertical scaling vs horizontal).
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Well spotted. I've added a commit that factors out a isScalingPossible and uses it in both places we make that check.
droberts195
reviewed
Jul 21, 2022
| return false; | ||
| } | ||
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| private boolean isScalingPossible(List<DiscoveryNode> nodes) { |
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I think you should add a comment that this only considers memory, and in the future it would be nice to consider CPU too.
It means there's a discrepancy in how we handle different situations:
- If autoscaling is enabled, a cluster is scaled to maximum size, there are 2 free CPUs, and you ask to start a deployment that needs 3 CPUs then you get told you cannot.
- If autoscaling is enabled, a cluster is scaled to one step below its maximum size, there are no free CPUs, and you ask to start a deployment that needs 100000 CPUs then that's fine - we start it, the cluster scales to its maximum size and the deployment goes ahead but with far fewer than 100000 CPUs allocated to it.
While autoscaling doesn't understand CPU there's not a lot we can do about this, but it's worth at least adding comments to acknowledge where we are today.
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I have added a TODO comment.
benwtrent
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Jul 21, 2022
weizijun
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Jul 22, 2022
* upstream/master: (40 commits) Fix CI job naming [ML] disallow autoscaling downscaling in two trained model assignment scenarios (elastic#88623) Add "Vector Search" area to changelog schema [DOCS] Update API key API (elastic#88499) Enable the pipeline on the feature branch (elastic#88672) Adding the ability to register a PeerFinderListener to Coordinator (elastic#88626) [DOCS] Fix transform painless example syntax (elastic#88364) [ML] Muting InternalCategorizationAggregationTests testReduceRandom (elastic#88685) Fix double rounding errors for disk usage (elastic#88683) Replace health request with a state observer. (elastic#88641) [ML] Fail model deployment if all allocations cannot be provided (elastic#88656) Upgrade to OpenJDK 18.0.2+9 (elastic#88675) [ML] make bucket_correlation aggregation generally available (elastic#88655) Adding cardinality support for random_sampler agg (elastic#86838) Use custom task instead of generic AckedClusterStateUpdateTask (elastic#88643) Reinstate test cluster throttling behavior (elastic#88664) Mute testReadBlobWithPrematureConnectionClose Simplify plugin descriptor tests (elastic#88659) Add CI job for testing more job parallelism [ML] make deployment infer requests fully cancellable (elastic#88649) ...
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When we cannot scale up (autoscaling is disabled) we should fail
requests to start a trained model deployment whose allocations
cannot be provided.