[ML] make allocated trained model infer requests fully cancellable#88649
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benwtrent merged 1 commit intoelastic:masterfrom Jul 20, 2022
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weizijun
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* 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 an infer request is made, it may or may not be queued for later execution. If the caller making the inference request stops listening for the result, we should not execute action.
This commit allows for infer requests made to deployed models to be cancelled even after they are queued for inference.
Related to: #88009