[ML] Start and stop model deployments #70713
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Pinging @elastic/ml-core (Team:ML) |
dimitris-athanasiou
changed the base branch from
master
to
feature/pytorch-inference
davidkyle
approved these changes
Mar 24, 2021
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| this.timeout = timeout; | |
| this.timeout = ExceptionsHelper.requireNonNull(timeout, TIMEOUT); |
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| public static class TaskParams implements PersistentTaskParams { | ||
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| public static final Version VERSION_INTRODUCED = Version.V_7_13_0; |
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Will change to 8 and we see :-)
| listener.onResponse(new StopTrainedModelDeploymentAction.Response(true)); | ||
| return; | ||
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| if (models.size() > 1) { |
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In future we may have more than one model config using the deployment. I might be that we don't do the GetTrainedModelsAction here and just look for persistent tasks that match the model ID
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Agreed. That's how we typically implement stop actions where we handle stopping multiple tasks at once. Just thought this was simpler for now.
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| private void doStartDeployment(TrainedModelDeploymentTask task) { | ||
| logger.info("[{}] Starting model deployment", task.getModelId()); |
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| logger.info("[{}] Starting model deployment", task.getModelId()); | |
| logger.debug("[{}] Starting model deployment", task.getModelId()); |
davidkyle
added a commit
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Jun 3, 2021
The feature branch contains changes to configure PyTorch models with a TrainedModelConfig and defines a format to store the binary models. The _start and _stop deployment actions control the model lifecycle and the model can be directly evaluated with the _infer endpoint. 2 Types of NLP tasks are supported: Named Entity Recognition and Fill Mask. The feature branch consists of these PRs: #73523, #72218, #71679 #71323, #71035, #71177, #70713
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