[ML] Pass inference timeout to start deployment #116725
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davidkyle
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Nov 13, 2024
| * @param modelVariant The configuration of the model variant to be downloaded | ||
| * @param listener The listener | ||
| */ | ||
| default void putModel(Model modelVariant, ActionListener<Boolean> listener) { |
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This method is only used by the ElasticsearchInternalService and does not need to be part of the InferenceService interface
prwhelan
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Nov 13, 2024
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Default inference endpoints automatically deploy the model on inference the inference timeout is now passed to start model deployment so users can control that timeout
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…)" This reverts commit 59602a9.
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Default inference endpoints automatically deploy the model on inference the inference timeout is now passed to start model deployment so users can control that timeout
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Default inference endpoints automatically deploy the model on inference the inference timeout is now passed to start model deployment so users can control that timeout
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) * [ML] Pass inference timeout to start deployment (#116725) Default inference endpoints automatically deploy the model on inference the inference timeout is now passed to start model deployment so users can control that timeout * handle max time --------- Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
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Default inference endpoints automatically deploy the model on inference the inference timeout is now passed to start model deployment so users can control that timeout
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Default inference endpoints automatically deploy the model on inference. If the model download has been started then deploying the model should wait for the download to complete. This happens but with a default timeout of 30 seconds which in some cases is not long enough.
The timeout parameter from the inference request is now used in the start deployment request. Semantic text sets the timeout to a large value so it will not timeout, clients can control this with the inference timeout parameter
Non issue as the code is behind a feature flag