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2e8bf33b0a
This adds model_alias support for native pytorch models. Model aliases can be used in `_infer` or within the inference processor. This way the alias can be atomically changed without down time to another deployed model. Restrictions: - Model alias changes need to be done between two models of the same kind (e.g. pytorch -> pytorch) - Model alias change is not allowed between a model that is deployed to a model that is not - Model alias change is not allowed between a model that deployed AND allocated to a model that is deployed but NOT allocated (not assigned to any nodes). - A deployment cannot be stopped (without supplying the `force` parameter) when the model has a model alias that is used by a pipeline. closes: https://github.com/elastic/elasticsearch/issues/90960
99 lines
3.3 KiB
Text
99 lines
3.3 KiB
Text
[role="xpack"]
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[[put-trained-models-aliases]]
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= Create or update trained model aliases API
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[subs="attributes"]
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++++
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<titleabbrev>Create or update trained model aliases</titleabbrev>
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++++
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Creates or updates a trained model alias.
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A trained model alias is a logical name used to reference a single trained model.
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[[ml-put-trained-models-aliases-request]]
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== {api-request-title}
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`PUT _ml/trained_models/<model_id>/model_aliases/<model_alias>`
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[[ml-put-trained-models-aliases-prereq]]
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== {api-prereq-title}
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Requires the `manage_ml` cluster privilege. This privilege is included in the
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`machine_learning_admin` built-in role.
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[[ml-put-trained-models-aliases-desc]]
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== {api-description-title}
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You can use aliases instead of trained model identifiers to make it easier to
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reference your models. For example, you can use aliases in {infer} aggregations
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and processors.
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An alias must be unique and refer to only a single trained model. However,
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you can have multiple aliases for each trained model.
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API Restrictions:
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+
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--
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* You are not allowed to update an alias such that it references a different
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trained model ID and the model uses a different type of {dfanalytics}. For example,
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this situation occurs if you have a trained model for
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{reganalysis} and a trained model for {classanalysis}; you cannot reassign an
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alias from one type of trained model to another.
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* You cannot update an alias from a `pytorch` model and a {dfanalytics} model.
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* You cannot update the alias from a deployed `pytorch` model to one
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not currently deployed.
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--
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If you use this API to update an alias and there are very few input fields in
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common between the old and new trained models for the model alias, the API
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returns a warning.
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[[ml-put-trained-models-aliases-path-params]]
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== {api-path-parms-title}
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`model_alias`::
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(Required, string)
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The alias to create or update. This value cannot end in numbers.
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`model_id`::
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(Required, string)
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The identifier for the trained model that the alias refers to.
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[[ml-put-trained-models-aliases-query-params]]
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== {api-query-parms-title}
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`reassign`::
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(Optional, boolean)
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Specifies whether the alias gets reassigned to the specified trained model if it
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is already assigned to a different model. If the alias is already assigned and
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this parameter is `false`, the API returns an error. Defaults to `false`.
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[[ml-put-trained-models-aliases-example]]
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== {api-examples-title}
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[[ml-put-trained-models-aliases-example-new-alias]]
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=== Create a trained model alias
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The following example shows how to create an alias (`flight_delay_model`) for a
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trained model (`flight-delay-prediction-1574775339910`):
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[source,console]
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--------------------------------------------------
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PUT _ml/trained_models/flight-delay-prediction-1574775339910/model_aliases/flight_delay_model
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--------------------------------------------------
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// TEST[skip:setup kibana sample data]
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[[ml-put-trained-models-aliases-example-put-alias]]
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=== Update a trained model alias
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The following example shows how to reassign an alias (`flight_delay_model`) to a
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different trained model (`flight-delay-prediction-1580004349800`):
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[source,console]
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--------------------------------------------------
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PUT _ml/trained_models/flight-delay-prediction-1580004349800/model_aliases/flight_delay_model?reassign=true
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--------------------------------------------------
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// TEST[skip:setup kibana sample data]
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