Put trained models API
editPut trained models API
edit[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
Creates a new trained model for inference.
The API accepts a PutTrainedModelRequest
object as a request and returns a PutTrainedModelResponse
.
Put trained models request
editA PutTrainedModelRequest
requires the following argument:
Trained model configuration
editThe TrainedModelConfig
object contains all the details about the trained model
configuration and contains the following arguments:
TrainedModelConfig trainedModelConfig = TrainedModelConfig.builder() .setDefinition(definition) .setCompressedDefinition(InferenceToXContentCompressor.deflate(definition)) .setModelId("my-new-trained-model") .setInput(new TrainedModelInput("col1", "col2", "col3", "col4")) .setDescription("test model") .setMetadata(new HashMap<>()) .setTags("my_regression_models") .setInferenceConfig(new RegressionConfig("value", 0)) .build();
The inference definition for the model |
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Optionally, if the inference definition is large, you may choose to compress it for transport. Do not supply both the compressed and uncompressed definitions. |
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The unique model id |
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The input field names for the model definition |
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Optionally, a human-readable description |
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Optionally, an object map contain metadata about the model |
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Optionally, an array of tags to organize the model |
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The default inference config to use with the model. Must match the underlying definition target_type. |
Synchronous execution
editWhen executing a PutTrainedModelRequest
in the following manner, the client waits
for the PutTrainedModelResponse
to be returned before continuing with code execution:
PutTrainedModelResponse response = client.machineLearning().putTrainedModel(request, RequestOptions.DEFAULT);
Synchronous calls may throw an IOException
in case of either failing to
parse the REST response in the high-level REST client, the request times out
or similar cases where there is no response coming back from the server.
In cases where the server returns a 4xx
or 5xx
error code, the high-level
client tries to parse the response body error details instead and then throws
a generic ElasticsearchException
and adds the original ResponseException
as a
suppressed exception to it.
Asynchronous execution
editExecuting a PutTrainedModelRequest
can also be done in an asynchronous fashion so that
the client can return directly. Users need to specify how the response or
potential failures will be handled by passing the request and a listener to the
asynchronous put-trained-model method:
The asynchronous method does not block and returns immediately. Once it is
completed the ActionListener
is called back using the onResponse
method
if the execution successfully completed or using the onFailure
method if
it failed. Failure scenarios and expected exceptions are the same as in the
synchronous execution case.
A typical listener for put-trained-model
looks like:
Response
editThe returned PutTrainedModelResponse
contains the newly created trained model.
The PutTrainedModelResponse
will omit the model definition as a precaution against
streaming large model definitions back to the client.
TrainedModelConfig model = response.getResponse();