Class StartTrainedModelDeploymentRequest
java.lang.Object
co.elastic.clients.elasticsearch._types.RequestBase
co.elastic.clients.elasticsearch.ml.StartTrainedModelDeploymentRequest
public class StartTrainedModelDeploymentRequest extends RequestBase
Starts a trained model deployment, which allocates the model to every machine
learning node.
- See Also:
- API specification
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
StartTrainedModelDeploymentRequest.Builder
Builder forStartTrainedModelDeploymentRequest
.Nested classes/interfaces inherited from class co.elastic.clients.elasticsearch._types.RequestBase
RequestBase.AbstractBuilder<BuilderT extends RequestBase.AbstractBuilder<BuilderT>>
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Field Summary
Fields Modifier and Type Field Description static Endpoint<StartTrainedModelDeploymentRequest,StartTrainedModelDeploymentResponse,ErrorResponse>
_ENDPOINT
Endpoint "ml.start_trained_model_deployment
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Method Summary
Modifier and Type Method Description java.lang.Integer
inferenceThreads()
Specifies the number of threads that are used by the inference process.java.lang.String
modelId()
Required - The unique identifier of the trained model.java.lang.Integer
modelThreads()
Specifies the number of threads that are used when sending inference requests to the model.static StartTrainedModelDeploymentRequest
of(java.util.function.Function<StartTrainedModelDeploymentRequest.Builder,ObjectBuilder<StartTrainedModelDeploymentRequest>> fn)
java.lang.Integer
queueCapacity()
Specifies the number of inference requests that are allowed in the queue.Time
timeout()
Specifies the amount of time to wait for the model to deploy.DeploymentAllocationState
waitFor()
Specifies the allocation status to wait for before returning.Methods inherited from class co.elastic.clients.elasticsearch._types.RequestBase
toString
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Field Details
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_ENDPOINT
public static final Endpoint<StartTrainedModelDeploymentRequest,StartTrainedModelDeploymentResponse,ErrorResponse> _ENDPOINTEndpoint "ml.start_trained_model_deployment
".
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Method Details
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of
public static StartTrainedModelDeploymentRequest of(java.util.function.Function<StartTrainedModelDeploymentRequest.Builder,ObjectBuilder<StartTrainedModelDeploymentRequest>> fn) -
inferenceThreads
@Nullable public final java.lang.Integer inferenceThreads()Specifies the number of threads that are used by the inference process. If you increase this value, inference speed generally increases. However, the actual number of threads is limited by the number of available CPU cores.API name:
inference_threads
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modelId
public final java.lang.String modelId()Required - The unique identifier of the trained model. Currently, only PyTorch models are supported.API name:
model_id
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modelThreads
@Nullable public final java.lang.Integer modelThreads()Specifies the number of threads that are used when sending inference requests to the model. If you increase this value, throughput generally increases.API name:
model_threads
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queueCapacity
@Nullable public final java.lang.Integer queueCapacity()Specifies the number of inference requests that are allowed in the queue. After the number of requests exceeds this value, new requests are rejected with a 429 error.API name:
queue_capacity
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timeout
Specifies the amount of time to wait for the model to deploy.API name:
timeout
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waitFor
Specifies the allocation status to wait for before returning.API name:
wait_for
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