co.elastic.clients.elasticsearch.ml.StartTrainedModelDeploymentRequest Maven / Gradle / Ivy
/*
* Licensed to Elasticsearch B.V. under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch B.V. licenses this file to you under
* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package co.elastic.clients.elasticsearch.ml;
import co.elastic.clients.elasticsearch._types.ErrorResponse;
import co.elastic.clients.elasticsearch._types.RequestBase;
import co.elastic.clients.elasticsearch._types.Time;
import co.elastic.clients.json.JsonpDeserializable;
import co.elastic.clients.json.JsonpDeserializer;
import co.elastic.clients.json.ObjectBuilderDeserializer;
import co.elastic.clients.json.ObjectDeserializer;
import co.elastic.clients.transport.Endpoint;
import co.elastic.clients.transport.endpoints.SimpleEndpoint;
import co.elastic.clients.util.ApiTypeHelper;
import co.elastic.clients.util.ObjectBuilder;
import jakarta.json.stream.JsonGenerator;
import java.lang.Integer;
import java.lang.String;
import java.util.HashMap;
import java.util.Map;
import java.util.Objects;
import java.util.function.Function;
import javax.annotation.Nullable;
//----------------------------------------------------------------
// THIS CODE IS GENERATED. MANUAL EDITS WILL BE LOST.
//----------------------------------------------------------------
//
// This code is generated from the Elasticsearch API specification
// at https://github.com/elastic/elasticsearch-specification
//
// Manual updates to this file will be lost when the code is
// re-generated.
//
// If you find a property that is missing or wrongly typed, please
// open an issue or a PR on the API specification repository.
//
//----------------------------------------------------------------
// typedef: ml.start_trained_model_deployment.Request
/**
* Start a trained model deployment. It allocates the model to every machine
* learning node.
*
* @see API
* specification
*/
public class StartTrainedModelDeploymentRequest extends RequestBase {
@Nullable
private final String cacheSize;
@Nullable
private final String deploymentId;
private final String modelId;
@Nullable
private final Integer numberOfAllocations;
@Nullable
private final TrainingPriority priority;
@Nullable
private final Integer queueCapacity;
@Nullable
private final Integer threadsPerAllocation;
@Nullable
private final Time timeout;
@Nullable
private final DeploymentAllocationState waitFor;
// ---------------------------------------------------------------------------------------------
private StartTrainedModelDeploymentRequest(Builder builder) {
this.cacheSize = builder.cacheSize;
this.deploymentId = builder.deploymentId;
this.modelId = ApiTypeHelper.requireNonNull(builder.modelId, this, "modelId");
this.numberOfAllocations = builder.numberOfAllocations;
this.priority = builder.priority;
this.queueCapacity = builder.queueCapacity;
this.threadsPerAllocation = builder.threadsPerAllocation;
this.timeout = builder.timeout;
this.waitFor = builder.waitFor;
}
public static StartTrainedModelDeploymentRequest of(
Function> fn) {
return fn.apply(new Builder()).build();
}
/**
* The inference cache size (in memory outside the JVM heap) per node for the
* model. The default value is the same size as the
* model_size_bytes. To disable the cache, 0b can be
* provided.
*
* API name: {@code cache_size}
*/
@Nullable
public final String cacheSize() {
return this.cacheSize;
}
/**
* A unique identifier for the deployment of the model.
*
* API name: {@code deployment_id}
*/
@Nullable
public final String deploymentId() {
return this.deploymentId;
}
/**
* Required - The unique identifier of the trained model. Currently, only
* PyTorch models are supported.
*
* API name: {@code model_id}
*/
public final String modelId() {
return this.modelId;
}
/**
* The number of model allocations on each node where the model is deployed. All
* allocations on a node share the same copy of the model in memory but use a
* separate set of threads to evaluate the model. Increasing this value
* generally increases the throughput. If this setting is greater than the
* number of hardware threads it will automatically be changed to a value less
* than the number of hardware threads.
*
* API name: {@code number_of_allocations}
*/
@Nullable
public final Integer numberOfAllocations() {
return this.numberOfAllocations;
}
/**
* The deployment priority.
*
* API name: {@code priority}
*/
@Nullable
public final TrainingPriority priority() {
return this.priority;
}
/**
* 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: {@code queue_capacity}
*/
@Nullable
public final Integer queueCapacity() {
return this.queueCapacity;
}
/**
* Sets the number of threads used by each model allocation during inference.
* This generally increases the inference speed. The inference process is a
* compute-bound process; any number greater than the number of available
* hardware threads on the machine does not increase the inference speed. If
* this setting is greater than the number of hardware threads it will
* automatically be changed to a value less than the number of hardware threads.
*
* API name: {@code threads_per_allocation}
*/
@Nullable
public final Integer threadsPerAllocation() {
return this.threadsPerAllocation;
}
/**
* Specifies the amount of time to wait for the model to deploy.
*
* API name: {@code timeout}
*/
@Nullable
public final Time timeout() {
return this.timeout;
}
/**
* Specifies the allocation status to wait for before returning.
*
* API name: {@code wait_for}
*/
@Nullable
public final DeploymentAllocationState waitFor() {
return this.waitFor;
}
// ---------------------------------------------------------------------------------------------
/**
* Builder for {@link StartTrainedModelDeploymentRequest}.
*/
public static class Builder extends RequestBase.AbstractBuilder
implements
ObjectBuilder {
@Nullable
private String cacheSize;
@Nullable
private String deploymentId;
private String modelId;
@Nullable
private Integer numberOfAllocations;
@Nullable
private TrainingPriority priority;
@Nullable
private Integer queueCapacity;
@Nullable
private Integer threadsPerAllocation;
@Nullable
private Time timeout;
@Nullable
private DeploymentAllocationState waitFor;
/**
* The inference cache size (in memory outside the JVM heap) per node for the
* model. The default value is the same size as the
* model_size_bytes. To disable the cache, 0b can be
* provided.
*
* API name: {@code cache_size}
*/
public final Builder cacheSize(@Nullable String value) {
this.cacheSize = value;
return this;
}
/**
* A unique identifier for the deployment of the model.
*
* API name: {@code deployment_id}
*/
public final Builder deploymentId(@Nullable String value) {
this.deploymentId = value;
return this;
}
/**
* Required - The unique identifier of the trained model. Currently, only
* PyTorch models are supported.
*
* API name: {@code model_id}
*/
public final Builder modelId(String value) {
this.modelId = value;
return this;
}
/**
* The number of model allocations on each node where the model is deployed. All
* allocations on a node share the same copy of the model in memory but use a
* separate set of threads to evaluate the model. Increasing this value
* generally increases the throughput. If this setting is greater than the
* number of hardware threads it will automatically be changed to a value less
* than the number of hardware threads.
*
* API name: {@code number_of_allocations}
*/
public final Builder numberOfAllocations(@Nullable Integer value) {
this.numberOfAllocations = value;
return this;
}
/**
* The deployment priority.
*
* API name: {@code priority}
*/
public final Builder priority(@Nullable TrainingPriority value) {
this.priority = value;
return this;
}
/**
* 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: {@code queue_capacity}
*/
public final Builder queueCapacity(@Nullable Integer value) {
this.queueCapacity = value;
return this;
}
/**
* Sets the number of threads used by each model allocation during inference.
* This generally increases the inference speed. The inference process is a
* compute-bound process; any number greater than the number of available
* hardware threads on the machine does not increase the inference speed. If
* this setting is greater than the number of hardware threads it will
* automatically be changed to a value less than the number of hardware threads.
*
* API name: {@code threads_per_allocation}
*/
public final Builder threadsPerAllocation(@Nullable Integer value) {
this.threadsPerAllocation = value;
return this;
}
/**
* Specifies the amount of time to wait for the model to deploy.
*
* API name: {@code timeout}
*/
public final Builder timeout(@Nullable Time value) {
this.timeout = value;
return this;
}
/**
* Specifies the amount of time to wait for the model to deploy.
*
* API name: {@code timeout}
*/
public final Builder timeout(Function> fn) {
return this.timeout(fn.apply(new Time.Builder()).build());
}
/**
* Specifies the allocation status to wait for before returning.
*
* API name: {@code wait_for}
*/
public final Builder waitFor(@Nullable DeploymentAllocationState value) {
this.waitFor = value;
return this;
}
@Override
protected Builder self() {
return this;
}
/**
* Builds a {@link StartTrainedModelDeploymentRequest}.
*
* @throws NullPointerException
* if some of the required fields are null.
*/
public StartTrainedModelDeploymentRequest build() {
_checkSingleUse();
return new StartTrainedModelDeploymentRequest(this);
}
}
// ---------------------------------------------------------------------------------------------
/**
* Endpoint "{@code ml.start_trained_model_deployment}".
*/
public static final Endpoint _ENDPOINT = new SimpleEndpoint<>(
"es/ml.start_trained_model_deployment",
// Request method
request -> {
return "POST";
},
// Request path
request -> {
final int _modelId = 1 << 0;
int propsSet = 0;
propsSet |= _modelId;
if (propsSet == (_modelId)) {
StringBuilder buf = new StringBuilder();
buf.append("/_ml");
buf.append("/trained_models");
buf.append("/");
SimpleEndpoint.pathEncode(request.modelId, buf);
buf.append("/deployment");
buf.append("/_start");
return buf.toString();
}
throw SimpleEndpoint.noPathTemplateFound("path");
},
// Path parameters
request -> {
Map params = new HashMap<>();
final int _modelId = 1 << 0;
int propsSet = 0;
propsSet |= _modelId;
if (propsSet == (_modelId)) {
params.put("modelId", request.modelId);
}
return params;
},
// Request parameters
request -> {
Map params = new HashMap<>();
if (request.cacheSize != null) {
params.put("cache_size", request.cacheSize);
}
if (request.threadsPerAllocation != null) {
params.put("threads_per_allocation", String.valueOf(request.threadsPerAllocation));
}
if (request.deploymentId != null) {
params.put("deployment_id", request.deploymentId);
}
if (request.waitFor != null) {
params.put("wait_for", request.waitFor.jsonValue());
}
if (request.numberOfAllocations != null) {
params.put("number_of_allocations", String.valueOf(request.numberOfAllocations));
}
if (request.priority != null) {
params.put("priority", request.priority.jsonValue());
}
if (request.timeout != null) {
params.put("timeout", request.timeout._toJsonString());
}
if (request.queueCapacity != null) {
params.put("queue_capacity", String.valueOf(request.queueCapacity));
}
return params;
}, SimpleEndpoint.emptyMap(), false, StartTrainedModelDeploymentResponse._DESERIALIZER);
}