com.google.cloud.visionai.v1.VertexCustomConfig Maven / Gradle / Ivy
/*
* Copyright 2024 Google LLC
*
* Licensed 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
*
* https://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.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/visionai/v1/platform.proto
// Protobuf Java Version: 3.25.3
package com.google.cloud.visionai.v1;
/**
*
*
*
* Message describing VertexCustomConfig.
*
*
* Protobuf type {@code google.cloud.visionai.v1.VertexCustomConfig}
*/
public final class VertexCustomConfig extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.visionai.v1.VertexCustomConfig)
VertexCustomConfigOrBuilder {
private static final long serialVersionUID = 0L;
// Use VertexCustomConfig.newBuilder() to construct.
private VertexCustomConfig(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private VertexCustomConfig() {
postProcessingCloudFunction_ = "";
dynamicConfigInputTopic_ = "";
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new VertexCustomConfig();
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.visionai.v1.PlatformProto
.internal_static_google_cloud_visionai_v1_VertexCustomConfig_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
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.internal_static_google_cloud_visionai_v1_VertexCustomConfig_fieldAccessorTable
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com.google.cloud.visionai.v1.VertexCustomConfig.class,
com.google.cloud.visionai.v1.VertexCustomConfig.Builder.class);
}
private int bitField0_;
public static final int MAX_PREDICTION_FPS_FIELD_NUMBER = 1;
private int maxPredictionFps_ = 0;
/**
*
*
*
* The max prediction frame per second. This attribute sets how fast the
* operator sends prediction requests to Vertex AI endpoint. Default value is
* 0, which means there is no max prediction fps limit. The operator sends
* prediction requests at input fps.
*
*
* int32 max_prediction_fps = 1;
*
* @return The maxPredictionFps.
*/
@java.lang.Override
public int getMaxPredictionFps() {
return maxPredictionFps_;
}
public static final int DEDICATED_RESOURCES_FIELD_NUMBER = 2;
private com.google.cloud.visionai.v1.DedicatedResources dedicatedResources_;
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*
* @return Whether the dedicatedResources field is set.
*/
@java.lang.Override
public boolean hasDedicatedResources() {
return ((bitField0_ & 0x00000001) != 0);
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*
* @return The dedicatedResources.
*/
@java.lang.Override
public com.google.cloud.visionai.v1.DedicatedResources getDedicatedResources() {
return dedicatedResources_ == null
? com.google.cloud.visionai.v1.DedicatedResources.getDefaultInstance()
: dedicatedResources_;
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
@java.lang.Override
public com.google.cloud.visionai.v1.DedicatedResourcesOrBuilder getDedicatedResourcesOrBuilder() {
return dedicatedResources_ == null
? com.google.cloud.visionai.v1.DedicatedResources.getDefaultInstance()
: dedicatedResources_;
}
public static final int POST_PROCESSING_CLOUD_FUNCTION_FIELD_NUMBER = 3;
@SuppressWarnings("serial")
private volatile java.lang.Object postProcessingCloudFunction_ = "";
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @return The postProcessingCloudFunction.
*/
@java.lang.Override
public java.lang.String getPostProcessingCloudFunction() {
java.lang.Object ref = postProcessingCloudFunction_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
postProcessingCloudFunction_ = s;
return s;
}
}
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @return The bytes for postProcessingCloudFunction.
*/
@java.lang.Override
public com.google.protobuf.ByteString getPostProcessingCloudFunctionBytes() {
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if (ref instanceof java.lang.String) {
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com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
postProcessingCloudFunction_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int ATTACH_APPLICATION_METADATA_FIELD_NUMBER = 4;
private boolean attachApplicationMetadata_ = false;
/**
*
*
*
* If true, the prediction request received by custom model will also contain
* metadata with the following schema:
* 'appPlatformMetadata': {
* 'ingestionTime': DOUBLE; (UNIX timestamp)
* 'application': STRING;
* 'instanceId': STRING;
* 'node': STRING;
* 'processor': STRING;
* }
*
*
* bool attach_application_metadata = 4;
*
* @return The attachApplicationMetadata.
*/
@java.lang.Override
public boolean getAttachApplicationMetadata() {
return attachApplicationMetadata_;
}
public static final int DYNAMIC_CONFIG_INPUT_TOPIC_FIELD_NUMBER = 6;
@SuppressWarnings("serial")
private volatile java.lang.Object dynamicConfigInputTopic_ = "";
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return Whether the dynamicConfigInputTopic field is set.
*/
@java.lang.Override
public boolean hasDynamicConfigInputTopic() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return The dynamicConfigInputTopic.
*/
@java.lang.Override
public java.lang.String getDynamicConfigInputTopic() {
java.lang.Object ref = dynamicConfigInputTopic_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
dynamicConfigInputTopic_ = s;
return s;
}
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return The bytes for dynamicConfigInputTopic.
*/
@java.lang.Override
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java.lang.Object ref = dynamicConfigInputTopic_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
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dynamicConfigInputTopic_ = b;
return b;
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memoizedIsInitialized = 1;
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@java.lang.Override
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if (maxPredictionFps_ != 0) {
output.writeInt32(1, maxPredictionFps_);
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if (attachApplicationMetadata_ != false) {
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if (!(obj instanceof com.google.cloud.visionai.v1.VertexCustomConfig)) {
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}
com.google.cloud.visionai.v1.VertexCustomConfig other =
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if (getMaxPredictionFps() != other.getMaxPredictionFps()) return false;
if (hasDedicatedResources() != other.hasDedicatedResources()) return false;
if (hasDedicatedResources()) {
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}
if (!getPostProcessingCloudFunction().equals(other.getPostProcessingCloudFunction()))
return false;
if (getAttachApplicationMetadata() != other.getAttachApplicationMetadata()) return false;
if (hasDynamicConfigInputTopic() != other.hasDynamicConfigInputTopic()) return false;
if (hasDynamicConfigInputTopic()) {
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@java.lang.Override
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int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + MAX_PREDICTION_FPS_FIELD_NUMBER;
hash = (53 * hash) + getMaxPredictionFps();
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(java.io.InputStream input)
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseDelimitedFrom(
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseDelimitedFrom(
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return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
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public static com.google.cloud.visionai.v1.VertexCustomConfig parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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public static Builder newBuilder() {
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public static Builder newBuilder(com.google.cloud.visionai.v1.VertexCustomConfig prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
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@java.lang.Override
protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
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return builder;
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/**
*
*
*
* Message describing VertexCustomConfig.
*
*
* Protobuf type {@code google.cloud.visionai.v1.VertexCustomConfig}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
implements
// @@protoc_insertion_point(builder_implements:google.cloud.visionai.v1.VertexCustomConfig)
com.google.cloud.visionai.v1.VertexCustomConfigOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.visionai.v1.PlatformProto
.internal_static_google_cloud_visionai_v1_VertexCustomConfig_descriptor;
}
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private Builder() {
maybeForceBuilderInitialization();
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maxPredictionFps_ = 0;
dedicatedResources_ = null;
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dedicatedResourcesBuilder_ = null;
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postProcessingCloudFunction_ = "";
attachApplicationMetadata_ = false;
dynamicConfigInputTopic_ = "";
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if (other == com.google.cloud.visionai.v1.VertexCustomConfig.getDefaultInstance())
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private int bitField0_;
private int maxPredictionFps_;
/**
*
*
*
* The max prediction frame per second. This attribute sets how fast the
* operator sends prediction requests to Vertex AI endpoint. Default value is
* 0, which means there is no max prediction fps limit. The operator sends
* prediction requests at input fps.
*
*
* int32 max_prediction_fps = 1;
*
* @return The maxPredictionFps.
*/
@java.lang.Override
public int getMaxPredictionFps() {
return maxPredictionFps_;
}
/**
*
*
*
* The max prediction frame per second. This attribute sets how fast the
* operator sends prediction requests to Vertex AI endpoint. Default value is
* 0, which means there is no max prediction fps limit. The operator sends
* prediction requests at input fps.
*
*
* int32 max_prediction_fps = 1;
*
* @param value The maxPredictionFps to set.
* @return This builder for chaining.
*/
public Builder setMaxPredictionFps(int value) {
maxPredictionFps_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* The max prediction frame per second. This attribute sets how fast the
* operator sends prediction requests to Vertex AI endpoint. Default value is
* 0, which means there is no max prediction fps limit. The operator sends
* prediction requests at input fps.
*
*
* int32 max_prediction_fps = 1;
*
* @return This builder for chaining.
*/
public Builder clearMaxPredictionFps() {
bitField0_ = (bitField0_ & ~0x00000001);
maxPredictionFps_ = 0;
onChanged();
return this;
}
private com.google.cloud.visionai.v1.DedicatedResources dedicatedResources_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.visionai.v1.DedicatedResources,
com.google.cloud.visionai.v1.DedicatedResources.Builder,
com.google.cloud.visionai.v1.DedicatedResourcesOrBuilder>
dedicatedResourcesBuilder_;
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*
* @return Whether the dedicatedResources field is set.
*/
public boolean hasDedicatedResources() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*
* @return The dedicatedResources.
*/
public com.google.cloud.visionai.v1.DedicatedResources getDedicatedResources() {
if (dedicatedResourcesBuilder_ == null) {
return dedicatedResources_ == null
? com.google.cloud.visionai.v1.DedicatedResources.getDefaultInstance()
: dedicatedResources_;
} else {
return dedicatedResourcesBuilder_.getMessage();
}
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public Builder setDedicatedResources(com.google.cloud.visionai.v1.DedicatedResources value) {
if (dedicatedResourcesBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
dedicatedResources_ = value;
} else {
dedicatedResourcesBuilder_.setMessage(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public Builder setDedicatedResources(
com.google.cloud.visionai.v1.DedicatedResources.Builder builderForValue) {
if (dedicatedResourcesBuilder_ == null) {
dedicatedResources_ = builderForValue.build();
} else {
dedicatedResourcesBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public Builder mergeDedicatedResources(com.google.cloud.visionai.v1.DedicatedResources value) {
if (dedicatedResourcesBuilder_ == null) {
if (((bitField0_ & 0x00000002) != 0)
&& dedicatedResources_ != null
&& dedicatedResources_
!= com.google.cloud.visionai.v1.DedicatedResources.getDefaultInstance()) {
getDedicatedResourcesBuilder().mergeFrom(value);
} else {
dedicatedResources_ = value;
}
} else {
dedicatedResourcesBuilder_.mergeFrom(value);
}
if (dedicatedResources_ != null) {
bitField0_ |= 0x00000002;
onChanged();
}
return this;
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public Builder clearDedicatedResources() {
bitField0_ = (bitField0_ & ~0x00000002);
dedicatedResources_ = null;
if (dedicatedResourcesBuilder_ != null) {
dedicatedResourcesBuilder_.dispose();
dedicatedResourcesBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public com.google.cloud.visionai.v1.DedicatedResources.Builder getDedicatedResourcesBuilder() {
bitField0_ |= 0x00000002;
onChanged();
return getDedicatedResourcesFieldBuilder().getBuilder();
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
public com.google.cloud.visionai.v1.DedicatedResourcesOrBuilder
getDedicatedResourcesOrBuilder() {
if (dedicatedResourcesBuilder_ != null) {
return dedicatedResourcesBuilder_.getMessageOrBuilder();
} else {
return dedicatedResources_ == null
? com.google.cloud.visionai.v1.DedicatedResources.getDefaultInstance()
: dedicatedResources_;
}
}
/**
*
*
*
* A description of resources that are dedicated to the DeployedModel, and
* that need a higher degree of manual configuration.
*
*
* .google.cloud.visionai.v1.DedicatedResources dedicated_resources = 2;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.visionai.v1.DedicatedResources,
com.google.cloud.visionai.v1.DedicatedResources.Builder,
com.google.cloud.visionai.v1.DedicatedResourcesOrBuilder>
getDedicatedResourcesFieldBuilder() {
if (dedicatedResourcesBuilder_ == null) {
dedicatedResourcesBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.visionai.v1.DedicatedResources,
com.google.cloud.visionai.v1.DedicatedResources.Builder,
com.google.cloud.visionai.v1.DedicatedResourcesOrBuilder>(
getDedicatedResources(), getParentForChildren(), isClean());
dedicatedResources_ = null;
}
return dedicatedResourcesBuilder_;
}
private java.lang.Object postProcessingCloudFunction_ = "";
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @return The postProcessingCloudFunction.
*/
public java.lang.String getPostProcessingCloudFunction() {
java.lang.Object ref = postProcessingCloudFunction_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
postProcessingCloudFunction_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @return The bytes for postProcessingCloudFunction.
*/
public com.google.protobuf.ByteString getPostProcessingCloudFunctionBytes() {
java.lang.Object ref = postProcessingCloudFunction_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
postProcessingCloudFunction_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @param value The postProcessingCloudFunction to set.
* @return This builder for chaining.
*/
public Builder setPostProcessingCloudFunction(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
postProcessingCloudFunction_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @return This builder for chaining.
*/
public Builder clearPostProcessingCloudFunction() {
postProcessingCloudFunction_ = getDefaultInstance().getPostProcessingCloudFunction();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
/**
*
*
*
* If not empty, the prediction result will be sent to the specified cloud
* function for post processing.
* * The cloud function will receive AppPlatformCloudFunctionRequest where
* the annotations field will be the json format of proto PredictResponse.
* * The cloud function should return AppPlatformCloudFunctionResponse with
* PredictResponse stored in the annotations field.
* * To drop the prediction output, simply clear the payload field in the
* returned AppPlatformCloudFunctionResponse.
*
*
* string post_processing_cloud_function = 3;
*
* @param value The bytes for postProcessingCloudFunction to set.
* @return This builder for chaining.
*/
public Builder setPostProcessingCloudFunctionBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
postProcessingCloudFunction_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
private boolean attachApplicationMetadata_;
/**
*
*
*
* If true, the prediction request received by custom model will also contain
* metadata with the following schema:
* 'appPlatformMetadata': {
* 'ingestionTime': DOUBLE; (UNIX timestamp)
* 'application': STRING;
* 'instanceId': STRING;
* 'node': STRING;
* 'processor': STRING;
* }
*
*
* bool attach_application_metadata = 4;
*
* @return The attachApplicationMetadata.
*/
@java.lang.Override
public boolean getAttachApplicationMetadata() {
return attachApplicationMetadata_;
}
/**
*
*
*
* If true, the prediction request received by custom model will also contain
* metadata with the following schema:
* 'appPlatformMetadata': {
* 'ingestionTime': DOUBLE; (UNIX timestamp)
* 'application': STRING;
* 'instanceId': STRING;
* 'node': STRING;
* 'processor': STRING;
* }
*
*
* bool attach_application_metadata = 4;
*
* @param value The attachApplicationMetadata to set.
* @return This builder for chaining.
*/
public Builder setAttachApplicationMetadata(boolean value) {
attachApplicationMetadata_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* If true, the prediction request received by custom model will also contain
* metadata with the following schema:
* 'appPlatformMetadata': {
* 'ingestionTime': DOUBLE; (UNIX timestamp)
* 'application': STRING;
* 'instanceId': STRING;
* 'node': STRING;
* 'processor': STRING;
* }
*
*
* bool attach_application_metadata = 4;
*
* @return This builder for chaining.
*/
public Builder clearAttachApplicationMetadata() {
bitField0_ = (bitField0_ & ~0x00000008);
attachApplicationMetadata_ = false;
onChanged();
return this;
}
private java.lang.Object dynamicConfigInputTopic_ = "";
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return Whether the dynamicConfigInputTopic field is set.
*/
public boolean hasDynamicConfigInputTopic() {
return ((bitField0_ & 0x00000010) != 0);
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return The dynamicConfigInputTopic.
*/
public java.lang.String getDynamicConfigInputTopic() {
java.lang.Object ref = dynamicConfigInputTopic_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
dynamicConfigInputTopic_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return The bytes for dynamicConfigInputTopic.
*/
public com.google.protobuf.ByteString getDynamicConfigInputTopicBytes() {
java.lang.Object ref = dynamicConfigInputTopic_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
dynamicConfigInputTopic_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @param value The dynamicConfigInputTopic to set.
* @return This builder for chaining.
*/
public Builder setDynamicConfigInputTopic(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
dynamicConfigInputTopic_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return This builder for chaining.
*/
public Builder clearDynamicConfigInputTopic() {
dynamicConfigInputTopic_ = getDefaultInstance().getDynamicConfigInputTopic();
bitField0_ = (bitField0_ & ~0x00000010);
onChanged();
return this;
}
/**
*
*
*
* Optional. By setting the configuration_input_topic, processor will
* subscribe to given topic, only pub/sub topic is supported now. Example
* channel:
* //pubsub.googleapis.com/projects/visionai-testing-stable/topics/test-topic
* message schema should be:
* message Message {
* // The ID of the stream that associates with the application instance.
* string stream_id = 1;
* // The target fps. By default, the custom processor will *not* send any
* data to the Vertex Prediction container. Note that once the
* dynamic_config_input_topic is set, max_prediction_fps will not work and be
* preceded by the fps set inside the topic.
* int32 fps = 2;
* }
*
*
*
* optional string dynamic_config_input_topic = 6 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @param value The bytes for dynamicConfigInputTopic to set.
* @return This builder for chaining.
*/
public Builder setDynamicConfigInputTopicBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
dynamicConfigInputTopic_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.visionai.v1.VertexCustomConfig)
}
// @@protoc_insertion_point(class_scope:google.cloud.visionai.v1.VertexCustomConfig)
private static final com.google.cloud.visionai.v1.VertexCustomConfig DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.visionai.v1.VertexCustomConfig();
}
public static com.google.cloud.visionai.v1.VertexCustomConfig getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public VertexCustomConfig parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.visionai.v1.VertexCustomConfig getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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