Package com.google.cloud.aiplatform.v1
Class SuggestTrialsRequest.Builder
java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
com.google.cloud.aiplatform.v1.SuggestTrialsRequest.Builder
- All Implemented Interfaces:
SuggestTrialsRequestOrBuilder,com.google.protobuf.Message.Builder,com.google.protobuf.MessageLite.Builder,com.google.protobuf.MessageLiteOrBuilder,com.google.protobuf.MessageOrBuilder,Cloneable
- Enclosing class:
- SuggestTrialsRequest
public static final class SuggestTrialsRequest.Builder
extends com.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
implements SuggestTrialsRequestOrBuilder
Request message for [VizierService.SuggestTrials][google.cloud.aiplatform.v1.VizierService.SuggestTrials].Protobuf type
google.cloud.aiplatform.v1.SuggestTrialsRequest-
Method Summary
Modifier and TypeMethodDescriptionaddAllContexts(Iterable<? extends TrialContext> values) Optional.addContexts(int index, TrialContext value) Optional.addContexts(int index, TrialContext.Builder builderForValue) Optional.addContexts(TrialContext value) Optional.addContexts(TrialContext.Builder builderForValue) Optional.Optional.addContextsBuilder(int index) Optional.build()clear()Required.Optional.Required.Required.Required.com.google.protobuf.ByteStringRequired.getContexts(int index) Optional.getContextsBuilder(int index) Optional.Optional.intOptional.Optional.getContextsOrBuilder(int index) Optional.List<? extends TrialContextOrBuilder>Optional.static final com.google.protobuf.Descriptors.Descriptorcom.google.protobuf.Descriptors.DescriptorRequired.com.google.protobuf.ByteStringRequired.intRequired.protected com.google.protobuf.GeneratedMessage.FieldAccessorTablefinal booleanmergeFrom(SuggestTrialsRequest other) mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) mergeFrom(com.google.protobuf.Message other) removeContexts(int index) Optional.setClientId(String value) Required.setClientIdBytes(com.google.protobuf.ByteString value) Required.setContexts(int index, TrialContext value) Optional.setContexts(int index, TrialContext.Builder builderForValue) Optional.Required.setParentBytes(com.google.protobuf.ByteString value) Required.setSuggestionCount(int value) Required.Methods inherited from class com.google.protobuf.GeneratedMessage.Builder
addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3Methods inherited from class com.google.protobuf.AbstractMessage.Builder
findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringMethods inherited from class com.google.protobuf.AbstractMessageLite.Builder
addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageExceptionMethods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface com.google.protobuf.Message.Builder
mergeDelimitedFrom, mergeDelimitedFromMethods inherited from interface com.google.protobuf.MessageLite.Builder
mergeFromMethods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
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Method Details
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getDescriptor
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() -
internalGetFieldAccessorTable
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()- Specified by:
internalGetFieldAccessorTablein classcom.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
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clear
- Specified by:
clearin interfacecom.google.protobuf.Message.Builder- Specified by:
clearin interfacecom.google.protobuf.MessageLite.Builder- Overrides:
clearin classcom.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
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getDescriptorForType
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()- Specified by:
getDescriptorForTypein interfacecom.google.protobuf.Message.Builder- Specified by:
getDescriptorForTypein interfacecom.google.protobuf.MessageOrBuilder- Overrides:
getDescriptorForTypein classcom.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
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getDefaultInstanceForType
- Specified by:
getDefaultInstanceForTypein interfacecom.google.protobuf.MessageLiteOrBuilder- Specified by:
getDefaultInstanceForTypein interfacecom.google.protobuf.MessageOrBuilder
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build
- Specified by:
buildin interfacecom.google.protobuf.Message.Builder- Specified by:
buildin interfacecom.google.protobuf.MessageLite.Builder
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buildPartial
- Specified by:
buildPartialin interfacecom.google.protobuf.Message.Builder- Specified by:
buildPartialin interfacecom.google.protobuf.MessageLite.Builder
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mergeFrom
- Specified by:
mergeFromin interfacecom.google.protobuf.Message.Builder- Overrides:
mergeFromin classcom.google.protobuf.AbstractMessage.Builder<SuggestTrialsRequest.Builder>
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mergeFrom
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isInitialized
public final boolean isInitialized()- Specified by:
isInitializedin interfacecom.google.protobuf.MessageLiteOrBuilder- Overrides:
isInitializedin classcom.google.protobuf.GeneratedMessage.Builder<SuggestTrialsRequest.Builder>
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mergeFrom
public SuggestTrialsRequest.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException - Specified by:
mergeFromin interfacecom.google.protobuf.Message.Builder- Specified by:
mergeFromin interfacecom.google.protobuf.MessageLite.Builder- Overrides:
mergeFromin classcom.google.protobuf.AbstractMessage.Builder<SuggestTrialsRequest.Builder>- Throws:
IOException
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getParent
Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}`string parent = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }- Specified by:
getParentin interfaceSuggestTrialsRequestOrBuilder- Returns:
- The parent.
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getParentBytes
public com.google.protobuf.ByteString getParentBytes()Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}`string parent = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }- Specified by:
getParentBytesin interfaceSuggestTrialsRequestOrBuilder- Returns:
- The bytes for parent.
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setParent
Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}`string parent = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }- Parameters:
value- The parent to set.- Returns:
- This builder for chaining.
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clearParent
Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}`string parent = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }- Returns:
- This builder for chaining.
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setParentBytes
Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}`string parent = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }- Parameters:
value- The bytes for parent to set.- Returns:
- This builder for chaining.
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getSuggestionCount
public int getSuggestionCount()Required. The number of suggestions requested. It must be positive.
int32 suggestion_count = 2 [(.google.api.field_behavior) = REQUIRED];- Specified by:
getSuggestionCountin interfaceSuggestTrialsRequestOrBuilder- Returns:
- The suggestionCount.
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setSuggestionCount
Required. The number of suggestions requested. It must be positive.
int32 suggestion_count = 2 [(.google.api.field_behavior) = REQUIRED];- Parameters:
value- The suggestionCount to set.- Returns:
- This builder for chaining.
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clearSuggestionCount
Required. The number of suggestions requested. It must be positive.
int32 suggestion_count = 2 [(.google.api.field_behavior) = REQUIRED];- Returns:
- This builder for chaining.
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getClientId
Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed.
string client_id = 3 [(.google.api.field_behavior) = REQUIRED];- Specified by:
getClientIdin interfaceSuggestTrialsRequestOrBuilder- Returns:
- The clientId.
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getClientIdBytes
public com.google.protobuf.ByteString getClientIdBytes()Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed.
string client_id = 3 [(.google.api.field_behavior) = REQUIRED];- Specified by:
getClientIdBytesin interfaceSuggestTrialsRequestOrBuilder- Returns:
- The bytes for clientId.
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setClientId
Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed.
string client_id = 3 [(.google.api.field_behavior) = REQUIRED];- Parameters:
value- The clientId to set.- Returns:
- This builder for chaining.
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clearClientId
Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed.
string client_id = 3 [(.google.api.field_behavior) = REQUIRED];- Returns:
- This builder for chaining.
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setClientIdBytes
Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed.
string client_id = 3 [(.google.api.field_behavior) = REQUIRED];- Parameters:
value- The bytes for clientId to set.- Returns:
- This builder for chaining.
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getContextsList
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];- Specified by:
getContextsListin interfaceSuggestTrialsRequestOrBuilder
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getContextsCount
public int getContextsCount()Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];- Specified by:
getContextsCountin interfaceSuggestTrialsRequestOrBuilder
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getContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];- Specified by:
getContextsin interfaceSuggestTrialsRequestOrBuilder
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setContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
setContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addAllContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
clearContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
removeContexts
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
getContextsBuilder
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
getContextsOrBuilder
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];- Specified by:
getContextsOrBuilderin interfaceSuggestTrialsRequestOrBuilder
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getContextsOrBuilderList
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];- Specified by:
getContextsOrBuilderListin interfaceSuggestTrialsRequestOrBuilder
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addContextsBuilder
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
addContextsBuilder
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL]; -
getContextsBuilderList
Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
repeated .google.cloud.aiplatform.v1.TrialContext contexts = 4 [(.google.api.field_behavior) = OPTIONAL];
-