$6$SrSSKJr SSKJr SSKJr SSKJr Sr "SS\R5r "S S \R5r "S S \R5r "S S\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS \R5r"S!S"\R5r"S#S$\R5r"S%S&\R5r"S'S(\R5r"S)S*\R5r"S+S,\R5r"S-S.\R5r"S/S0\R5r"S1S2\R5r "S3S4\R5r!"S5S6\R5r""S7S8\R5r#"S9S:\R5r$"S;S<\R5r%"S=S>\R5r&"S?S@\R5r'"SASB\R5r("SCSD\R5r)"SESF\R5r*"SGSH\R5r+"SISJ\R5r,"SKSL\R5r-"SMSN\R5r."SOSP\R5r/"SQSR\R5r0"SSST\R5r1"SUSV\R5r2"SWSX\R5r3"SYSZ\R5r4"S[S\\R5r5"S]S^\R5r6"S_S`\R5r7"SaSb\R5r8"ScSd\R5r9"SeSf\R5r:"SgSh\R5r;"SiSj\R5r<"SkSl\R5r="SmSn\R5r>"SoSp\R5r?"SqSr\R5r@"SsSt\R5rA"SuSv\R5rB"SwSx\R5rC"SySz\R5rD"S{S|\R5rE"S}S~\R5rF"SS\R5rG"SS\R5rH"SS\R5rI"SS\R5rJ"SS\R5rK"SS\R5rL"SS\R5rM"SS\R5rN"SS\R5rO"SS\R5rP"SS\R5rQ"SS\R5rR"SS\R5rS"SS\R5rT"SS\R5rU"SS\R5rV"SS\R5rW"SS\R5rX"SS\R5rY"SS\R5rZ"SS\R5r["SS\R5r\"SS\R5r]"SS\R5r^"SS\R5r_"SS\R5r`"SS\R5ra"SS\R5rb"SS\R5rc"SS\R5rd"SS\R5re"SS\R5rf"SS\R5rg"SS\R5rh"SS\R5ri"SS\R5rj"SS\R5rk"SS\R5rl"SS\R5rm"SS\R5rn"SS\R5ro"SS\R5rp"SS\R5rq"SS\R5rr"SS\R5rs"SS\R5rt"SS\R5ru"SS\R5rv"SS\R5rw"SS\R5rx"SS\R5ry"SS\R5rz"SS\R5r{"SS\R5r|"SS\R5r}"SS\R5r~"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SS\R5r"SGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GS GS \R5r"GS GS \R5r"GS GS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS\R5r"GSGS \R5r"GS!GS"\R5r"GS#GS$\R5r"GS%GS&\R5r"GS'GS(\R5r"GS)GS*\R5r"GS+GS,\R5r"GS-GS.\R5r"GS/GS0\R5r\GR@"\GS1GS25 \GRB"\GRDGS3GS45 \GRB"\GRDGS5GS65 \GR@"\uGS7GS85 \GR@"\GS7GS85 Gg9(:zkGenerated message classes for ml version v1. An API to enable creating and using machine learning models. )absolute_import)messages)encoding) extra_typesmlc\rSrSrSr\R "S5"SS\R55r \R"S5r \R"S5r \R"SSS S 9rS rg ) GoogleApiHttpBodyaMessage that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top- level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. Messages: ExtensionsValueListEntry: A ExtensionsValueListEntry object. Fields: contentType: The HTTP Content-Type header value specifying the content type of the body. data: The HTTP request/response body as raw binary. extensions: Application specific response metadata. Must be set in the first response for streaming APIs. additionalPropertiescf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) *GoogleApiHttpBody.ExtensionsValueListEntry/zA ExtensionsValueListEntry object. Messages: AdditionalProperty: An additional property for a ExtensionsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. cb\rSrSrSr\R "S5r\R"SS5r Sr g)=GoogleApiHttpBody.ExtensionsValueListEntry.AdditionalProperty<zAn additional property for a ExtensionsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. extra_types.JsonValueN __name__ __module__ __qualname____firstlineno____doc__ _messages StringFieldkey MessageFieldvalue__static_attributes__rAlib/googlecloudsdk/generated_clients/apis/ml/v1/ml_v1_messages.pyAdditionalPropertyr<,   ! !! $c$$%   #%   2A 6$r"r<c>\rSrSrSr\R "SS5rSrg))GoogleCloudMlV1AddTrialMeasurementRequestzThe request message for the AddTrialMeasurement service method. Fields: measurement: Required. The measurement to be added to a trial. GoogleCloudMlV1MeasurementrrN) rrrrrrr measurementr!rr"r#r]r]s &&'CQG+r"r]c\rSrSrSr\R "S\RRS9r \R"SSSS9r \R "S \RRS9r S r g ) GoogleCloudMlV1AutoScalinga Options for automatically scaling a model. Fields: maxNodes: The maximum number of nodes to scale this model under load. The actual value will depend on resource quota and availability. metrics: MetricSpec contains the specifications to use to calculate the desired nodes count. minNodes: Optional. The minimum number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed. Therefore, the cost of operating this model will be at least `rate` * `min_nodes` * number of hours since last billing cycle, where `rate` is the cost per node-hour as documented in the [pricing guide](/ml-engine/docs/pricing), even if no predictions are performed. There is additional cost for each prediction performed. Unlike manual scaling, if the load gets too heavy for the nodes that are up, the service will automatically add nodes to handle the increased load as well as scale back as traffic drops, always maintaining at least `min_nodes`. You will be charged for the time in which additional nodes are used. If `min_nodes` is not specified and AutoScaling is used with a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online- prediction), `min_nodes` defaults to 0, in which case, when traffic to a model stops (and after a cool-down period), nodes will be shut down and no charges will be incurred until traffic to the model resumes. If `min_nodes` is not specified and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online- prediction), `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a Compute Engine machine type. You can set `min_nodes` when creating the model version, and you can also update `min_nodes` for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects/*/models /*/versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json rr5GoogleCloudMlV1MetricSpecrTr&r,rN)rrrrrrr7r8r9maxNodesrmetricsminNodesr!rr"r#rbrbs[B # #Ay/@/@/F/F G(  " "#>D Q'  # #Ay/@/@/F/F G(r"rbcd\rSrSrSr\R "SS5r\R "SS5rSr g) &GoogleCloudMlV1AutomatedStoppingConfigaConfiguration for Automated Early Stopping of Trials. If no implementation_config is set, automated early stopping will not be run. Fields: decayCurveStoppingConfig: A GoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfig attribute. medianAutomatedStoppingConfig: A GoogleCloudMlV1AutomatedStoppingConfigMedianAutomatedStoppingConfig attribute. GGoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfigrCGoogleCloudMlV1AutomatedStoppingConfigMedianAutomatedStoppingConfigrrN) rrrrrrrdecayCurveStoppingConfigmedianAutomatedStoppingConfigr!rr"r#ririsA '334}@AB"+"8"89~AB#Cr"ric<\rSrSrSr\R "S5rSrg)rkaA GoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfig object. Fields: useElapsedTime: If true, measurement.elapsed_time is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.steps will be used as the x-axis. rrN rrrrrr BooleanFielduseElapsedTimer!rr"r#rkrks))!,.r"rkc<\rSrSrSr\R "S5rSrg)rlaXThe median automated stopping rule stops a pending trial if the trial's best objective_value is strictly below the median 'performance' of all completed trials reported up to the trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the trial in each measurement. Fields: useElapsedTime: If true, the median automated stopping rule applies to measurement.use_elapsed_time, which means the elapsed_time field of the current trial's latest measurement is used to compute the median objective value for each completed trial. rrNrqrr"r#rlrls ))!,.r"rlcb\rSrSrSr\R "S\RRS9r Sr g)!GoogleCloudMlV1BlurBaselineConfiga Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 Fields: maxBlurSigma: The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline. rr5rN) rrrrrr FloatFieldr8FLOAT maxBlurSigmar!rr"r#rwrws' %%a1B1B1H1HI,r"rwc\rSrSrSr\R "S5r\R "S5r\R "S5r \R "S5r Sr g) %GoogleCloudMlV1BuiltInAlgorithmOutputiaRepresents output related to a built-in algorithm Job. Fields: framework: Framework on which the built-in algorithm was trained. modelPath: The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning. pythonVersion: Python version on which the built-in algorithm was trained. runtimeVersion: AI Platform runtime version on which the built-in algorithm was trained. rrr,rArN) rrrrrrr framework modelPath pythonVersionruntimeVersionr!rr"r#r}r}sI ##A&)##A&)''*-((+.r"r}c\rSrSrSrSrg)GoogleCloudMlV1CancelJobRequestiz)Request message for the CancelJob method.rNrrrrrr!rr"r#rr2r"rc\rSrSrSr"SS\R 5r"SS\R 5r\R"SSSS 9r \R"SS 5r S r g ) GoogleCloudMlV1CapabilityizA GoogleCloudMlV1Capability object. Enums: AvailableAcceleratorsValueListEntryValuesEnum: TypeValueValuesEnum: Fields: availableAccelerators: Available accelerators for the capability. type: A TypeValueValuesEnum attribute. cH\rSrSrSrSrSrSrSrSr Sr S r S r S r S rS rSrSrg)GGoogleCloudMlV1Capability.AvailableAcceleratorsValueListEntryValuesEnumi&aAvailableAcceleratorsValueListEntryValuesEnum enum type. Values: ACCELERATOR_TYPE_UNSPECIFIED: Unspecified accelerator type. Default to no GPU. NVIDIA_TESLA_K80: Nvidia Tesla K80 GPU. NVIDIA_TESLA_P100: Nvidia Tesla P100 GPU. NVIDIA_TESLA_V100: Nvidia V100 GPU. NVIDIA_TESLA_P4: Nvidia Tesla P4 GPU. NVIDIA_TESLA_T4: Nvidia T4 GPU. NVIDIA_TESLA_A100: Nvidia A100 GPU. TPU_V2: TPU v2. TPU_V3: TPU v3. TPU_V2_POD: TPU v2 POD. TPU_V3_POD: TPU v3 POD. TPU_V4_POD: TPU v4 POD. rrrr,rArBrCrDrErFrGrHrNrIrr"r#-AvailableAcceleratorsValueListEntryValuesEnumr&rWr"rc(\rSrSrSrSrSrSrSrSr g) -GoogleCloudMlV1Capability.TypeValueValuesEnumiEzTypeValueValuesEnum enum type. Values: TYPE_UNSPECIFIED: TRAINING: BATCH_PREDICTION: ONLINE_PREDICTION: rrrr,rN) rrrrrTYPE_UNSPECIFIEDTRAININGBATCH_PREDICTIONONLINE_PREDICTIONr!rr"r#rVrEs Hr"rVrTr&rrN) rrrrrrrXrrVrZavailableAcceleratorsr[r!rr"r#rrsT inn> INN $--.]_`kop   2A 6$r"rc\rSrSrSr\R "S5r\R "S5r\R "S5r Sr g)4GoogleCloudMlV1CheckTrialEarlyStoppingStateMetatdataiWa%This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request. Fields: createTime: The time at which the operation was submitted. study: The name of the study that the trial belongs to. trial: The trial name. rrr,rN) rrrrrrr createTimestudytrialr!rr"r#rrWs9$$Q'*    "%    "%r"rc\rSrSrSrSrg)2GoogleCloudMlV1CheckTrialEarlyStoppingStateRequestigzKThe request message for the CheckTrialEarlyStoppingState service method. rNrrr"r#rrgsr"rc\rSrSrSr\R "S5r\R"S5r \R "S5r Sr g)3GoogleCloudMlV1CheckTrialEarlyStoppingStateResponseimaBThe message will be placed in the response field of a completed google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request. Fields: endTime: The time at which operation processing completed. shouldStop: True if the Trial should stop. startTime: The time at which the operation was started. rrr,rN) rrrrrrrendTimerr shouldStop startTimer!rr"r#rrms9  ! !! $'%%a(*##A&)r"rc\rSrSrSr\R "SS5r\R"S5r \R"S5r Sr g) #GoogleCloudMlV1CompleteTrialRequesti}a+The request message for the CompleteTrial service method. Fields: finalMeasurement: Optional. If provided, it will be used as the completed trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement infeasibleReason: Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true. trialInfeasible: Optional. True if the trial cannot be run with the given Parameter, and final_measurement will be ignored. r_rrr,rN) rrrrrrrfinalMeasurementrinfeasibleReasonrrtrialInfeasibler!rr"r#rr}s> ++,H!L**1-**1-/r"rc<\rSrSrSr\R "S5rSrg)GoogleCloudMlV1ConfigiztA GoogleCloudMlV1Config object. Fields: tpuServiceAccount: The service account Cloud ML uses to run on TPU node. rrN) rrrrrrrtpuServiceAccountr!rr"r#rrs  ++A.r"rcb\rSrSrSr\R "S\RRS9r Sr g)GoogleCloudMlV1ContainerPortiahRepresents a network port in a single container. This message is a subset of the [Kubernetes ContainerPort v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#containerport-v1-core). Fields: containerPort: Number of the port to expose on the container. This must be a valid port number: 0 < PORT_NUMBER < 65536. rr5rN) rrrrrrr7r8r9 containerPortr!rr"r#rrs'((I4E4E4K4KL-r"rc\rSrSrSr\R "SSS9r\R "SSS9r\R"SSSS9r \R "S 5r \R"S S SS9r S r g )GoogleCloudMlV1ContainerSpeciaSpecification of a custom container for serving predictions. This message is a subset of the [Kubernetes Container v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#container-v1-core). Fields: args: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify this field as an array of executable and arguments, similar to a Docker `CMD`'s "default parameters" form. If you don't specify this field but do specify the command field, then the command from the `command` field runs without any additional arguments. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data- application/define-command-argument-container/#notes). If you don't specify this field and don't specify the `commmand` field, then the container's [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and `CMD` determine what runs based on their default behavior. See the [Docker documentation about how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand- how-cmd-and-entrypoint-interact). In this field, you can reference [environment variables set by AI Platform Prediction](/ai- platform/prediction/docs/custom-container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `args` field of the [Kubernetes Containers v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#container-v1-core). command: Immutable. Specifies the command that runs when the container starts. This overrides the container's [`ENTRYPOINT`](https://docs.docke r.com/engine/reference/builder/#entrypoint). Specify this field as an array of executable and arguments, similar to a Docker `ENTRYPOINT`'s "exec" form, not its "shell" form. If you do not specify this field, then the container's `ENTRYPOINT` runs, in conjunction with the args field or the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd), if either exists. If this field is not specified and the container does not have an `ENTRYPOINT`, then refer to the [Docker documentation about how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand- how-cmd-and-entrypoint-interact). If you specify this field, then you can also specify the `args` field to provide additional arguments for this command. However, if you specify this field, then the container's `CMD` is ignored. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define- command-argument-container/#notes). In this field, you can reference [environment variables set by AI Platform Prediction](/ai- platform/prediction/docs/custom-container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `command` field of the [Kubernetes Containers v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#container-v1-core). env: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable `VAR_2` to have the value `foo bar`: ```json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] ``` If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the `env` field of the [Kubernetes Containers v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#container-v1-core). image: URI of the Docker image to be used as the custom container for serving predictions. This URI must identify [an image in Artifact Registry](/artifact-registry/docs/overview) and begin with the hostname `{REGION}-docker.pkg.dev`, where `{REGION}` is replaced by the region that matches AI Platform Prediction [regional endpoint](/ai- platform/prediction/docs/regional-endpoints) that you are using. For example, if you are using the `us-central1-ml.googleapis.com` endpoint, then this URI must begin with `us-central1-docker.pkg.dev`. To use a custom container, the [AI Platform Google-managed service account](/ai- platform/prediction/docs/custom-service-account#default) must have permission to pull (read) the Docker image at this URI. The AI Platform Google-managed service account has the following format: `service-{PROJECT_NUMBER}@cloud-ml.google.com.iam.gserviceaccount.com` {PROJECT_NUMBER} is replaced by your Google Cloud project number. By default, this service account has necessary permissions to pull an Artifact Registry image in the same Google Cloud project where you are using AI Platform Prediction. In this case, no configuration is necessary. If you want to use an image from a different Google Cloud project, learn how to [grant the Artifact Registry Reader (roles/artifactregistry.reader) role for a repository](/artifact- registry/docs/access-control#grant-repo) to your projet's AI Platform Google-managed service account. To learn about the requirements for the Docker image itself, read [Custom container requirements](/ai- platform/prediction/docs/custom-container-requirements). ports: Immutable. List of ports to expose from the container. AI Platform Prediction sends any prediction requests that it receives to the first port on this list. AI Platform Prediction also sends [liveness and health checks](/ai-platform/prediction/docs/custom-container- requirements#health) to this port. If you do not specify this field, it defaults to following value: ```json [ { "containerPort": 8080 } ] ``` AI Platform Prediction does not use ports other than the first one listed. This field corresponds to the `ports` field of the [Kubernetes Containers v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#container-v1-core). rTr&rGoogleCloudMlV1EnvVarr,rArrBrN)rrrrrrrargscommandrenvimageportsr!rr"r#rrsisj   q4 0$  ! !!d 3'6DI#    "%  !?T R%r"rc\rSrSrSr\R "S\RRS9r \R"S5r Sr g)GoogleCloudMlV1DiskConfigi$a&Represents the config of disk options. Fields: bootDiskSizeGb: Size in GB of the boot disk (default is 100GB). bootDiskType: Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive). rr5rrN) rrrrrrr7r8r9bootDiskSizeGbr bootDiskTyper!rr"r#rr$s7))!Y5F5F5L5LM.&&q),r"rc<\rSrSrSr\R "S5rSrg)GoogleCloudMlV1EncryptionConfigi2alRepresents a custom encryption key configuration that can be applied to a resource. Fields: kmsKeyName: The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyR ings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}` rrN) rrrrrrr kmsKeyNamer!rr"r#rr2s$$Q'*r"rc`\rSrSrSr\R "S5r\R "S5rSr g)ri@a'Represents an environment variable to be made available in a container. This message is a subset of the [Kubernetes EnvVar v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.18/#envvar-v1-core). Fields: name: Name of the environment variable. Must be a [valid C identifier](htt ps://github.com/kubernetes/kubernetes/blob/v1.18.8/staging/src/k8s.io/ap imachinery/pkg/util/validation/validation.go#L258) and must not begin with the prefix `AIP_`. value: Value of the environment variable. Defaults to an empty string. In this field, you can reference [environment variables set by AI Platform Prediction](/ai-platform/prediction/docs/custom-container- requirements#aip-variables) and environment variables set earlier in the same env field as where this message occurs. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $(VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) rrrN) rrrrrrrnamer r!rr"r#rr@s)0   q !$    "%r"rc>\rSrSrSr\R "SS5rSrg)GoogleCloudMlV1ExplainRequesti]z|Request for explanations to be issued against a trained model. Fields: httpBody: Required. The explanation request body. r rrN rrrrrrrhttpBodyr!rr"r#rr]s  # #$7 ;(r"rc\rSrSrSr\R "SS5r\R "SS5r\R "SS5r \R "S S 5r \R "S S 5r \R "S S5r Sr g) GoogleCloudMlV1ExplanationConfigigaMessage holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. [Learn more about feature attributions.](/ai-platform/prediction/docs/ai-explanations/overview) Fields: ablationAttribution: TensorFlow framework explanation methods. Deprecated. Attributes credit to model inputs by ablating features (ie. setting them to their default/missing values) and computing corresponding model score delta per feature. The term "ablation" is in reference to running an "ablation study" to analyze input effects on the outcome of interest, which in this case is the model's output. This attribution method is supported for TensorFlow and XGBoost models. integratedGradientsAttribution: Attributes credit by computing the Aumann- Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 saabasAttribution: Attributes credit by running a faster approximation to the TreeShap method. Please refer to this link for more details: https://blog.datadive.net/interpreting-random-forests/ This attribution method is only supported for XGBoost models. sampledShapleyAttribution: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. treeShapAttribution: XGBoost framework explanation methods. Attributes credit by computing the Shapley value taking advantage of the model's tree ensemble structure. Refer to this paper for more details: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting- model-predictions.pdf. This attribution method is supported for XGBoost models. xraiAttribution: Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs. r3r-GoogleCloudMlV1IntegratedGradientsAttributionr GoogleCloudMlV1SaabasAttributionr,(GoogleCloudMlV1SampledShapleyAttributionrA"GoogleCloudMlV1TreeShapAttributionrBGoogleCloudMlV1XraiAttributionrCrN)rrrrrrrablationAttributionintegratedGradientsAttributionsaabasAttributionsampledShapleyAttributiontreeShapAttributionxraiAttributionr!rr"r#rrgs#J"../SUVW#,#9#9:ikl#m ,,-OQRS'445_abc!../SUVW**+KQO/r"rc$\rSrSrSr"SS\R 5r"SS\R 5r"SS\R 5r \R"S S 5r \R"S 5r \R"SS 5r\R"S S5r\R"SS5r\R"S5r\R&"SSS9r\R"S5r\R&"S5r\R&"S5r\R"SS5r\R&"S5r\R&"S5r\R&"S5r\R&"SSS9r\R&"S5r\R&"S5r\R&"S5rSr g )!GoogleCloudMlV1ExplanationInputia4Represents input parameters for a model explanation job. Enums: DataFormatValueValuesEnum: Required. The format of the input data. FrameworkValueValuesEnum: Optional. The framework used to train this model. Only needed if model_version is a GCS path. Otherwise the framework specified during version creation will be used. OutputDataFormatValueValuesEnum: Optional. The format of the output data, defaults to BIGQUERY. Fields: accelerator: Optional. The type and number of accelerators to be attached to each machine running the job. batchSize: Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter. dataFormat: Required. The format of the input data. explanationConfig: Required only if model_version is specified through a uri, otherwise the same explanation config specified at model version creation will be used. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload. framework: Optional. The framework used to train this model. Only needed if model_version is a GCS path. Otherwise the framework specified during version creation will be used. initialWorkerCount: Optional. The initial number of workers to be used for parallel processing. Defaults to 0 if one wants the service to figure out the number. The actual number of workers being used may change after the job starts depending on the autoscaling policy. inputPaths: Required when data_format is JSON. The Cloud Storage location of the input data. May contain wildcards. maxWorkerCount: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified. modelName: Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"` outputBigqueryTable: Required when output_data_format is BIGQUERY. The output fully qualified BigQuery table name in the format of "[project_id].[dataset_name].[table_name]". outputDataFormat: Optional. The format of the output data, defaults to BIGQUERY. region: Required. The Compute Engine region to run the explanation job in. See the available regions for AI Platform services. runtimeVersion: Required. The AI Platform runtime version to use for the explanation job. See B*,,-OQRS!!"%%&_abmqr*$112Y[\]  ! !! $'&&'`bcd+**+A1E/!..q1##A&)   4a 8%  ! !! $'(()=rB-r"r c`\rSrSrSr\R "S5r\R"S5r Sr g)r'izAn observed value of a metric. Fields: objectiveValue: The objective value at this training step. trainingStep: The global training step for this metric. rrrN) rrrrrrryobjectiveValuer7 trainingStepr!rr"r#r'r's)''*.''*,r"r'c&\rSrSrSr"SS\R 5r"SS\R 5r\R"SS5r \R"S5r \R"SS 5r \R"S 5r\R "S \R"R$S 9r\R "S \R"R$S 9r\R "S\R"R$S 9r\R,"SSSS9r\R"S5rSrg)!GoogleCloudMlV1HyperparameterSpecia Represents a set of hyperparameters to optimize. Enums: AlgorithmValueValuesEnum: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified. GoalValueValuesEnum: Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`. Fields: algorithm: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified. enableTrialEarlyStopping: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping. goal: Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`. hyperparameterMetricTag: Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used. maxFailedTrials: Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail. maxParallelTrials: Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one. maxTrials: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one. params: Required. The set of parameters to tune. resumePreviousJobId: Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study. c(\rSrSrSrSrSrSrSrSr g) :GoogleCloudMlV1HyperparameterSpec.AlgorithmValueValuesEnumiaGOptional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified. Values: ALGORITHM_UNSPECIFIED: The default algorithm used by the hyperparameter tuning service. This is a Bayesian optimization algorithm. GRID_SEARCH: Simple grid search within the feasible space. To use grid search, all parameters must be `INTEGER`, `CATEGORICAL`, or `DISCRETE`. RANDOM_SEARCH: Simple random search within the feasible space. POPULATION_BASED_TRAINING: Population Based Training Algorithm. rrrr,rN) rrrrrALGORITHM_UNSPECIFIED GRID_SEARCH RANDOM_SEARCHPOPULATION_BASED_TRAININGr!rr"r#AlgorithmValueValuesEnumr6s KM !r"r;c$\rSrSrSrSrSrSrSrg)5GoogleCloudMlV1HyperparameterSpec.GoalValueValuesEnumi/aRequired. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`. Values: GOAL_TYPE_UNSPECIFIED: Goal Type will default to maximize. MAXIMIZE: Maximize the goal metric. MINIMIZE: Minimize the goal metric. rrrrN rrrrrGOAL_TYPE_UNSPECIFIEDMAXIMIZEMINIMIZEr!rr"r#GoalValueValuesEnumr=/sHHr"rBrrr,rArBr5rCrDGoogleCloudMlV1ParameterSpecrETr&rFrN)rrrrrrrXr;rBrZ algorithmrrenableTrialEarlyStoppinggoalrhyperparameterMetricTagr7r8r9maxFailedTrialsmaxParallelTrials maxTrialsrparamsresumePreviousJobIdr!rr"r#r4r4s)V""& INN !!"DQ)''*-r"rqc`\rSrSrSr\R "SSSS9r\R"S5r Sr g ) !GoogleCloudMlV1ListModelsResponseizResponse message for the ListModels method. Fields: models: The list of models. nextPageToken: Optional. Pass this token as the `page_token` field of the request for a subsequent call. GoogleCloudMlV1ModelrTr&rrN) rrrrrrrmodelsrror!rr"r#rurus.  ! !"8!d K&''*-r"ruc\rSrSrSrSrg)'GoogleCloudMlV1ListOptimalTrialsRequestiz6The request message for the ListTrials service method.rNrrr"r#ryrys?r"ryc<\rSrSrSr\R "SSSS9rSrg) (GoogleCloudMlV1ListOptimalTrialsResponseia-The response message for the ListOptimalTrials method. Fields: trials: The pareto-optimal trials for multiple objective study or the optimal trial for single objective study. The definition of pareto- optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency GoogleCloudMlV1TrialrTr&rN rrrrrrrtrialsr!rr"r#r{r{s  ! !"8!d K&r"r{c<\rSrSrSr\R "SSSS9rSrg) "GoogleCloudMlV1ListStudiesResponseizjA GoogleCloudMlV1ListStudiesResponse object. Fields: studies: The studies associated with the project. GoogleCloudMlV1StudyrTr&rN) rrrrrrrstudiesr!rr"r#rrs  " "#91t L'r"rc<\rSrSrSr\R "SSSS9rSrg) !GoogleCloudMlV1ListTrialsResponseiziThe response message for the ListTrials method. Fields: trials: The trials associated with the study. r|rTr&rNr}rr"r#rrs  ! !"8!d K&r"rc`\rSrSrSr\R "S5r\R"SSSS9r Sr g ) #GoogleCloudMlV1ListVersionsResponseizResponse message for the ListVersions method. Fields: nextPageToken: Optional. Pass this token as the `page_token` field of the request for a subsequent call. versions: The list of versions. rGoogleCloudMlV1VersionrTr&rN) rrrrrrrrorversionsr!rr"r#rrs.''*-  # #$->-D-D E&r"rdc\rSrSrSr\R "S5"SS\R55r \R"SS5r \R"S5r \R"S 5r\R"SS 5r\R"S 5r\R$"S 5r\R$"S 5r\R"SSS9rSrg)rvi}a Represents a machine learning solution. A model can have multiple versions, each of which is a deployed, trained model ready to receive prediction requests. The model itself is just a container. Messages: LabelsValue: Optional. One or more labels that you can add, to organize your models. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. Fields: defaultVersion: Output only. The default version of the model. This version will be used to handle prediction requests that do not specify a version. You can change the default version by calling projects.models.versions.setDefault. description: Optional. The description specified for the model when it was created. etag: `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read- modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetModel`, and systems are expected to put that etag in the request to `UpdateModel` to ensure that their change will be applied to the model as intended. labels: Optional. One or more labels that you can add, to organize your models. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. name: Required. The name specified for the model when it was created. The model name must be unique within the project it is created in. onlinePredictionConsoleLogging: Optional. If true, online prediction nodes send `stderr` and `stdout` streams to Cloud Logging. These can be more verbose than the standard access logs (see `onlinePredictionLogging`) and can incur higher cost. However, they are helpful for debugging. Note that [logs may incur a cost](/stackdriver/pricing), especially if your project receives prediction requests at a high QPS. Estimate your costs before enabling this option. Default is false. onlinePredictionLogging: Optional. If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each request. Note that [logs may incur a cost](/stackdriver/pricing), especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option. Default is false. regions: Optional. The list of regions where the model is going to be deployed. Only one region per model is supported. Defaults to 'us- central1' if nothing is set. See the available regions for AI Platform services. Note: * No matter where a model is deployed, it can always be accessed by users from anywhere, both for online and batch prediction. * The region for a batch prediction job is set by the region field when submitting the batch prediction job and does not take its value from this field. r cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) GoogleCloudMlV1Model.LabelsValueiaOptional. One or more labels that you can add, to organize your models. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue c`\rSrSrSr\R "S5r\R "S5rSr g)3GoogleCloudMlV1Model.LabelsValue.AdditionalPropertyir[rrrNrrr"r#r$rrr"r$rTr&rNr(rr"r#r\r2  'Y.. '%112FTXYr"r\rrrr,rArBrCrDrETr&rN)rrrrrrr-rr)r\rdefaultVersionr descriptionr/rbrgrrronlinePredictionConsoleLoggingonlinePredictionLoggingregionsr!rr"r#rvrv}s6p !!"89ZI%%Z:Z6))*BAF.%%a(+   a $  ! !- 3&   q !$#,#9#9!#< %2215  ! !!d 3'r"rvc>\rSrSrSr\R "SS5rSrg)GoogleCloudMlV1NasJobOutputizThe output of Neural Archhitecture Search (NAS) jobs. Fields: multiTrialJobOutputs: The output of a multi-trial Neural Architecture Search (NAS) job. /GoogleCloudMlV1NasJobOutputMultiTrialJobOutputsrrN) rrrrrrrmultiTrialJobOutputsr!rr"r#rrs#//0acder"rcB\rSrSrSr"SS\R 5r\R"SSSS9r \R"S 5r \R"SS 5r \R"S 5r \R"S 5r\R"SS 5r\R"S5rSrg).GoogleCloudMlV1NasJobOutputMultiTrialJobOutputia~The output of Multi-trial Neural Architecture Search (NAS) jobs. Enums: StateValueValuesEnum: Output only. The detailed state of the trial. Fields: allMetrics: All objective metrics for this Neural Architecture Search (NAS) job. endTime: Output only. End time for the trial. finalMetric: The final objective metric seen for this Neural Architecture Search (NAS) job. nasParamsStr: The parameters that are associated with this Neural Architecture Search (NAS) job. startTime: Output only. Start time for the trial. state: Output only. The detailed state of the trial. trialId: The trial id for these results. c8\rSrSrSrSrSrSrSrSr Sr S r S r S r g ) CGoogleCloudMlV1NasJobOutputMultiTrialJobOutput.StateValueValuesEnumir rrrr,rArBrCrDrNr rr"r#rrrr"r@GoogleCloudMlV1NasJobOutputMultiTrialJobOutputNasParameterMetricrTr&rr,rArBrCrDrN)rrrrrrrXrrr(rrr* nasParamsStrrrZr-r.r!rr"r#rrs$Y^^2%%&hjkvz{*  ! !! $'&&'iklm+&&q),##A&)   4a 8%  ! !! $'r"rc\rSrSrSr\R "S5"SS\R55r \R"SS5r \R"S5r \R"S5r\R"S 5rS rg ) riaAn observed value of a metric of the trial. Messages: MetricsValue: Reported metrics other than objective and model_flops Fields: metrics: Reported metrics other than objective and model_flops modelFlops: The model flops associated with the `objective_value`. objectiveValue: The objective value at this training step. trainingStep: The global training step for this metric. r cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) MGoogleCloudMlV1NasJobOutputMultiTrialJobOutputNasParameterMetric.MetricsValuei)zReported metrics other than objective and model_flops Messages: AdditionalProperty: An additional property for a MetricsValue object. Fields: additionalProperties: Additional properties of type MetricsValue c`\rSrSrSr\R "S5r\R"S5r Sr g)`GoogleCloudMlV1NasJobOutputMultiTrialJobOutputNasParameterMetric.MetricsValue.AdditionalPropertyi4zAn additional property for a MetricsValue object. Fields: key: Name of the additional property. value: A number attribute. rrrN) rrrrrrrrryr r!rr"r#r$r4s)   ! !! $c""1%er"r$rTr&rNr(rr"r# MetricsValuer)s2 &Y.. &%112FTXYr"rrrr,rArN)rrrrrrr-rr)rrrfry modelFlopsr1r7r2r!rr"r#rrs{  !!"89ZY&&Z:Z.  " ">1 5'##A&*''*.''*,r"rc<\rSrSrSr\R "SSSS9rSrg) riGzThe list of all MultiTrialJobOutput. Fields: multiTrialJobOutput: A GoogleCloudMlV1NasJobOutputMultiTrialJobOutput attribute. rrTr&rN) rrrrrrrmultiTrialJobOutputr!rr"r#rrGs!"../_abmqrr"rc\rSrSrSr\R "SS5r\R "SS5r\R"S5r \R"S5r S r g ) GoogleCloudMlV1NasSpeciRaSpec for Neural Architecture Search (NAS) jobs. Fields: multiTrialAlgorithmSpec: The spec of multi-trial algorithms. oneShotAlgorithmSpec: The spec of one-shot algorithms. previousNasJobId: The previous NAS job ID to resume search. The `search_space_spec` needs to be the same between this and previous NAS job and its job state is `FINISHED` or `CANCELLED`. searchSpaceSpec: Required. It defines the search space for Neural Architecture Search (NAS). -GoogleCloudMlV1NasSpecMultiTrialAlgorithmSpecr*GoogleCloudMlV1NasSpecOneShotAlgorithmSpecrr,rArN) rrrrrrrmultiTrialAlgorithmSpeconeShotAlgorithmSpecrpreviousNasJobIdsearchSpaceSpecr!rr"r#rrRsT &223bdef"//0\^_`**1-))!,/r"rc\rSrSrSr"SS\R 5r\R"S\RRS9r \R"S\RRS9r \R"S\RRS9r \R"S \RRS9r\R"SS 5r\R""S 5rS rg )riea\The spec of multi-trial Neural Architecture Search (NAS). Enums: MultiTrialAlgorithmValueValuesEnum: Optional. The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `NAS_MULTI_TRIAL_ALGORITHM_REINFORCEMENT_LEARNING`. Fields: initialIgnoredModelCount: If non-zero, it specifies the number of first models whose rewards will be ignored. maxFailedNasTrials: Optional. It decides when a Neural Architecture Search (NAS) job should fail. Defaults to zero. maxNasTrials: Optional. How many Neural Architecture Search (NAS) trials should be attempted. maxParallelNasTrials: Required. The number of Neural Architecture Search (NAS) trials to run concurrently. multiTrialAlgorithm: Optional. The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `NAS_MULTI_TRIAL_ALGORITHM_REINFORCEMENT_LEARNING`. nasTargetRewardMetric: Required. The TensorFlow summary tag that the controller tries to optimize. Its value needs to be consistent with the TensorFlow summary tag that is reported by trainer (customer provided dockers). c(\rSrSrSrSrSrSrSrSr g) PGoogleCloudMlV1NasSpecMultiTrialAlgorithmSpec.MultiTrialAlgorithmValueValuesEnumiaOptional. The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `NAS_MULTI_TRIAL_ALGORITHM_REINFORCEMENT_LEARNING`. Values: MULTI_TRIAL_ALGORITHM_UNSPECIFIED: REINFORCEMENT_LEARNING: The Reinforcement Learning Algorithm for Multi- trial Neural Architecture Search (NAS). GRID_SEARCH: The Grid Search Algorithm for Multi-trial Neural Architecture Search (NAS). REGULARIZED_EVOLUTION: The Regularized evolution Algorithm for Multi- trial Neural Architecture Search (NAS). rrrr,rN) rrrrr!MULTI_TRIAL_ALGORITHM_UNSPECIFIEDREINFORCEMENT_LEARNINGr8REGULARIZED_EVOLUTIONr!rr"r#"MultiTrialAlgorithmValueValuesEnumrs  )*%Kr"rrr5rr,rArBrCrN)rrrrrrrXrr7r8r9initialIgnoredModelCountmaxFailedNasTrials maxNasTrialsmaxParallelNasTrialsrZmultiTrialAlgorithmrnasTargetRewardMetricr!rr"r#rres29>>$'33Ay?P?P?V?VW --a9J9J9P9PQ''93D3D3J3JK,"//9;L;L;R;RS!++,PRST#//2r"rch\rSrSrSr"SS\R 5r\R"SS5r Sr g)riaThe spec of one shot Neural Architecture Search (NAS). Enums: OneShotAlgorithmValueValuesEnum: Optional. The one-shot Neural Architecture Search (NAS) algorithm type. Defaults to `ONE_SHOT_ALGORITHM_REINFORCEMENT_LEARNING`. Fields: oneShotAlgorithm: Optional. The one-shot Neural Architecture Search (NAS) algorithm type. Defaults to `ONE_SHOT_ALGORITHM_REINFORCEMENT_LEARNING`. c \rSrSrSrSrSrSrg)JGoogleCloudMlV1NasSpecOneShotAlgorithmSpec.OneShotAlgorithmValueValuesEnumia0Optional. The one-shot Neural Architecture Search (NAS) algorithm type. Defaults to `ONE_SHOT_ALGORITHM_REINFORCEMENT_LEARNING`. Values: ONE_SHOT_ALGORITHM_UNSPECIFIED: REINFORCEMENT_LEARNING: The Reinforcement Learning Algorithm for one- shot Neural Architecture Search (NAS). rrrN)rrrrrONE_SHOT_ALGORITHM_UNSPECIFIEDrr!rr"r#OneShotAlgorithmValueValuesEnumrs&'"r"rrrN) rrrrrrrXrrZoneShotAlgorithmr!rr"r#rrs-    (()JANr"rc\rSrSrSr"SS\R 5r\R"S5"SS\R55r \R"S5r \R"S 5r\R"S 5r\R""SS 5r\R"S 5r\R("SS 5r\R,"S5r\R"S5r\R""SS5rSrg) GoogleCloudMlV1OperationMetadataiaFRepresents the metadata of the long-running operation. Enums: OperationTypeValueValuesEnum: The operation type. Messages: LabelsValue: The user labels, inherited from the model or the model version being operated on. Fields: createTime: The time the operation was submitted. endTime: The time operation processing completed. isCancellationRequested: Indicates whether a request to cancel this operation has been made. labels: The user labels, inherited from the model or the model version being operated on. modelName: Contains the name of the model associated with the operation. operationType: The operation type. projectNumber: Contains the project number associated with the operation. startTime: The time operation processing started. version: Contains the version associated with the operation. c4\rSrSrSrSrSrSrSrSr Sr S r S r g ) =GoogleCloudMlV1OperationMetadata.OperationTypeValueValuesEnumiaThe operation type. Values: OPERATION_TYPE_UNSPECIFIED: Unspecified operation type. CREATE_VERSION: An operation to create a new version. DELETE_VERSION: An operation to delete an existing version. DELETE_MODEL: An operation to delete an existing model. UPDATE_MODEL: An operation to update an existing model. UPDATE_VERSION: An operation to update an existing version. UPDATE_CONFIG: An operation to update project configuration. rrrr,rArBrCrN) rrrrrOPERATION_TYPE_UNSPECIFIEDCREATE_VERSIONDELETE_VERSION DELETE_MODEL UPDATE_MODELUPDATE_VERSION UPDATE_CONFIGr!rr"r#OperationTypeValueValuesEnumrs- "#NNLLNMr"rr cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) ,GoogleCloudMlV1OperationMetadata.LabelsValueizThe user labels, inherited from the model or the model version being operated on. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue c`\rSrSrSr\R "S5r\R "S5rSr g)?GoogleCloudMlV1OperationMetadata.LabelsValue.AdditionalPropertyir[rrrNrrr"r#r$rrr"r$rTr&rNr(rr"r#r\rrr"r\rrr,rArBrCrDrErrFrN)rrrrrrrXrrr-r)r\rrrrrisCancellationRequestedrrgrrZ operationTyper7 projectNumberrversionr!rr"r#rrs.Y^^( !!"89ZI%%Z:Z0$$Q'*  ! !! $'%2215  ! !- 3&##A&)%%&DaH-((+-##A&)  " "#;Q ?'r"rch\rSrSrSr"SS\R 5r"SS\R 5r\R"SSS 9r \R"S SS 9r \R"S 5r \R"S 5r\R"S 5r\R "SS5r\R "SS5rSrg)rCiaRepresents a single hyperparameter to optimize. Enums: ScaleTypeValueValuesEnum: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`). TypeValueValuesEnum: Required. The type of the parameter. Fields: categoricalValues: Required if type is `CATEGORICAL`. The list of possible categories. discreteValues: Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values. maxValue: Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`. minValue: Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER. parameterName: Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate". scaleType: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`). type: Required. The type of the parameter. c(\rSrSrSrSrSrSrSrSr g) 5GoogleCloudMlV1ParameterSpec.ScaleTypeValueValuesEnumi$aOptional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`). Values: NONE: By default, no scaling is applied. UNIT_LINEAR_SCALE: Scales the feasible space to (0, 1) linearly. UNIT_LOG_SCALE: Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive. UNIT_REVERSE_LOG_SCALE: Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive. rrrr,rN) rrrrrNONEUNIT_LINEAR_SCALEUNIT_LOG_SCALEUNIT_REVERSE_LOG_SCALEr!rr"r#ScaleTypeValueValuesEnumr$s  DNr"rc,\rSrSrSrSrSrSrSrSr Sr g ) 0GoogleCloudMlV1ParameterSpec.TypeValueValuesEnumi8Required. The type of the parameter. Values: PARAMETER_TYPE_UNSPECIFIED: You must specify a valid type. Using this unspecified type will result in an error. DOUBLE: Type for real-valued parameters. INTEGER: Type for integral parameters. CATEGORICAL: The parameter is categorical, with a value chosen from the categories field. DISCRETE: The parameter is real valued, with a fixed set of feasible points. If `type==DISCRETE`, feasible_points must be provided, and {`min_value`, `max_value`} will be ignored. rrrr,rArN rrrrrPARAMETER_TYPE_UNSPECIFIEDDOUBLEINTEGER CATEGORICALDISCRETEr!rr"r#rVr8# "# FGKHr"rVrTr&rr,rArBrCrDrN)rrrrrrrXrrVrcategoricalValuesrydiscreteValuesmaxValueminValue parameterNamerZ scaleTyper[r!rr"r#rCrCs<(INN( ++A=''D9.  ! !! $(  ! !! $(''*-!!"\rSrSrSr\R "SS5rSrg)GoogleCloudMlV1PredictRequestiUzRequest for predictions to be issued against a trained model. Fields: httpBody: Required. The prediction request body. Refer to the [request body details section](#request-body-details) for more information on how to structure your request. r rrNrrr"r#rrUs # #$7 ;(r"rc\rSrSrSr"SS\R 5r"SS\R 5r"SS\R 5r \R"S S 5r \R"S 5r \R"SS 5r\R"SS 5r\R"S5r\R$"SSS9r\R"S5r\R$"S5r\R"SS5r\R$"S5r\R$"S5r\R$"S5r\R$"S5r\R$"SSS9r\R$"S5r\R$"S5r\R$"S5rSrg)r]iaaRepresents input parameters for a prediction job. Enums: DataFormatValueValuesEnum: Required. The format of the input data files. FrameworkValueValuesEnum: Optional. The framework used to train this model. Only needed if model_version is a GCS path. Otherwise the framework specified during version creation will be used. OutputDataFormatValueValuesEnum: Optional. Format of the output data files, defaults to JSON. Fields: accelerator: Optional. The type and number of accelerators to be attached to each machine running the job. batchSize: Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter. dataFormat: Required. The format of the input data files. framework: Optional. The framework used to train this model. Only needed if model_version is a GCS path. Otherwise the framework specified during version creation will be used. initialWorkerCount: Optional. The initial number of workers to be used for parallel processing. Defaults to 0 if one wants the service to figure out the number. The actual number of workers being used may change after the job starts depending on the autoscaling policy. inputPaths: Required. The Cloud Storage location of the input data files. May contain wildcards. maxWorkerCount: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified. modelName: Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"` outputDataFormat: Optional. Format of the output data files, defaults to JSON. outputPath: Required. The output Google Cloud Storage location. region: Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services. runtimeVersion: Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri. signatureName: Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVIN G_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved _model/signature_constants) , which is "serving_default". tagsOverride: Optional. The set of tags to select which meta graph defined in the SavedModel to use for this job. Please refer to [SavedModel](https://www.tensorflow.org/serving/serving_basic) for information about how to use tags. Overrides the default tags when predicting from a deployed model version. When predicting from a model directory, the tag defaults to [SERVING](https://www.tensorflow.org/api_ docs/python/tf/saved_model/tag_constants) , which is "serve". uri: Use this field if you want to specify a Google Cloud Storage path for the model to use. versionName: Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"` workerType: Optional. The type of virtual machine to use for batch prediction job's worker nodes. It supports all machine types available on GCP ( https://cloud.google.com/compute/docs/machine-types), subject to the availability in the specific region the job runs. c4\rSrSrSrSrSrSrSrSr Sr S r S r g ) 8GoogleCloudMlV1PredictionInput.DataFormatValueValuesEnumiaRequired. The format of the input data files. Values: DATA_FORMAT_UNSPECIFIED: Unspecified format. JSON: Each line of the file is a JSON dictionary representing one record. TEXT: Deprecated. Use JSON instead. TF_RECORD: The source file is a TFRecord file. Currently available only for input data. TF_RECORD_GZIP: The source file is a GZIP-compressed TFRecord file. Currently available only for input data. FILE_LIST: Each line of the file is the location of an instance to process. Currently available only for input data. CSV: Values are comma-separated rows, with keys in a separate file. Currently available only for output data. rrrr,rArBrCrN rrrrrrrTEXT TF_RECORDTF_RECORD_GZIP FILE_LISTCSVr!rr"r#rr-   D DINI Cr"rc(\rSrSrSrSrSrSrSrSr g) 7GoogleCloudMlV1PredictionInput.FrameworkValueValuesEnumirrrrr,rNrrr"r#rrrr"rc4\rSrSrSrSrSrSrSrSr Sr S r S r g ) >GoogleCloudMlV1PredictionInput.OutputDataFormatValueValuesEnumiaOptional. Format of the output data files, defaults to JSON. Values: DATA_FORMAT_UNSPECIFIED: Unspecified format. JSON: Each line of the file is a JSON dictionary representing one record. TEXT: Deprecated. Use JSON instead. TF_RECORD: The source file is a TFRecord file. Currently available only for input data. TF_RECORD_GZIP: The source file is a GZIP-compressed TFRecord file. Currently available only for input data. FILE_LIST: Each line of the file is the location of an instance to process. Currently available only for input data. CSV: Values are comma-separated rows, with keys in a separate file. Currently available only for output data. rrrr,rArBrCrNrrr"r#rrr r"rr<rrr,rArBrCTr&rDrErFrGrHrrrrrr rN) rrrrrrrXrrrrrr7rrZrr~rrrrrr outputPathrrrrrrrr!rr"r#r]r]as]BH ).. 2"  2&&'I1M+$$Q')""#>B*!!"(r"r)c\rSrSrSrSrg)'GoogleCloudMlV1SetDefaultVersionRequestiz2Request message for the SetDefaultVersion request.rNrrr"r#r6r6s;r"r6c\rSrSrSr\R "SS5r\R"S\RRS9r \R"S\RRS9rSrg ) rNiaConfig for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. See here for why https://arxiv.org/pdf/1706.03825.pdf Fields: featureNoiseSigma: Alternatively, set this to use different noise_sigma per feature. One entry per feature. No noise is added to features that are not set. noiseSigma: If set, this std. deviation will be used to apply noise to all features. noisySampleCount: The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity of IG increases by this factor as well. rrrr5r,rN)rrrrrrrfeatureNoiseSigmaryr8rzrr7r9noisySampleCountr!rr"r#rNrNs[  ,,-OQRS##Ay/@/@/F/FG*++Ay7H7H7N7NOr"rNc\rSrSrSrSrg)GoogleCloudMlV1StopTrialRequesti z)A GoogleCloudMlV1StopTrialRequest object.rNrrr"r#r;r; rr"r;c\rSrSrSr"SS\R 5r\R"S5r \R"S5r \R"S5r \R"SS5r \R"S S 5rS rg ) ri aA message representing a Study. Enums: StateValueValuesEnum: Output only. The detailed state of a study. Fields: createTime: Output only. Time at which the study was created. inactiveReason: Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED. name: Output only. The name of a study. state: Output only. The detailed state of a study. studyConfig: Required. Configuration of the study. c(\rSrSrSrSrSrSrSrSr g) )GoogleCloudMlV1Study.StateValueValuesEnumi a;Output only. The detailed state of a study. Values: STATE_UNSPECIFIED: The study state is unspecified. ACTIVE: The study is active. INACTIVE: The study is stopped due to an internal error. COMPLETED: The study is done when the service exhausts the parameter search space or max_trial_count is reached. rrrr,rN rrrrrrACTIVEINACTIVE COMPLETEDr!rr"r#rr>  FHIr"rrrr,rAGoogleCloudMlV1StudyConfigrBrN)rrrrrrrXrrrinactiveReasonrrZr-r studyConfigr!rr"r#rr so  Y^^ $$Q'*((+.   q !$   4a 8%&&'CQG+r"rc\rSrSrSr"SS\R 5r\R"SS5r \R"SS5r \R"SS S S 9r \R"S S S S 9r Srg)rDi4 atRepresents configuration of a study. Enums: AlgorithmValueValuesEnum: The search algorithm specified for the study. Fields: algorithm: The search algorithm specified for the study. automatedStoppingConfig: Configuration for automated stopping of unpromising Trials. metrics: Metric specs for the study. parameters: Required. The set of parameters to tune. c(\rSrSrSrSrSrSrSrSr g) 3GoogleCloudMlV1StudyConfig.AlgorithmValueValuesEnumiB aThe search algorithm specified for the study. Values: ALGORITHM_UNSPECIFIED: The default algorithm used by the Cloud AI Platform Vizier service. GAUSSIAN_PROCESS_BANDIT: Gaussian Process Bandit. GRID_SEARCH: Simple grid search within the feasible space. To use grid search, all parameters must be `INTEGER`, `CATEGORICAL`, or `DISCRETE`. RANDOM_SEARCH: Simple random search within the feasible space. rrrr,rN) rrrrrr7GAUSSIAN_PROCESS_BANDITr8r9r!rr"r#r;rIB s KMr"r;rrir$GoogleCloudMlV1StudyConfigMetricSpecr,Tr&'GoogleCloudMlV1StudyConfigParameterSpecrArN)rrrrrrrXr;rZrDrautomatedStoppingConfigrf parametersr!rr"r#rDrD4 sn "!!"rr"r#rBrQd sHHr"rBrrrN) rrrrrrrXrBrZrFrrr!rr"r#rKrKY s< INN    2A 6$   #&r"rKc \rSrSrSr"SS\R 5r"SS\R 5r\R"SS5r \R"SS S S 9r \R"S S 5r \R"SS5r \R"SS5r\R"S5r\R"SS5r\R"SS5r\R"SS5r\R("SS5r\R("SS5rSrg)rLit aRepresents a single parameter to optimize. Enums: ScaleTypeValueValuesEnum: How the parameter should be scaled. Leave unset for categorical parameters. TypeValueValuesEnum: Required. The type of the parameter. Fields: categoricalValueSpec: The value spec for a 'CATEGORICAL' parameter. childParameterSpecs: A child node is active if the parameter's value matches the child node's matching_parent_values. If two items in child_parameter_specs have the same name, they must have disjoint matching_parent_values. discreteValueSpec: The value spec for a 'DISCRETE' parameter. doubleValueSpec: The value spec for a 'DOUBLE' parameter. integerValueSpec: The value spec for an 'INTEGER' parameter. parameter: Required. The parameter name must be unique amongst all ParameterSpecs. parentCategoricalValues: A GoogleCloudMlV1StudyConfigParameterSpecMatchingParentCategoricalValueSpe c attribute. parentDiscreteValues: A GoogleCloudMlV1StudyConfigParameterSpecMatchingParentDiscreteValueSpec attribute. parentIntValues: A GoogleCloudMlV1StudyConfigParameterSpecMatchingParentIntValueSpec attribute. scaleType: How the parameter should be scaled. Leave unset for categorical parameters. type: Required. The type of the parameter. c(\rSrSrSrSrSrSrSrSr g) @GoogleCloudMlV1StudyConfigParameterSpec.ScaleTypeValueValuesEnumi aaHow the parameter should be scaled. Leave unset for categorical parameters. Values: SCALE_TYPE_UNSPECIFIED: By default, no scaling is applied. UNIT_LINEAR_SCALE: Scales the feasible space to (0, 1) linearly. UNIT_LOG_SCALE: Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive. UNIT_REVERSE_LOG_SCALE: Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive. rrrr,rN) rrrrrSCALE_TYPE_UNSPECIFIEDrrrr!rr"r#rrT s  Nr"rc,\rSrSrSrSrSrSrSrSr Sr g ) ;GoogleCloudMlV1StudyConfigParameterSpec.TypeValueValuesEnumi rrrrr,rArNrrr"r#rVrW rr"rV;GoogleCloudMlV1StudyConfigParameterSpecCategoricalValueSpecrrTr&8GoogleCloudMlV1StudyConfigParameterSpecDiscreteValueSpecr,6GoogleCloudMlV1StudyConfigParameterSpecDoubleValueSpecrA7GoogleCloudMlV1StudyConfigParameterSpecIntegerValueSpecrBrCIGoogleCloudMlV1StudyConfigParameterSpecMatchingParentCategoricalValueSpecrDFGoogleCloudMlV1StudyConfigParameterSpecMatchingParentDiscreteValueSpecrEAGoogleCloudMlV1StudyConfigParameterSpecMatchingParentIntValueSpecrFrGrHrN)rrrrrrrXrrVrcategoricalValueSpecchildParameterSpecsdiscreteValueSpecdoubleValueSpecintegerValueSpecr parameterparentCategoricalValuesparentDiscreteValuesparentIntValuesrZrr[r!rr"r#rLrLt s@&INN(#//0mopq!../XZ[fjk,,-gijk**+cefg/++,eghi##A&)%223~ABC"//0xz{|**+npqr/!!"GoogleCloudMlV1SuggestTrialsResponse.StudyStateValueValuesEnumiM a'The state of the study. Values: STATE_UNSPECIFIED: The study state is unspecified. ACTIVE: The study is active. INACTIVE: The study is stopped due to an internal error. COMPLETED: The study is done when the service exhausts the parameter search space or max_trial_count is reached. rrrr,rNr?rr"r#StudyStateValueValuesEnumr|M rCr"r}rrr,r|rATr&rN)rrrrrrrXr}rrrrZ studyStaterr~r!rr"r#rzrz? sa  )..   ! !! $'##A&)""#>B*  ! !"8!d K&r"rzc\rSrSrSr"SS\R 5r\R"SSS9r \R"S5r \R"S S 5r \R"S S 5r\R"S 5r\R"S5r\R"SS5r\R"S5r\R"S S5r\R"S5r\R"SS5r\R"S5r\R"SSS9r\R"S S5r\R"S5r\R"S5r\R"S5r\R"S5r\R"S5r\R"S5r\R@"SS5r!\R"S S!5r"\R"S"5r#\R"S#5r$\R"S S$5r%\R"S%5r&\R"S&5r'S'r(g())r_ib a,Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). Enums: ScaleTierValueValuesEnum: Required. Specifies the machine types, the number of replicas for workers and parameter servers. Fields: args: Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command. enableWebAccess: Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai- platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials). encryptionConfig: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek). evaluatorConfig: Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training- containers). evaluatorCount: Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero. evaluatorType: Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero. hyperparameters: Optional. The set of Hyperparameters to tune. jobDir: Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training. masterConfig: Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers). masterType: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine- types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine- types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using- tpus#configuring_a_custom_tpu_machine). nasJobSpec: Optional. The spec of a Neural Architecture Search (NAS) job. network: Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering). packageUris: Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100. parameterServerConfig: Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai- platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai- platform/training/docs/distributed-training-containers). parameterServerCount: Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero. parameterServerType: Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero. pythonModule: Required. The Python module name to run after installing the packages. pythonVersion: Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list). region: Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training. runtimeVersion: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning). scaleTier: Required. Specifies the machine types, the number of replicas for workers and parameter servers. scheduling: Optional. Scheduling options for a training job. serviceAccount: Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default. useChiefInTfConfig: Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai- platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container. workerConfig: Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using- gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers). workerCount: Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero. workerType: Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml- engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero. c0\rSrSrSrSrSrSrSrSr Sr S r g ) 5GoogleCloudMlV1TrainingInput.ScaleTierValueValuesEnumi aRequired. Specifies the machine types, the number of replicas for workers and parameter servers. Values: BASIC: A single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets. STANDARD_1: Many workers and a few parameter servers. PREMIUM_1: A large number of workers with many parameter servers. BASIC_GPU: A single worker instance [with a GPU](/ai- platform/training/docs/using-gpus). BASIC_TPU: A single worker instance with a [Cloud TPU](/ml- engine/docs/tensorflow/using-tpus). CUSTOM: The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You _must_ set `TrainingInput.masterType` to specify the type of machine to use for your master node. This is the only required setting. * You _may_ set `TrainingInput.workerCount` to specify the number of workers to use. If you specify one or more workers, you _must_ also set `TrainingInput.workerType` to specify the type of machine to use for your worker nodes. * You _may_ set `TrainingInput.parameterServerCount` to specify the number of parameter servers to use. If you specify one or more parameter servers, you _must_ also set `TrainingInput.parameterServerType` to specify the type of machine to use for your parameter servers. Note that all of your workers must use the same machine type, which can be different from your parameter server type and master type. Your parameter servers must likewise use the same machine type, which can be different from your worker type and master type. rrrr,rArBrN) rrrrrBASIC STANDARD_1 PREMIUM_1 BASIC_GPU BASIC_TPUCUSTOMr!rr"r#ScaleTierValueValuesEnumr s'> EJIII Fr"rrTr&rrr,rrArBrCr4rDrErFrGrrHrrrrrr rr)rN))rrrrrrrXrrrrrenableWebAccessrencryptionConfigevaluatorConfigr7evaluatorCount evaluatorTyper+jobDir masterConfig masterType nasJobSpecnetwork packageUrisparameterServerConfigparameterServerCountparameterServerType pythonModulerrrrZ scaleTier schedulingruseChiefInTfConfig workerConfig workerCountrr!rr"r#r_r_b sjX%%N   q4 0$**1-/++,MqQ**+I1M/))!,.''*-**+NPQR/   #&''(FJ,$$R(*%%&>C*  ! !" %'%%b48+#001OQST"//3!--b1&&r*,''+-   $&((,.!!"%112Y[\]!..q1((+/%11!4#003'44Q7''(EqI,  ! !"GUY Z&(()=qA-r"r`c\rSrSrSrSrg)ri zAttributes credit by computing the Shapley value taking advantage of the model's tree ensemble structure. Refer to this paper for more details: https://arxiv.org/abs/1705.07874 This attribution method is supported for XGBoost models. rNrrr"r#rr r%r"rc\rSrSrSr"SS\R 5r\R"S5r \R"S5r \R"SS5r \R"S 5r \R"SS S S 9r\R"S 5r\R"SSS S 9r\R"S5r\R$"SS5r\R("S5rSrg)r|i aA message representing a trial. Enums: StateValueValuesEnum: The detailed state of a trial. Fields: clientId: Output only. The identifier of the client that originally requested this trial. endTime: Output only. Time at which the trial's status changed to COMPLETED. finalMeasurement: The final measurement containing the objective value. infeasibleReason: Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true. measurements: A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations. name: Output only. Name of the trial assigned by the service. parameters: The parameters of the trial. startTime: Output only. Time at which the trial was started. state: The detailed state of a trial. trialInfeasible: Output only. If true, the parameters in this trial are not attempted again. c,\rSrSrSrSrSrSrSrSr Sr g ) )GoogleCloudMlV1Trial.StateValueValuesEnumi aThe detailed state of a trial. Values: STATE_UNSPECIFIED: The trial state is unspecified. REQUESTED: Indicates that a specific trial has been requested, but it has not yet been suggested by the service. ACTIVE: Indicates that the trial has been suggested. COMPLETED: Indicates that the trial is done, and either has a final_measurement set, or is marked as trial_infeasible. STOPPING: Indicates that the trial should stop according to the service. rrrr,rArN) rrrrrr REQUESTEDr@rBSTOPPINGr!rr"r#rr s# I FIHr"rrrr_r,rArBTr&rCGoogleCloudMlV1TrialParameterrDrErFrGrN)rrrrrrrXrrrurrrr measurementsrrNrrZr-rrrr!rr"r#r|r| s2Y^^$ " "1 %(  ! !! $'++,H!L**1-''(DaRVW,   q !$%%&EqSWX*##A&)   4a 8%**2./r"r|c\rSrSrSr\R "S5r\R"S5r \R"S5r \R"S5r Sr g) ri acA message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial. Fields: floatValue: Must be set if ParameterType is DOUBLE or DISCRETE. intValue: Must be set if ParameterType is INTEGER parameter: The name of the parameter. stringValue: Must be set if ParameterTypeis CATEGORICAL rrr,rArN)rrrrrrry floatValuer7intValuerrd stringValuer!rr"r#rr sI##A&*  # #A &(##A&)%%a(+r"rc\rSrSrSr"SS\R 5r"SS\R 5r\ R"S5"SS \R55r \R"S S 5r\R"S S 5r\R"SS5r\R""S5r\R""S5r\R""S5r\R""S5r\R,"S5r\R"SS5r\R2"SS5r\R""S5r\R8"S5r\R"S S5r\R""S5r\R""S5r \R""S5r!\R""S5r"\R"SS 5r#\R""S!5r$\R""S"5r%\R""S#S$S%9r&\R""S&5r'\R""S'5r(\R"S(S)5r)\R"S*S+5r*\R""S,5r+\R""S-5r,\R2"SS.5r-S/r.g0)1ri a.Represents a version of the model. Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions. You can get information about all of the versions of a given model by calling projects.models.versions.list. Enums: FrameworkValueValuesEnum: Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do **not** specify a framework if you're deploying a [custom prediction routine](/ai-platform/prediction/docs/custom-prediction-routines) or if you're using a [custom container](/ai-platform/prediction/docs/use- custom-container). StateValueValuesEnum: Output only. The state of a version. Messages: LabelsValue: Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. Fields: acceleratorConfig: Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the `machineType` field. Learn more about [using GPUs for online prediction](/ml-engine/docs/machine-types- online-prediction#gpus). autoScaling: Automatically scale the number of nodes used to serve the model in response to increases and decreases in traffic. Care should be taken to ramp up traffic according to the model's ability to scale or you will start seeing increases in latency and 429 response codes. container: Optional. Specifies a custom container to use for serving predictions. If you specify this field, then `machineType` is required. If you specify this field, then `deploymentUri` is optional. If you specify this field, then you must not specify `runtimeVersion`, `packageUris`, `framework`, `pythonVersion`, or `predictionClass`. createTime: Output only. The time the version was created. deploymentUri: The Cloud Storage URI of a directory containing trained model artifacts to be used to create the model version. See the [guide to deploying models](/ai-platform/prediction/docs/deploying-models) for more information. The total number of files under this directory must not exceed 1000. During projects.models.versions.create, AI Platform Prediction copies all files from the specified directory to a location managed by the service. From then on, AI Platform Prediction uses these copies of the model artifacts to serve predictions, not the original files in Cloud Storage, so this location is useful only as a historical record. If you specify container, then this field is optional. Otherwise, it is required. Learn [how to use this field with a custom container](/ai-platform/prediction/docs/custom-container- requirements#artifacts). description: Optional. The description specified for the version when it was created. errorMessage: Output only. The details of a failure or a cancellation. etag: `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read- modify-write cycle to perform model updates in order to avoid race conditions: An `etag` is returned in the response to `GetVersion`, and systems are expected to put that etag in the request to `UpdateVersion` to ensure that their change will be applied to the model as intended. explanationConfig: Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload. framework: Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do **not** specify a framework if you're deploying a [custom prediction routine](/ai- platform/prediction/docs/custom-prediction-routines) or if you're using a [custom container](/ai-platform/prediction/docs/use-custom-container). imageUri: Optional. The docker image to run for custom serving container. This image must be in Google Container Registry. isDefault: Output only. If true, this version will be used to handle prediction requests that do not specify a version. You can change the default version by calling projects.methods.versions.setDefault. labels: Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. lastMigrationModelId: Output only. The [AI Platform (Unified) `Model`](https://cloud.google.com/ai-platform- unified/docs/reference/rest/v1beta1/projects.locations.models) ID for the last [model migration](https://cloud.google.com/ai-platform- unified/docs/start/migrating-to-ai-platform-unified). lastMigrationTime: Output only. The last time this version was successfully [migrated to AI Platform (Unified)](https://cloud.google.com/ai-platform- unified/docs/start/migrating-to-ai-platform-unified). lastUseTime: Output only. The time the version was last used for prediction. machineType: Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. To learn about valid values for this field, read [Choosing a machine type for online prediction](/ai-platform/prediction/docs/machine-types-online- prediction). If this field is not specified and you are using a [regional endpoint](/ai-platform/prediction/docs/regional-endpoints), then the machine type defaults to `n1-standard-2`. If this field is not specified and you are using the global endpoint (`ml.googleapis.com`), then the machine type defaults to `mls1-c1-m2`. manualScaling: Manually select the number of nodes to use for serving the model. You should generally use `auto_scaling` with an appropriate `min_nodes` instead, but this option is available if you want more predictable billing. Beware that latency and error rates will increase if the traffic exceeds that capability of the system to serve it based on the selected number of nodes. modelClass: A string attribute. name: Required. The name specified for the version when it was created. The version name must be unique within the model it is created in. packageUris: Optional. Cloud Storage paths (`gs://...`) of packages for [custom prediction routines](/ml-engine/docs/tensorflow/custom- prediction-routines) or [scikit-learn pipelines with custom code](/ml- engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). For a custom prediction routine, one of these packages must contain your Predictor class (see [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-list). If you specify this field, you must also set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. predictionClass: Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the [`packageUris` field](#Version.FIELDS.package_uris). Specify this field if and only if you are deploying a [custom prediction routine (beta)](/ml- engine/docs/tensorflow/custom-prediction-routines). If you specify this field, you must set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction). The following code sample provides the Predictor interface: class Predictor(object): " " "Interface for constructing custom predictors." " " def predict(self, instances, **kwargs): " " "Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. **kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. " " " raise NotImplementedError() @classmethod def from_path(cls, model_dir): " " "Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. " " " raise NotImplementedError() Learn more about [the Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/custom- prediction-routines). pythonVersion: Required. The version of Python used in prediction. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list). requestLoggingConfig: Optional. *Only* specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version. routes: Optional. Specifies paths on a custom container's HTTP server where AI Platform Prediction sends certain requests. If you specify this field, then you must also specify the `container` field. If you specify the `container` field and do not specify this field, it defaults to the following: ```json { "predict": "/v1/models/MODEL/versions/VERSION:predict", "health": "/v1/models/MODEL/versions/VERSION" } ``` See RouteMap for more details about these default values. runtimeVersion: Required. The AI Platform runtime version to use for this deployment. For more information, see the [runtime version list](/ml- engine/docs/runtime-version-list) and [how to manage runtime versions](/ml-engine/docs/versioning). serviceAccount: Optional. Specifies the service account for resource access control. If you specify this field, then you must also specify either the `containerSpec` or the `predictionClass` field. Learn more about [using a custom service account](/ai- platform/prediction/docs/custom-service-account). state: Output only. The state of a version. c(\rSrSrSrSrSrSrSrSr g) /GoogleCloudMlV1Version.FrameworkValueValuesEnumi a3Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version of the model to 1.4 or greater. Do **not** specify a framework if you're deploying a [custom prediction routine](/ai- platform/prediction/docs/custom-prediction-routines) or if you're using a [custom container](/ai-platform/prediction/docs/use-custom-container). Values: FRAMEWORK_UNSPECIFIED: Unspecified framework. Assigns a value based on the file suffix. TENSORFLOW: Tensorflow framework. SCIKIT_LEARN: Scikit-learn framework. XGBOOST: XGBoost framework. rrrr,rNrrr"r#rr s"JLGr"rc0\rSrSrSrSrSrSrSrSr Sr S r g ) +GoogleCloudMlV1Version.StateValueValuesEnumi aOutput only. The state of a version. Values: UNKNOWN: The version state is unspecified. READY: The version is ready for prediction. CREATING: The version is being created. New UpdateVersion and DeleteVersion requests will fail if a version is in the CREATING state. FAILED: The version failed to be created, possibly cancelled. `error_message` should contain the details of the failure. DELETING: The version is being deleted. New UpdateVersion and DeleteVersion requests will fail if a version is in the DELETING state. UPDATING: The version is being updated. New UpdateVersion and DeleteVersion requests will fail if a version is in the UPDATING state. rrrr,rArBrN) rrrrrUNKNOWNREADYCREATINGrDELETINGUPDATINGr!rr"r#rr s'"G EH FHHr"rr cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) "GoogleCloudMlV1Version.LabelsValuei aOptional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue c`\rSrSrSr\R "S5r\R "S5rSr g)5GoogleCloudMlV1Version.LabelsValue.AdditionalPropertyi r[rrrNrrr"r#r$r rr"r$rTr&rNr(rr"r#r\r rr"r\r<rrbrrr,rArBrCrDrErrFrGrHrrrrrr rrrrrTr&rrrrr!rrrrN)/rrrrrrrXrrrr-r)r\rr autoScaling containerrr deploymentUrirrar/rbrrZr~rrr isDefaultrglastMigrationModelIdlastMigrationTime lastUseTime machineType manualScaling modelClassrrpredictionClassrrequestLoggingConfigroutesrrr-r!rr"r#rr sBwr.Y^^2 !!"89ZI%%Z:Z6 ,,-OQRS&&'CQG+$$%CQG)$$Q'*''*-%%a(+&&q),   a $,,-OQRS!!"  y~~ ))!d;/    8! <'r"rc\rSrSrSr\R "SS5r\R"SSS9r \R"S5r S r g ) GoogleIamV1Bindingiu atAssociates `members`, or principals, with a `role`. Fields: condition: The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). members: Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other- app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how- to/kubernetes-service-accounts). For example, `my- project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `principal://iam.googleapis.com/locatio ns/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.goog leapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.co m/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{ attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePo ols/{pool_id}/*`: All identities in a workforce identity pool. * `princi pal://iam.googleapis.com/projects/{project_number}/locations/global/work loadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.c om/projects/{project_number}/locations/global/workloadIdentityPools/{poo l_id}/group/{group_id}`: A workload identity pool group. * `principalSet ://iam.googleapis.com/projects/{project_number}/locations/global/workloa dIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All identities in a workload identity pool with a certain attribute. * ` principalSet://iam.googleapis.com/projects/{project_number}/locations/gl obal/workloadIdentityPools/{pool_id}/*`: All identities in a workload identity pool. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other- app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. * `deleted:principal://iam.google apis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attr ibute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workfo rcePools/my-pool-id/subject/my-subject-attribute-value`. role: Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. For an overview of the IAM roles and permissions, see the [IAM documentation](https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see [here](https://cloud.google.com/iam/docs/understanding-roles). GoogleTypeExprrrTr&r,rN) rrrrrrr conditionrmembersroler!rr"r#rru s@Ob$$%5q9)  ! !!d 3'   q !$r"rc\rSrSrSr\R "SSSS9r\R "SSSS9r\R"S 5r \R"S \RRS 9rS rg )GoogleIamV1Policyi awAn Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members`, or principals, to a single `role`. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions; each `role` can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a `binding` can also specify a `condition`, which is a logical expression that allows access to a resource only if the expression evaluates to `true`. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). **JSON example:** ``` { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } ``` **YAML example:** ``` bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the [IAM documentation](https://cloud.google.com/iam/docs/). Fields: auditConfigs: Specifies cloud audit logging configuration for this policy. bindings: Associates a list of `members`, or principals, with a `role`. Optionally, may specify a `condition` that determines how and when the `bindings` are applied. Each of the `bindings` must contain at least one principal. The `bindings` in a `Policy` can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the `bindings` grant 50 different roles to `user:alice@example.com`, and not to any other principal, then you can add another 1,450 principals to the `bindings` in the `Policy`. etag: `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read- modify-write cycle to perform policy updates in order to avoid race conditions: An `etag` is returned in the response to `getIamPolicy`, and systems are expected to put that etag in the request to `setIamPolicy` to ensure that their change will be applied to the same version of the policy. **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. version: Specifies the format of the policy. Valid values are `0`, `1`, and `3`. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version `3`. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). rrTr&rrr,rAr5rN)rrrrrrr auditConfigsbindingsr/rbr7r8r9rr!rr"r#rr scEN''(@!dS,  # #$8!d K(   a $  " "1i.?.?.E.E F'r"rcb\rSrSrSr\R "SS5r\R"S5r Sr g)GoogleIamV1SetIamPolicyRequestia Request message for `SetIamPolicy` method. Fields: policy: REQUIRED: The complete policy to be applied to the `resource`. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them. updateMask: OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only the fields in the mask will be modified. If no mask is provided, the following default mask is used: `paths: "bindings, etag"` rrrrN) rrrrrrrpolicyr updateMaskr!rr"r#rrs,   ! !"5q 9&$$Q'*r"rc:\rSrSrSr\R "SSS9rSrg)$GoogleIamV1TestIamPermissionsRequesti+a3Request message for `TestIamPermissions` method. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). rTr&rN rrrrrrr permissionsr!rr"r#rr+s%%a$7+r"rc:\rSrSrSr\R "SSS9rSrg)%GoogleIamV1TestIamPermissionsResponsei8zResponse message for `TestIamPermissions` method. Fields: permissions: A subset of `TestPermissionsRequest.permissions` that the caller is allowed. rTr&rNrrr"r#rr8s%%a$7+r"rc\rSrSrSr\R "S5r\R"SSSS9r \R "SSS9r S r g ) 'GoogleLongrunningListOperationsResponseiCaThe response message for Operations.ListOperations. Fields: nextPageToken: The standard List next-page token. operations: A list of operations that matches the specified filter in the request. unreachable: Unordered list. Unreachable resources. Populated when the request sets `ListOperationsRequest.return_partial_success` and reads across collections e.g. when attempting to list all resources across all supported locations. rGoogleLongrunningOperationrTr&r,rN) rrrrrrrror operations unreachabler!rr"r#rrCsA ''*-%%&BAPTU*%%a$7+r"rcz\rSrSrSr\R "S5"SS\R55r \R "S5"SS\R55r \R"S5r \R"S S 5r\R"SS 5r\R "S 5r\R"SS 5rSrg)riUaThis resource represents a long-running operation that is the result of a network API call. Messages: MetadataValue: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. ResponseValue: The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. Fields: done: If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. error: The error result of the operation in case of failure or cancellation. metadata: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. name: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. response: The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. r cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) (GoogleLongrunningOperation.MetadataValueiaService-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. cb\rSrSrSr\R "S5r\R"SS5r Sr g);GoogleLongrunningOperation.MetadataValue.AdditionalPropertyizAn additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. rrrrNrrr"r#r$rr%r"r$rTr&rNr(rr"r# MetadataValuers4  AY.. A%112FTXYr"rcf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) (GoogleLongrunningOperation.ResponseValueiaThe normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. Messages: AdditionalProperty: An additional property for a ResponseValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. cb\rSrSrSr\R "S5r\R"SS5r Sr g);GoogleLongrunningOperation.ResponseValue.AdditionalPropertyizAn additional property for a ResponseValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. rrrrNrrr"r#r$r r%r"r$rTr&rNr(rr"r# ResponseValuers4 AY.. A%112FTXYr"r rGoogleRpcStatusrr,rArBrN)rrrrrrr-rr)rr rrdonererrormetadatarrresponser!rr"r#rrUs'R !!"89Zi''Z:Z6 !!"89Zi''Z:Z<    "$  !2A 6%  # #OQ 7(   q !$  # #OQ 7(r"rc\rSrSrSrSrg)GoogleProtobufEmptyiaA generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } rNrrr"r#rrr%r"rc\rSrSrSr\R "S5"SS\R55r \R"S\RRS9r \R"SSS S 9r\R "S 5rS rg )r iaThe `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). Messages: DetailsValueListEntry: A DetailsValueListEntry object. Fields: code: The status code, which should be an enum value of google.rpc.Code. details: A list of messages that carry the error details. There is a common set of message types for APIs to use. message: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. r cf\rSrSrSr"SS\R 5r\R"SSSS9r Sr g ) %GoogleRpcStatus.DetailsValueListEntryizA DetailsValueListEntry object. Messages: AdditionalProperty: An additional property for a DetailsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. cb\rSrSrSr\R "S5r\R"SS5r Sr g)8GoogleRpcStatus.DetailsValueListEntry.AdditionalPropertyizAn additional property for a DetailsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. rrrrNrrr"r#r$rr%r"r$rTr&rNr(rr"r#DetailsValueListEntryrr+r"rrr5rTr&r,rN)rrrrrrr-rr)rr7r8r9coderdetailsrmessager!rr"r#r r s|& !!"89Zi//Z:Z2   9+<+<+B+B C$  " "#:A M'  ! !! $'r"r c\rSrSrSr\R "S5r\R "S5r\R "S5r \R "S5r Sr g) riaqRepresents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. Fields: description: Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI. expression: Textual representation of an expression in Common Expression Language syntax. location: Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file. title: Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression. rrr,rArN) rrrrrrrr expressionlocationtitler!rr"r#rrsI:%%a(+$$Q'*  " "1 %(    "%r"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) MlProjectsExplainRequesti!a)A MlProjectsExplainRequest object. Fields: googleCloudMlV1ExplainRequest: A GoogleCloudMlV1ExplainRequest resource to be passed as the request body. name: Required. The resource name of a model or a version. Authorization: requires the `predict` permission on the specified resource. rrrTrequiredrN) rrrrrrrgoogleCloudMlV1ExplainRequestrrr!rr"r#r!r!!0#,"8"89XZ["\   q4 0$r"r!c:\rSrSrSr\R "SSS9rSrg)MlProjectsGetConfigRequesti/zRA MlProjectsGetConfigRequest object. Fields: name: Required. The project name. rTr"rN rrrrrrrrr!rr"r#r'r'/   q4 0$r"r'c`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) MlProjectsJobsCancelRequesti9zA MlProjectsJobsCancelRequest object. Fields: googleCloudMlV1CancelJobRequest: A GoogleCloudMlV1CancelJobRequest resource to be passed as the request body. name: Required. The name of the job to cancel. rrrTr"rN) rrrrrrrgoogleCloudMlV1CancelJobRequestrrr!rr"r#r+r+90%.$:$:;\^_$`!   q4 0$r"r+c`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) MlProjectsJobsCreateRequestiFzA MlProjectsJobsCreateRequest object. Fields: googleCloudMlV1Job: A GoogleCloudMlV1Job resource to be passed as the request body. parent: Required. The project name. rTrrTr"rN) rrrrrrrgoogleCloudMlV1Jobrparentr!rr"r#r/r/Fs/!--.BAF  T 2&r"r/c\rSrSrSr\R "S\RRS9r \R"SSS9r Sr g ) !MlProjectsJobsGetIamPolicyRequestiSa-A MlProjectsJobsGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rr5rTr"rN rrrrrrr7r8r9options_requestedPolicyVersionrresourcer!rr"r#r3r3S:*$-#9#9!YEVEVE\E\#]  " "1t 4(r"r3c:\rSrSrSr\R "SSS9rSrg)MlProjectsJobsGetRequestimzmA MlProjectsJobsGetRequest object. Fields: name: Required. The name of the job to get the description of. rTr"rNr(rr"r#r9r9mr)r"r9c\rSrSrSr\R "S5r\R"S\RRS9r \R "S5r \R "SSS 9r S rg ) MlProjectsJobsListRequestiwaA MlProjectsJobsListRequest object. Fields: filter: Optional. Specifies the subset of jobs to retrieve. You can filter on the value of one or more attributes of the job object. For example, retrieve jobs with a job identifier that starts with 'census': gcloud ai-platform jobs list --filter='jobId:census*' List all failed jobs with names that start with 'rnn': gcloud ai-platform jobs list --filter='jobId:rnn* AND state:FAILED' For more examples, see the guide to monitoring jobs. pageSize: Optional. The number of jobs to retrieve per "page" of results. If there are more remaining results than this number, the response message will contain a valid value in the `next_page_token` field. The default value is 20, and the maximum page size is 100. pageToken: Optional. A page token to request the next page of results. You get the token from the `next_page_token` field of the response from the previous call. parent: Required. The name of the project for which to list jobs. rrr5r,rATr"rNrrrrrrrfilterr7r8r9pageSize pageTokenr1r!rr"r#r;r;wsY(   #&  # #Ay/@/@/F/F G(##A&)  T 2&r"r;c\rSrSrSr\R "SS5r\R"SSS9r \R"S5r S r g ) MlProjectsJobsPatchRequestiaA MlProjectsJobsPatchRequest object. Fields: googleCloudMlV1Job: A GoogleCloudMlV1Job resource to be passed as the request body. name: Required. The job name. updateMask: Required. Specifies the path, relative to `Job`, of the field to update. To adopt etag mechanism, include `etag` field in the mask, and include the `etag` value in your job resource. For example, to change the labels of a job, the `update_mask` parameter would be specified as `labels`, `etag`, and the `PATCH` request body would specify the new value, as follows: { "labels": { "owner": "Google", "color": "Blue" } "etag": "33a64df551425fcc55e4d42a148795d9f25f89d4" } If `etag` matches the one on the server, the labels of the job will be replaced with the given ones, and the server end `etag` will be recalculated. Currently the only supported update masks are `labels` and `etag`. rTrrTr"r,rN) rrrrrrrr0rrrr!rr"r#rArAs?&!--.BAF   q4 0$$$Q'*r"rAc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) !MlProjectsJobsSetIamPolicyRequestiasA MlProjectsJobsSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rrrTr"rN rrrrrrrgoogleIamV1SetIamPolicyRequestrr6r!rr"r#rCrC0 $-#9#9:Z\]#^  " "1t 4(r"rCc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) 'MlProjectsJobsTestIamPermissionsRequestiaA MlProjectsJobsTestIamPermissionsRequest object. Fields: googleIamV1TestIamPermissionsRequest: A GoogleIamV1TestIamPermissionsRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rrrTr"rN rrrrrrr$googleIamV1TestIamPermissionsRequestrr6r!rr"r#rHrH0 *3)?)?@fhi)j&  " "1t 4(r"rHc:\rSrSrSr\R "SSS9rSrg)MlProjectsLocationsGetRequestiz]A MlProjectsLocationsGetRequest object. Fields: name: Required. The name of the location. rTr"rNr(rr"r#rMrMr)r"rMc\rSrSrSr\R "S\RRS9r \R"S5r \R"SSS9r S r g ) MlProjectsLocationsListRequestiaA MlProjectsLocationsListRequest object. Fields: pageSize: Optional. The number of locations to retrieve per "page" of results. If there are more remaining results than this number, the response message will contain a valid value in the `next_page_token` field. The default value is 20, and the maximum page size is 100. pageToken: Optional. A page token to request the next page of results. You get the token from the `next_page_token` field of the response from the previous call. parent: Required. The name of the project for which available locations are to be listed (since some locations might be whitelisted for specific projects). rr5rr,Tr"rN)rrrrrrr7r8r9r>rr?r1r!rr"r#rOrOsI  # #Ay/@/@/F/F G(##A&)  T 2&r"rOc:\rSrSrSr\R "SSS9rSrg)*MlProjectsLocationsOperationsCancelRequestizzA MlProjectsLocationsOperationsCancelRequest object. Fields: name: The name of the operation resource to be cancelled. rTr"rNr(rr"r#rQrQr)r"rQc:\rSrSrSr\R "SSS9rSrg)'MlProjectsLocationsOperationsGetRequestizgA MlProjectsLocationsOperationsGetRequest object. Fields: name: The name of the operation resource. rTr"rNr(rr"r#rSrSr)r"rSc\rSrSrSr\R "SS5r\R"SSS9r \R"S5r S r g ) 'MlProjectsLocationsStudiesCreateRequestiaA MlProjectsLocationsStudiesCreateRequest object. Fields: googleCloudMlV1Study: A GoogleCloudMlV1Study resource to be passed as the request body. parent: Required. The project and location that the study belongs to. Format: projects/{project}/locations/{location} studyId: Required. The ID to use for the study, which will become the final component of the study's resource name. rrrTr"r,rN) rrrrrrrgoogleCloudMlV1Studyrr1studyIdr!rr"r#rUrUs? #//0FJ  T 2&  ! !! $'r"rUc:\rSrSrSr\R "SSS9rSrg)'MlProjectsLocationsStudiesDeleteRequestiz]A MlProjectsLocationsStudiesDeleteRequest object. Fields: name: Required. The study name. rTr"rNr(rr"r#rYrYr)r"rYc:\rSrSrSr\R "SSS9rSrg)$MlProjectsLocationsStudiesGetRequestizZA MlProjectsLocationsStudiesGetRequest object. Fields: name: Required. The study name. rTr"rNr(rr"r#r[r[r)r"r[c:\rSrSrSr\R "SSS9rSrg)%MlProjectsLocationsStudiesListRequesti$zA MlProjectsLocationsStudiesListRequest object. Fields: parent: Required. The project and location that the study belongs to. Format: projects/{project}/locations/{location} rTr"rN rrrrrrrr1r!rr"r#r]r]$rkr"r]c`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) 5MlProjectsLocationsStudiesTrialsAddMeasurementRequesti/zA MlProjectsLocationsStudiesTrialsAddMeasurementRequest object. Fields: googleCloudMlV1AddTrialMeasurementRequest: A GoogleCloudMlV1AddTrialMeasurementRequest resource to be passed as the request body. name: Required. The trial name. r]rrTr"rN) rrrrrrr)googleCloudMlV1AddTrialMeasurementRequestrrr!rr"r#r`r`/s0/8.D.DEprs.t+   q4 0$r"r`c`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) >MlProjectsLocationsStudiesTrialsCheckEarlyStoppingStateRequesti=aA MlProjectsLocationsStudiesTrialsCheckEarlyStoppingStateRequest object. Fields: googleCloudMlV1CheckTrialEarlyStoppingStateRequest: A GoogleCloudMlV1CheckTrialEarlyStoppingStateRequest resource to be passed as the request body. name: Required. The trial name. rrrTr"rN) rrrrrrr2googleCloudMlV1CheckTrialEarlyStoppingStateRequestrrr!rr"r#rcrc=s<8A7M7MOCEF8G4   q4 0$r"rcc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) /MlProjectsLocationsStudiesTrialsCompleteRequestiKzA MlProjectsLocationsStudiesTrialsCompleteRequest object. Fields: googleCloudMlV1CompleteTrialRequest: A GoogleCloudMlV1CompleteTrialRequest resource to be passed as the request body. name: Required. The trial name.metat rrrTr"rN) rrrrrrr#googleCloudMlV1CompleteTrialRequestrrr!rr"r#rfrfKs0)2(>(>?dfg(h%   q4 0$r"rfc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) -MlProjectsLocationsStudiesTrialsCreateRequestiXzA MlProjectsLocationsStudiesTrialsCreateRequest object. Fields: googleCloudMlV1Trial: A GoogleCloudMlV1Trial resource to be passed as the request body. parent: Required. The name of the study that the trial belongs to. r|rrTr"rN) rrrrrrrgoogleCloudMlV1Trialrr1r!rr"r#ririX/#//0FJ  T 2&r"ric:\rSrSrSr\R "SSS9rSrg)-MlProjectsLocationsStudiesTrialsDeleteRequestiezcA MlProjectsLocationsStudiesTrialsDeleteRequest object. Fields: name: Required. The trial name. rTr"rNr(rr"r#rmrmer)r"rmc:\rSrSrSr\R "SSS9rSrg)*MlProjectsLocationsStudiesTrialsGetRequestioz`A MlProjectsLocationsStudiesTrialsGetRequest object. Fields: name: Required. The trial name. rTr"rNr(rr"r#roroor)r"roc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) 8MlProjectsLocationsStudiesTrialsListOptimalTrialsRequestiya,A MlProjectsLocationsStudiesTrialsListOptimalTrialsRequest object. Fields: googleCloudMlV1ListOptimalTrialsRequest: A GoogleCloudMlV1ListOptimalTrialsRequest resource to be passed as the request body. parent: Required. The name of the study that the pareto-optimal trial belongs to. ryrrTr"rN) rrrrrrr'googleCloudMlV1ListOptimalTrialsRequestrr1r!rr"r#rqrqys0-6,B,BClno,p)  T 2&r"rqc:\rSrSrSr\R "SSS9rSrg)+MlProjectsLocationsStudiesTrialsListRequestizA MlProjectsLocationsStudiesTrialsListRequest object. Fields: parent: Required. The name of the study that the trial belongs to. rTr"rNr^rr"r#rtrts  T 2&r"rtc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) +MlProjectsLocationsStudiesTrialsStopRequestizA MlProjectsLocationsStudiesTrialsStopRequest object. Fields: googleCloudMlV1StopTrialRequest: A GoogleCloudMlV1StopTrialRequest resource to be passed as the request body. name: Required. The trial name. r;rrTr"rN) rrrrrrrgoogleCloudMlV1StopTrialRequestrrr!rr"r#rvrvr-r"rvc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) .MlProjectsLocationsStudiesTrialsSuggestRequestiaA MlProjectsLocationsStudiesTrialsSuggestRequest object. Fields: googleCloudMlV1SuggestTrialsRequest: A GoogleCloudMlV1SuggestTrialsRequest resource to be passed as the request body. parent: Required. The name of the study that the trial belongs to. rxrrTr"rN) rrrrrrr#googleCloudMlV1SuggestTrialsRequestrr1r!rr"r#ryrys0)2(>(>?dfg(h%  T 2&r"ryc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) MlProjectsModelsCreateRequestizA MlProjectsModelsCreateRequest object. Fields: googleCloudMlV1Model: A GoogleCloudMlV1Model resource to be passed as the request body. parent: Required. The project name. rvrrTr"rN) rrrrrrrgoogleCloudMlV1Modelrr1r!rr"r#r|r|rkr"r|c:\rSrSrSr\R "SSS9rSrg)MlProjectsModelsDeleteRequestizZA MlProjectsModelsDeleteRequest object. Fields: name: Required. The name of the model. rTr"rNr(rr"r#rrr)r"rc\rSrSrSr\R "S\RRS9r \R"SSS9r Sr g ) #MlProjectsModelsGetIamPolicyRequestia/A MlProjectsModelsGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rr5rTr"rNr4rr"r#rrr7r"rc:\rSrSrSr\R "SSS9rSrg)MlProjectsModelsGetRequestizWA MlProjectsModelsGetRequest object. Fields: name: Required. The name of the model. rTr"rNr(rr"r#rrr)r"rc\rSrSrSr\R "S5r\R"S\RRS9r \R "S5r \R "SSS 9r S rg ) MlProjectsModelsListRequestiaA MlProjectsModelsListRequest object. Fields: filter: Optional. Specifies the subset of models to retrieve. pageSize: Optional. The number of models to retrieve per "page" of results. If there are more remaining results than this number, the response message will contain a valid value in the `next_page_token` field. The default value is 20, and the maximum page size is 100. pageToken: Optional. A page token to request the next page of results. You get the token from the `next_page_token` field of the response from the previous call. parent: Required. The name of the project whose models are to be listed. rrr5r,rATr"rNr<rr"r#rrY    #&  # #Ay/@/@/F/F G(##A&)  T 2&r"rc\rSrSrSr\R "SS5r\R"SSS9r \R"S5r S r g ) MlProjectsModelsPatchRequestiaA MlProjectsModelsPatchRequest object. Fields: googleCloudMlV1Model: A GoogleCloudMlV1Model resource to be passed as the request body. name: Required. The project name. updateMask: Required. Specifies the path, relative to `Model`, of the field to update. For example, to change the description of a model to "foo" and set its default version to "version_1", the `update_mask` parameter would be specified as `description`, `default_version.name`, and the `PATCH` request body would specify the new value, as follows: { "description": "foo", "defaultVersion": { "name":"version_1" } } Currently the supported update masks are `description` and `default_version.name`. rvrrTr"r,rN) rrrrrrrr}rrrr!rr"r#rrs? #//0FJ   q4 0$$$Q'*r"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) #MlProjectsModelsSetIamPolicyRequestiauA MlProjectsModelsSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rrrTr"rNrDrr"r#rrrFr"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) )MlProjectsModelsTestIamPermissionsRequesti"aA MlProjectsModelsTestIamPermissionsRequest object. Fields: googleIamV1TestIamPermissionsRequest: A GoogleIamV1TestIamPermissionsRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. rrrTr"rNrIrr"r#rr"rKr"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) %MlProjectsModelsVersionsCreateRequesti3zA MlProjectsModelsVersionsCreateRequest object. Fields: googleCloudMlV1Version: A GoogleCloudMlV1Version resource to be passed as the request body. parent: Required. The name of the model. rrrTr"rN) rrrrrrrgoogleCloudMlV1Versionrr1r!rr"r#rr3s/%112JAN  T 2&r"rc:\rSrSrSr\R "SSS9rSrg)%MlProjectsModelsVersionsDeleteRequesti@zA MlProjectsModelsVersionsDeleteRequest object. Fields: name: Required. The name of the version. You can get the names of all the versions of a model by calling projects.models.versions.list. rTr"rNr(rr"r#rr@s   q4 0$r"rc:\rSrSrSr\R "SSS9rSrg)"MlProjectsModelsVersionsGetRequestiKzaA MlProjectsModelsVersionsGetRequest object. Fields: name: Required. The name of the version. rTr"rNr(rr"r#rrKr)r"rc\rSrSrSr\R "S5r\R"S\RRS9r \R "S5r \R "SSS 9r S rg ) #MlProjectsModelsVersionsListRequestiUaA MlProjectsModelsVersionsListRequest object. Fields: filter: Optional. Specifies the subset of versions to retrieve. pageSize: Optional. The number of versions to retrieve per "page" of results. If there are more remaining results than this number, the response message will contain a valid value in the `next_page_token` field. The default value is 20, and the maximum page size is 100. pageToken: Optional. A page token to request the next page of results. You get the token from the `next_page_token` field of the response from the previous call. parent: Required. The name of the model for which to list the version. rrr5r,rATr"rNr<rr"r#rrUrr"rc\rSrSrSr\R "SS5r\R"SSS9r \R"S5r S r g ) $MlProjectsModelsVersionsPatchRequestijasA MlProjectsModelsVersionsPatchRequest object. Fields: googleCloudMlV1Version: A GoogleCloudMlV1Version resource to be passed as the request body. name: Required. The name of the model. updateMask: Required. Specifies the path, relative to `Version`, of the field to update. Must be present and non-empty. For example, to change the description of a version to "foo", the `update_mask` parameter would be specified as `description`, and the `PATCH` request body would specify the new value, as follows: ``` { "description": "foo" } ``` Currently the only supported update mask fields are `description`, `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`. However, you can only update `manualScaling.nodes` if the version uses a [Compute Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction). rrrTr"r,rN) rrrrrrrrrrrr!rr"r#rrjs?$%112JAN   q4 0$$$Q'*r"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) )MlProjectsModelsVersionsSetDefaultRequestiayA MlProjectsModelsVersionsSetDefaultRequest object. Fields: googleCloudMlV1SetDefaultVersionRequest: A GoogleCloudMlV1SetDefaultVersionRequest resource to be passed as the request body. name: Required. The name of the version to make the default for the model. You can get the names of all the versions of a model by calling projects.models.versions.list. r6rrTr"rN) rrrrrrr'googleCloudMlV1SetDefaultVersionRequestrrr!rr"r#rrs0 -6,B,BClno,p)   q4 0$r"rc:\rSrSrSr\R "SSS9rSrg)!MlProjectsOperationsCancelRequestizqA MlProjectsOperationsCancelRequest object. Fields: name: The name of the operation resource to be cancelled. rTr"rNr(rr"r#rrr)r"rc:\rSrSrSr\R "SSS9rSrg)MlProjectsOperationsGetRequestiz^A MlProjectsOperationsGetRequest object. Fields: name: The name of the operation resource. rTr"rNr(rr"r#rrr)r"rc\rSrSrSr\R "S5r\R "SSS9r\R"S\RRS9r \R "S 5r \R"S 5rS rg ) MlProjectsOperationsListRequestiaA MlProjectsOperationsListRequest object. Fields: filter: The standard list filter. name: The name of the operation's parent resource. pageSize: The standard list page size. pageToken: The standard list page token. returnPartialSuccess: When set to `true`, operations that are reachable are returned as normal, and those that are unreachable are returned in the [ListOperationsResponse.unreachable] field. This can only be `true` when reading across collections e.g. when `parent` is set to `"projects/example/locations/-"`. This field is not by default supported and will result in an `UNIMPLEMENTED` error if set unless explicitly documented otherwise in service or product specific documentation. rrTr"r,r5rArBrN)rrrrrrrr=rr7r8r9r>r?rrreturnPartialSuccessr!rr"r#rrsj   #&   q4 0$  # #Ay/@/@/F/F G(##A&)"//2r"rc`\rSrSrSr\R "SS5r\R"SSS9r Sr g ) MlProjectsPredictRequestia)A MlProjectsPredictRequest object. Fields: googleCloudMlV1PredictRequest: A GoogleCloudMlV1PredictRequest resource to be passed as the request body. name: Required. The resource name of a model or a version. Authorization: requires the `predict` permission on the specified resource. rrrTr"rN) rrrrrrrgoogleCloudMlV1PredictRequestrrr!rr"r#rrr%r"rc\rSrSrSr"SS\R 5r"SS\R 5r\R"SS5r \R"S5r \R"SS S S 9r \R"S 5r\R"S 5r\R"S5r\R"S5r\R$"SSS 9r\R"S5r\R"S5r\R"S5r\R"S5rSrg)StandardQueryParametersiaQuery parameters accepted by all methods. Enums: FXgafvValueValuesEnum: V1 error format. AltValueValuesEnum: Data format for response. Fields: f__xgafv: V1 error format. access_token: OAuth access token. alt: Data format for response. callback: JSONP fields: Selector specifying which fields to include in a partial response. key: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. oauth_token: OAuth 2.0 token for the current user. prettyPrint: Returns response with indentations and line breaks. quotaUser: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. trace: A tracing token of the form "token:" to include in api requests. uploadType: Legacy upload protocol for media (e.g. "media", "multipart"). upload_protocol: Upload protocol for media (e.g. "raw", "multipart"). c$\rSrSrSrSrSrSrSrg)*StandardQueryParameters.AltValueValuesEnumizData format for response. Values: json: Responses with Content-Type of application/json media: Media download with context-dependent Content-Type proto: Responses with Content-Type of application/x-protobuf rrrrN) rrrrrjsonmediaprotor!rr"r#AltValueValuesEnumrs D E Er"rc \rSrSrSrSrSrSrg)-StandardQueryParameters.FXgafvValueValuesEnumizFV1 error format. Values: _1: v1 error format _2: v2 error format rrrN)rrrrr_1_2r!rr"r#FXgafvValueValuesEnumrs B Br"rrrr,r)defaultrArBrCrDrETrFrGrHrrN)rrrrrrrXrrrZf__xgafvr access_tokenaltcallbackfieldsr oauth_tokenrr prettyPrint quotaUsertrace uploadTypeupload_protocolr!rr"r#rrs4 9>>  inn  !8! <(&&q),0!VD#  " "1 %(   #&a #%%a(+&&q$7+##A&)    #%$$R(*))"-/r"rrz$.xgafvr1r2r5zoptions.requestedPolicyVersionN)r __future__rapitools.base.protorpcliterrapitools.base.pyrrpackager)r r3r<r]rbrirkrlrwr}rrrrrrrrrrrrrrrrrrrr r'r4rrTrmrqruryr{rrrrrrr_rrdrvrrrrrrrrrCrr]r^rrr!rrr)r6rNr;rrDrKrLrXrYrZr[r\r]r^rtrxrzr_r`rr|rrrrrrrrrrrrrr rr!r'r+r/r3r9r;rArCrHrMrOrQrSrUrYr[r]r`rcrfrirmrorqrtrvryr|rrrrrrrrrrrrrrrrrrAddCustomJsonFieldMappingAddCustomJsonEnumMappingrrr"r#rs  '<%( :T )):TzV):):V"07y'8'807fH 0A0AH$H!2!2$HNCY->->C" -iN_N_ --)J[J[-" J (9(9 J ,I,=,=,&3i&7&73:7 1 1:7z #9;L;L # 9J9J '):K:K ' .)*;*;.$/I--/ M9#4#4 MzS9#4#4zSz * 1 1 * (i&7&7 (#I--#:O>O +T1 (9(9T1nRI4E4ER4kO**kO\ +i&7&7 + +9+<+< + + (9(9 +@i.?.?@ Ly/@/@ LM):):ML (9(9L P)*;*; P "i// " E9#4#4 E(!2!2(" "y'8'8 "F 1 1F<\49,,\4~f)"3"3f2%Y5F5F2%j(+yGXGX(+Vsi6G6Gs-Y..-&13I4E4E13hO1B1BO8M@y'8'8M@`M79#4#4M7` .%i//.%by'8'8 Hy/@/@ HG? 1 1G?TO>O '3PYPaPa32YM^M^24 HYHY4O9+<+<O" O)*;*; O L9+<+< LFn)9#4#4n)bOBI$5$5OBd):):5/9,,5/p)I$5$5)"a:Y..a:H RY%6%6R6%Y..%:= 1 1=DT"**T"nKG ))KG\(Y%6%6(" 89+<+< 88I,=,=88i.?.?8$i8!2!2i8X)++0%i''0%f!#Y&&!#H 1y00 11!2!21 1)"3"3 1 3)"3"3 35 (9(9541y0013 1 136(!2!2(2 5 (9(9 5 5i.?.?5"1I$5$513Y%6%63*11B1B11i.?.?1%i.?.?%"1i.?.?119+<+<13I,=,=3 1I!!114>""%'GIik""')IKkmr"