MCP Tools Reference: aiplatform.googleapis.com

Tool: update_model

Updates specific metadata fields (display_name, description, labels) of an existing Model or a specific version. Format: 'projects/{project_id}/locations/{region}/models/{model_id}'. CRITICAL: For {region}, use the region specified in the current context window. If no region is specified, prompt the user to provide one. Do not use 'global'. Use the updateMask parameter to specify fields (e.g., 'display_name', 'description'). Target a specific version using the '@' format in the model name.

The following sample demonstrate how to use curl to invoke the update_model MCP tool.

Curl Request
curl --location 'https://aiplatform.googleapis.com/mcp/generate' \ --header 'content-type: application/json' \ --header 'accept: application/json, text/event-stream' \ --data '{ "method": "tools/call", "params": { "name": "update_model", "arguments": { // provide these details according to the tool's MCP specification } }, "jsonrpc": "2.0", "id": 1 }'

Input Schema

Request message for ModelService.UpdateModel.

UpdateModelRequest

JSON representation
{ "model": { object (Model) }, "updateMask": string }

Fields
model	object (Model) Required. The Model which replaces the resource on the server. When Model Versioning is enabled, the model.name will be used to determine whether to update the model or model version. 1. model.name with the @ value, e.g. models/123@1, refers to a version specific update. 2. model.name without the @ value, e.g. models/123, refers to a model update. 3. model.name with @-, e.g. models/123@-, refers to a model update. 4. Supported model fields: display_name, description; supported version-specific fields: version_description. Labels are supported in both scenarios. Both the model labels and the version labels are merged when a model is returned. When updating labels, if the request is for model-specific update, model label gets updated. Otherwise, version labels get updated. 5. A model name or model version name fields update mismatch will cause a precondition error. 6. One request cannot update both the model and the version fields. You must update them separately.
updateMask	string (FieldMask format) Required. The update mask applies to the resource. For the FieldMask definition, see google.protobuf.FieldMask. This is a comma-separated list of fully qualified names of fields. Example: "user.displayName,photo".

Model

JSON representation

{ "name": string, "versionId": string, "versionAliases": [ string ], "versionCreateTime": string, "versionUpdateTime": string, "displayName": string, "description": string, "versionDescription": string, "defaultCheckpointId": string, "predictSchemata": { object (PredictSchemata) }, "metadataSchemaUri": string, "metadata": value, "supportedExportFormats": [ { object (ExportFormat) } ], "trainingPipeline": string, "pipelineJob": string, "containerSpec": { object (ModelContainerSpec) }, "artifactUri": string, "supportedDeploymentResourcesTypes": [ enum (DeploymentResourcesType) ], "supportedInputStorageFormats": [ string ], "supportedOutputStorageFormats": [ string ], "createTime": string, "updateTime": string, "deployedModels": [ { object (DeployedModelRef) } ], "explanationSpec": { object (ExplanationSpec) }, "etag": string, "labels": { string: string, ... }, "dataStats": { object (DataStats) }, "encryptionSpec": { object (EncryptionSpec) }, "modelSourceInfo": { object (ModelSourceInfo) }, "originalModelInfo": { object (OriginalModelInfo) }, "metadataArtifact": string, "baseModelSource": { object (BaseModelSource) }, "satisfiesPzs": boolean, "satisfiesPzi": boolean, "checkpoints": [ { object (Checkpoint) } ] }

Fields
name	string Identifier. The resource name of the Model.
versionId	string Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionAliases[]	string User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime	string (Timestamp format) Output only. Timestamp when this version was created. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
versionUpdateTime	string (Timestamp format) Output only. Timestamp when this version was most recently updated. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
displayName	string Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Model.
versionDescription	string The description of this version.
defaultCheckpointId	string The default checkpoint id of a model version.
predictSchemata	object (PredictSchemata) The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
metadataSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
metadata	value (Value format) Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
supportedExportFormats[]	object (ExportFormat) Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
trainingPipeline	string Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
pipelineJob	string Optional. This field is populated if the model is produced by a pipeline job.
containerSpec	object (ModelContainerSpec) Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.
artifactUri	string Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
supportedDeploymentResourcesTypes[]	enum (DeploymentResourcesType) Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedInputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
createTime	string (Timestamp format) Output only. Timestamp when this Model was uploaded into Vertex AI. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
updateTime	string (Timestamp format) Output only. Timestamp when this Model was most recently updated. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
deployedModels[]	object (DeployedModelRef) Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
explanationSpec	object (ExplanationSpec) The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
dataStats	object (DataStats) Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config.
encryptionSpec	object (EncryptionSpec) Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
modelSourceInfo	object (ModelSourceInfo) Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
originalModelInfo	object (OriginalModelInfo) Output only. If this Model is a copy of another Model, this contains info about the original.
metadataArtifact	string Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
baseModelSource	object (BaseModelSource) Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
satisfiesPzs	boolean Output only. Reserved for future use.
satisfiesPzi	boolean Output only. Reserved for future use.
checkpoints[]	object (Checkpoint) Optional. Output only. The checkpoints of the model.

Timestamp

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be between -62135596800 and 253402300799 inclusive (which corresponds to 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z).
nanos	integer Non-negative fractions of a second at nanosecond resolution. This field is the nanosecond portion of the duration, not an alternative to seconds. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be between 0 and 999,999,999 inclusive.

PredictSchemata

JSON representation
{ "instanceSchemaUri": string, "parametersSchemaUri": string, "predictionSchemaUri": string }

Fields
instanceSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Value

JSON representation
{ // Union field kind can be only one of the following: "nullValue": null, "numberValue": number, "stringValue": string, "boolValue": boolean, "structValue": { object }, "listValue": array // End of list of possible types for union field kind. }

Fields
Union field kind. The kind of value. kind can be only one of the following:
nullValue	null Represents a JSON null.
numberValue	number Represents a JSON number. Must not be NaN, Infinity or -Infinity, since those are not supported in JSON. This also cannot represent large Int64 values, since JSON format generally does not support them in its number type.
stringValue	string Represents a JSON string.
boolValue	boolean Represents a JSON boolean (true or false literal in JSON).
structValue	object (Struct format) Represents a JSON object.
listValue	array (ListValue format) Represents a JSON array.

Struct

JSON representation
{ "fields": { string: value, ... } }

Fields
fields	map (key: string, value: value (Value format)) Unordered map of dynamically typed values. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.

FieldsEntry

JSON representation
{ "key": string, "value": value }

Fields
key	string
value	value (Value format)

ListValue

JSON representation
{ "values": [ value ] }

Fields
values[]	value (Value format) Repeated field of dynamically typed values.

ExportFormat

JSON representation
{ "id": string, "exportableContents": [ enum (ExportableContent) ] }

Fields
id	string Output only. The ID of the export format. The possible format IDs are: tflite Used for Android mobile devices. edgetpu-tflite Used for Edge TPU devices. tf-saved-model A tensorflow model in SavedModel format. tf-js A TensorFlow.js model that can be used in the browser and in Node.js using JavaScript. core-ml Used for iOS mobile devices. custom-trained A Model that was uploaded or trained by custom code. genie A tuned Model Garden model.
exportableContents[]	enum (ExportableContent) Output only. The content of this Model that may be exported.

ModelContainerSpec

JSON representation

{ "imageUri": string, "command": [ string ], "args": [ string ], "env": [ { object (EnvVar) } ], "ports": [ { object (Port) } ], "predictRoute": string, "healthRoute": string, "invokeRoutePrefix": string, "grpcPorts": [ { object (Port) } ], "deploymentTimeout": string, "sharedMemorySizeMb": string, "startupProbe": { object (Probe) }, "healthProbe": { object (Probe) }, "livenessProbe": { object (Probe) } }

Fields
imageUri	string Required. Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
command[]	string Immutable. Specifies the command that runs when the container starts. This overrides the container's 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, 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. 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. In this field, you can reference environment variables set by Vertex AI 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.
args[]	string Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's 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. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI 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.
env[]	object (EnvVar) 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: [ { "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.
ports[]	object (Port) Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute	string Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
healthRoute	string Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
invokeRoutePrefix	string Immutable. Invoke route prefix for the custom container. "/*" is the only supported value right now. By setting this field, any non-root route on this model will be accessible with invoke http call eg: "/invoke/foo/bar", however the [PredictionService.Invoke] RPC is not supported yet. Only one of predict_route or invoke_route_prefix can be set, and we default to using predict_route if this field is not set. If this field is set, the Model can only be deployed to dedicated endpoint.
grpcPorts[]	object (Port) Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, gRPC requests to the container will be disabled. Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
deploymentTimeout	string (Duration format) Immutable. Deployment timeout. Limit for deployment timeout is 2 hours. A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".
sharedMemorySizeMb	string (int64 format) Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
startupProbe	object (Probe) Immutable. Specification for Kubernetes startup probe.
healthProbe	object (Probe) Immutable. Specification for Kubernetes readiness probe.
livenessProbe	object (Probe) Immutable. Specification for Kubernetes liveness probe.

EnvVar

JSON representation
{ "name": string, "value": string }

Fields
name	string Required. Name of the environment variable. Must be a valid C identifier.
value	string Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

Port

JSON representation
{ "containerPort": integer }

Fields
containerPort	integer The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

Duration

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive. Note: these bounds are computed from: 60 sec/min * 60 min/hr * 24 hr/day * 365.25 days/year * 10000 years
nanos	integer Signed fractions of a second at nanosecond resolution of the span of time. Durations less than one second are represented with a 0 seconds field and a positive or negative nanos field. For durations of one second or more, a non-zero value for the nanos field must be of the same sign as the seconds field. Must be from -999,999,999 to +999,999,999 inclusive.

Probe

JSON representation

{ "periodSeconds": integer, "timeoutSeconds": integer, "failureThreshold": integer, "successThreshold": integer, "initialDelaySeconds": integer, // Union field probe_type can be only one of the following: "exec": { object (ExecAction) }, "httpGet": { object (HttpGetAction) }, "grpc": { object (GrpcAction) }, "tcpSocket": { object (TcpSocketAction) } // End of list of possible types for union field probe_type. }

Fields
periodSeconds	integer How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds	integer Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
failureThreshold	integer Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'.
successThreshold	integer Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'.
initialDelaySeconds	integer Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'.
Union field probe_type. probe_type can be only one of the following:
exec	object (ExecAction) ExecAction probes the health of a container by executing a command.
httpGet	object (HttpGetAction) HttpGetAction probes the health of a container by sending an HTTP GET request.
grpc	object (GrpcAction) GrpcAction probes the health of a container by sending a gRPC request.
tcpSocket	object (TcpSocketAction) TcpSocketAction probes the health of a container by opening a TCP socket connection.

ExecAction

JSON representation
{ "command": [ string ] }

Fields

command[]

string Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

HttpGetAction

JSON representation
{ "path": string, "port": integer, "host": string, "scheme": string, "httpHeaders": [ { object (HttpHeader) } ] }

Fields
path	string Path to access on the HTTP server.
port	integer Number of the port to access on the container. Number must be in the range 1 to 65535.
host	string Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead.
scheme	string Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS".
httpHeaders[]	object (HttpHeader) Custom headers to set in the request. HTTP allows repeated headers.

HttpHeader

JSON representation
{ "name": string, "value": string }

Fields
name	string The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
value	string The header field value

GrpcAction

JSON representation
{ "port": integer, "service": string }

Fields
port	integer Port number of the gRPC service. Number must be in the range 1 to 65535.
service	string Service is the name of the service to place in the gRPC HealthCheckRequest. See https://github.com/grpc/grpc/blob/master/doc/health-checking.md. If this is not specified, the default behavior is defined by gRPC.

TcpSocketAction

JSON representation
{ "port": integer, "host": string }

Fields
port	integer Number of the port to access on the container. Number must be in the range 1 to 65535.
host	string Optional: Host name to connect to, defaults to the model serving container's IP.

DeployedModelRef

JSON representation
{ "endpoint": string, "deployedModelId": string, "checkpointId": string }

Fields
endpoint	string Immutable. A resource name of an Endpoint.
deployedModelId	string Immutable. An ID of a DeployedModel in the above Endpoint.
checkpointId	string Immutable. The ID of the Checkpoint deployed in the DeployedModel.

ExplanationSpec

JSON representation
{ "parameters": { object (ExplanationParameters) }, "metadata": { object (ExplanationMetadata) } }

Fields
parameters	object (ExplanationParameters) Required. Parameters that configure explaining of the Model's predictions.
metadata	object (ExplanationMetadata) Optional. Metadata describing the Model's input and output for explanation.

ExplanationParameters

JSON representation
{ "topK": integer, "outputIndices": array, // Union field method can be only one of the following: "sampledShapleyAttribution": { object (SampledShapleyAttribution) }, "integratedGradientsAttribution": { object (IntegratedGradientsAttribution) }, "xraiAttribution": { object (XraiAttribution) }, "examples": { object (Examples) } // End of list of possible types for union field method. }

Fields
topK	integer If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
outputIndices	array (ListValue format) If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
Union field method. method can be only one of the following:
sampledShapleyAttribution	object (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. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
integratedGradientsAttribution	object (IntegratedGradientsAttribution) An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
xraiAttribution	object (XraiAttribution) An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples	object (Examples) Example-based explanations that returns the nearest neighbors from the provided dataset.

SampledShapleyAttribution

JSON representation
{ "pathCount": integer }

Fields
pathCount	integer Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

IntegratedGradientsAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config 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. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for IG with 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

SmoothGradConfig

JSON representation
{ "noisySampleCount": integer, // Union field GradientNoiseSigma can be only one of the following: "noiseSigma": number, "featureNoiseSigma": { object (FeatureNoiseSigma) } // End of list of possible types for union field GradientNoiseSigma. }

Fields
noisySampleCount	integer The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
Union field GradientNoiseSigma. Represents the standard deviation of the gaussian kernel that will be used to add noise to the interpolated inputs prior to computing gradients. GradientNoiseSigma can be only one of the following:
noiseSigma	number This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
featureNoiseSigma	object (FeatureNoiseSigma) This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.

FeatureNoiseSigma

JSON representation
{ "noiseSigma": [ { object (NoiseSigmaForFeature) } ] }

Fields
noiseSigma[]	object (NoiseSigmaForFeature) Noise sigma per feature. No noise is added to features that are not set.

NoiseSigmaForFeature

JSON representation
{ "name": string, "sigma": number }

Fields
name	string The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma	number This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

BlurBaselineConfig

JSON representation
{ "maxBlurSigma": number }

Fields
maxBlurSigma	number 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.

XraiAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config 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. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for XRAI with 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

Examples

JSON representation
{ "neighborCount": integer, // Union field source can be only one of the following: "exampleGcsSource": { object (ExampleGcsSource) } // End of list of possible types for union field source. // Union field config can be only one of the following: "nearestNeighborSearchConfig": value, "presets": { object (Presets) } // End of list of possible types for union field config. }

Fields
neighborCount	integer The number of neighbors to return when querying for examples.
Union field source. source can be only one of the following:
exampleGcsSource	object (ExampleGcsSource) The Cloud Storage input instances.
Union field config. config can be only one of the following:
nearestNeighborSearchConfig	value (Value format) The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
presets	object (Presets) Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

ExampleGcsSource

JSON representation
{ "dataFormat": enum (DataFormat), "gcsSource": { object (GcsSource) } }

Fields
dataFormat	enum (DataFormat) The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource	object (GcsSource) The Cloud Storage location for the input instances.

GcsSource

JSON representation
{ "uris": [ string ] }

Fields
uris[]	string Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.

Presets

JSON representation
{ "modality": enum (Modality), // Union field _query can be only one of the following: "query": enum (Query) // End of list of possible types for union field _query. }

Fields
modality	enum (Modality) The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Union field _query. _query can be only one of the following:
query	enum (Query) Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

ExplanationMetadata

JSON representation
{ "inputs": { string: { object (InputMetadata) }, ... }, "outputs": { string: { object (OutputMetadata) }, ... }, "featureAttributionsSchemaUri": string, "latentSpaceSource": string }

Fields
inputs	map (key: string, value: object (InputMetadata)) Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
outputs	map (key: string, value: object (OutputMetadata)) Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
featureAttributionsSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource	string Name of the source to generate embeddings for example based explanations.

InputsEntry

JSON representation
{ "key": string, "value": { object (InputMetadata) } }

Fields
key	string
value	object (InputMetadata)

InputMetadata

JSON representation
{ "inputBaselines": [ value ], "inputTensorName": string, "encoding": enum (Encoding), "modality": string, "featureValueDomain": { object (FeatureValueDomain) }, "indicesTensorName": string, "denseShapeTensorName": string, "indexFeatureMapping": [ string ], "encodedTensorName": string, "encodedBaselines": [ value ], "visualization": { object (Visualization) }, "groupName": string }

Fields
inputBaselines[]	value (Value format) Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
inputTensorName	string Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
encoding	enum (Encoding) Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
modality	string Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
featureValueDomain	object (FeatureValueDomain) The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
indicesTensorName	string Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
denseShapeTensorName	string Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
indexFeatureMapping[]	string A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
encodedTensorName	string Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
encodedBaselines[]	value (Value format) A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
visualization	object (Visualization) Visualization configurations for image explanation.
groupName	string Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.

FeatureValueDomain

JSON representation
{ "minValue": number, "maxValue": number, "originalMean": number, "originalStddev": number }

Fields
minValue	number The minimum permissible value for this feature.
maxValue	number The maximum permissible value for this feature.
originalMean	number If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
originalStddev	number If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.

Visualization

JSON representation
{ "type": enum (Type), "polarity": enum (Polarity), "colorMap": enum (ColorMap), "clipPercentUpperbound": number, "clipPercentLowerbound": number, "overlayType": enum (OverlayType) }

Fields
type	enum (Type) Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
polarity	enum (Polarity) Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
colorMap	enum (ColorMap) The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
clipPercentUpperbound	number Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
clipPercentLowerbound	number Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
overlayType	enum (OverlayType) How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.

OutputsEntry

JSON representation
{ "key": string, "value": { object (OutputMetadata) } }

Fields
key	string
value	object (OutputMetadata)

OutputMetadata

JSON representation
{ "outputTensorName": string, // Union field display_name_mapping can be only one of the following: "indexDisplayNameMapping": value, "displayNameMappingKey": string // End of list of possible types for union field display_name_mapping. }

Fields
outputTensorName	string Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
Union field display_name_mapping. Defines how to map Attribution.output_index to Attribution.output_display_name. If neither of the fields are specified, Attribution.output_display_name will not be populated. display_name_mapping can be only one of the following:
indexDisplayNameMapping	value (Value format) Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
displayNameMappingKey	string Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.

LabelsEntry

JSON representation
{ "key": string, "value": string }

Fields
key	string
value	string

DataStats

JSON representation
{ "trainingDataItemsCount": string, "validationDataItemsCount": string, "testDataItemsCount": string, "trainingAnnotationsCount": string, "validationAnnotationsCount": string, "testAnnotationsCount": string }

Fields
trainingDataItemsCount	string (int64 format) Number of DataItems that were used for training this Model.
validationDataItemsCount	string (int64 format) Number of DataItems that were used for validating this Model during training.
testDataItemsCount	string (int64 format) Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0.
trainingAnnotationsCount	string (int64 format) Number of Annotations that are used for training this Model.
validationAnnotationsCount	string (int64 format) Number of Annotations that are used for validating this Model during training.
testAnnotationsCount	string (int64 format) Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0.

EncryptionSpec

JSON representation
{ "kmsKeyName": string }

Fields
kmsKeyName	string Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}.

ModelSourceInfo

JSON representation
{ "sourceType": enum (ModelSourceType), "copy": boolean }

Fields
sourceType	enum (ModelSourceType) Type of the model source.
copy	boolean If this Model is copy of another Model. If true then source_type pertains to the original.

OriginalModelInfo

JSON representation
{ "model": string }

Fields
model	string Output only. The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}

BaseModelSource

JSON representation
{ // Union field source can be only one of the following: "modelGardenSource": { object (ModelGardenSource) }, "genieSource": { object (GenieSource) } // End of list of possible types for union field source. }

Fields
Union field source. source can be only one of the following:
modelGardenSource	object (ModelGardenSource) Source information of Model Garden models.
genieSource	object (GenieSource) Information about the base model of Genie models.

ModelGardenSource

JSON representation
{ "publicModelName": string, "versionId": string, "skipHfModelCache": boolean }

Fields
publicModelName	string Required. The model garden source model resource name.
versionId	string Optional. The model garden source model version ID.
skipHfModelCache	boolean Optional. Whether to avoid pulling the model from the HF cache.

GenieSource

JSON representation
{ "baseModelUri": string }

Fields
baseModelUri	string Required. The public base model URI.

Checkpoint

JSON representation
{ "checkpointId": string, "epoch": string, "step": string }

Fields
checkpointId	string The ID of the checkpoint.
epoch	string (int64 format) The epoch of the checkpoint.
step	string (int64 format) The step of the checkpoint.

FieldMask

JSON representation
{ "paths": [ string ] }

Fields
paths[]	string The set of field mask paths.

Output Schema

A trained machine learning Model.

Model

JSON representation

{ "name": string, "versionId": string, "versionAliases": [ string ], "versionCreateTime": string, "versionUpdateTime": string, "displayName": string, "description": string, "versionDescription": string, "defaultCheckpointId": string, "predictSchemata": { object (PredictSchemata) }, "metadataSchemaUri": string, "metadata": value, "supportedExportFormats": [ { object (ExportFormat) } ], "trainingPipeline": string, "pipelineJob": string, "containerSpec": { object (ModelContainerSpec) }, "artifactUri": string, "supportedDeploymentResourcesTypes": [ enum (DeploymentResourcesType) ], "supportedInputStorageFormats": [ string ], "supportedOutputStorageFormats": [ string ], "createTime": string, "updateTime": string, "deployedModels": [ { object (DeployedModelRef) } ], "explanationSpec": { object (ExplanationSpec) }, "etag": string, "labels": { string: string, ... }, "dataStats": { object (DataStats) }, "encryptionSpec": { object (EncryptionSpec) }, "modelSourceInfo": { object (ModelSourceInfo) }, "originalModelInfo": { object (OriginalModelInfo) }, "metadataArtifact": string, "baseModelSource": { object (BaseModelSource) }, "satisfiesPzs": boolean, "satisfiesPzi": boolean, "checkpoints": [ { object (Checkpoint) } ] }

Fields
name	string Identifier. The resource name of the Model.
versionId	string Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionAliases[]	string User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime	string (Timestamp format) Output only. Timestamp when this version was created. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
versionUpdateTime	string (Timestamp format) Output only. Timestamp when this version was most recently updated. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
displayName	string Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Model.
versionDescription	string The description of this version.
defaultCheckpointId	string The default checkpoint id of a model version.
predictSchemata	object (PredictSchemata) The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
metadataSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
metadata	value (Value format) Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
supportedExportFormats[]	object (ExportFormat) Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
trainingPipeline	string Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
pipelineJob	string Optional. This field is populated if the model is produced by a pipeline job.
containerSpec	object (ModelContainerSpec) Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.
artifactUri	string Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
supportedDeploymentResourcesTypes[]	enum (DeploymentResourcesType) Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedInputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
createTime	string (Timestamp format) Output only. Timestamp when this Model was uploaded into Vertex AI. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
updateTime	string (Timestamp format) Output only. Timestamp when this Model was most recently updated. Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".
deployedModels[]	object (DeployedModelRef) Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
explanationSpec	object (ExplanationSpec) The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
dataStats	object (DataStats) Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config.
encryptionSpec	object (EncryptionSpec) Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
modelSourceInfo	object (ModelSourceInfo) Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
originalModelInfo	object (OriginalModelInfo) Output only. If this Model is a copy of another Model, this contains info about the original.
metadataArtifact	string Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
baseModelSource	object (BaseModelSource) Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
satisfiesPzs	boolean Output only. Reserved for future use.
satisfiesPzi	boolean Output only. Reserved for future use.
checkpoints[]	object (Checkpoint) Optional. Output only. The checkpoints of the model.

Timestamp

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be between -62135596800 and 253402300799 inclusive (which corresponds to 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z).
nanos	integer Non-negative fractions of a second at nanosecond resolution. This field is the nanosecond portion of the duration, not an alternative to seconds. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be between 0 and 999,999,999 inclusive.

PredictSchemata

JSON representation
{ "instanceSchemaUri": string, "parametersSchemaUri": string, "predictionSchemaUri": string }

Fields
instanceSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Value

JSON representation
{ // Union field kind can be only one of the following: "nullValue": null, "numberValue": number, "stringValue": string, "boolValue": boolean, "structValue": { object }, "listValue": array // End of list of possible types for union field kind. }

Fields
Union field kind. The kind of value. kind can be only one of the following:
nullValue	null Represents a JSON null.
numberValue	number Represents a JSON number. Must not be NaN, Infinity or -Infinity, since those are not supported in JSON. This also cannot represent large Int64 values, since JSON format generally does not support them in its number type.
stringValue	string Represents a JSON string.
boolValue	boolean Represents a JSON boolean (true or false literal in JSON).
structValue	object (Struct format) Represents a JSON object.
listValue	array (ListValue format) Represents a JSON array.

Struct

JSON representation
{ "fields": { string: value, ... } }

Fields
fields	map (key: string, value: value (Value format)) Unordered map of dynamically typed values. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.

FieldsEntry

JSON representation
{ "key": string, "value": value }

Fields
key	string
value	value (Value format)

ListValue

JSON representation
{ "values": [ value ] }

Fields
values[]	value (Value format) Repeated field of dynamically typed values.

ExportFormat

JSON representation
{ "id": string, "exportableContents": [ enum (ExportableContent) ] }

Fields
id	string Output only. The ID of the export format. The possible format IDs are: tflite Used for Android mobile devices. edgetpu-tflite Used for Edge TPU devices. tf-saved-model A tensorflow model in SavedModel format. tf-js A TensorFlow.js model that can be used in the browser and in Node.js using JavaScript. core-ml Used for iOS mobile devices. custom-trained A Model that was uploaded or trained by custom code. genie A tuned Model Garden model.
exportableContents[]	enum (ExportableContent) Output only. The content of this Model that may be exported.

ModelContainerSpec

JSON representation

{ "imageUri": string, "command": [ string ], "args": [ string ], "env": [ { object (EnvVar) } ], "ports": [ { object (Port) } ], "predictRoute": string, "healthRoute": string, "invokeRoutePrefix": string, "grpcPorts": [ { object (Port) } ], "deploymentTimeout": string, "sharedMemorySizeMb": string, "startupProbe": { object (Probe) }, "healthProbe": { object (Probe) }, "livenessProbe": { object (Probe) } }

Fields
imageUri	string Required. Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
command[]	string Immutable. Specifies the command that runs when the container starts. This overrides the container's 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, 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. 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. In this field, you can reference environment variables set by Vertex AI 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.
args[]	string Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's 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. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI 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.
env[]	object (EnvVar) 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: [ { "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.
ports[]	object (Port) Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute	string Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
healthRoute	string Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
invokeRoutePrefix	string Immutable. Invoke route prefix for the custom container. "/*" is the only supported value right now. By setting this field, any non-root route on this model will be accessible with invoke http call eg: "/invoke/foo/bar", however the [PredictionService.Invoke] RPC is not supported yet. Only one of predict_route or invoke_route_prefix can be set, and we default to using predict_route if this field is not set. If this field is set, the Model can only be deployed to dedicated endpoint.
grpcPorts[]	object (Port) Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, gRPC requests to the container will be disabled. Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
deploymentTimeout	string (Duration format) Immutable. Deployment timeout. Limit for deployment timeout is 2 hours. A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".
sharedMemorySizeMb	string (int64 format) Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
startupProbe	object (Probe) Immutable. Specification for Kubernetes startup probe.
healthProbe	object (Probe) Immutable. Specification for Kubernetes readiness probe.
livenessProbe	object (Probe) Immutable. Specification for Kubernetes liveness probe.

EnvVar

JSON representation
{ "name": string, "value": string }

Fields
name	string Required. Name of the environment variable. Must be a valid C identifier.
value	string Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

Port

JSON representation
{ "containerPort": integer }

Fields
containerPort	integer The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

Duration

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive. Note: these bounds are computed from: 60 sec/min * 60 min/hr * 24 hr/day * 365.25 days/year * 10000 years
nanos	integer Signed fractions of a second at nanosecond resolution of the span of time. Durations less than one second are represented with a 0 seconds field and a positive or negative nanos field. For durations of one second or more, a non-zero value for the nanos field must be of the same sign as the seconds field. Must be from -999,999,999 to +999,999,999 inclusive.

Probe

JSON representation

{ "periodSeconds": integer, "timeoutSeconds": integer, "failureThreshold": integer, "successThreshold": integer, "initialDelaySeconds": integer, // Union field probe_type can be only one of the following: "exec": { object (ExecAction) }, "httpGet": { object (HttpGetAction) }, "grpc": { object (GrpcAction) }, "tcpSocket": { object (TcpSocketAction) } // End of list of possible types for union field probe_type. }

Fields
periodSeconds	integer How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds	integer Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
failureThreshold	integer Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'.
successThreshold	integer Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'.
initialDelaySeconds	integer Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'.
Union field probe_type. probe_type can be only one of the following:
exec	object (ExecAction) ExecAction probes the health of a container by executing a command.
httpGet	object (HttpGetAction) HttpGetAction probes the health of a container by sending an HTTP GET request.
grpc	object (GrpcAction) GrpcAction probes the health of a container by sending a gRPC request.
tcpSocket	object (TcpSocketAction) TcpSocketAction probes the health of a container by opening a TCP socket connection.

ExecAction

JSON representation
{ "command": [ string ] }

Fields

command[]

string Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

HttpGetAction

JSON representation
{ "path": string, "port": integer, "host": string, "scheme": string, "httpHeaders": [ { object (HttpHeader) } ] }

Fields
path	string Path to access on the HTTP server.
port	integer Number of the port to access on the container. Number must be in the range 1 to 65535.
host	string Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead.
scheme	string Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS".
httpHeaders[]	object (HttpHeader) Custom headers to set in the request. HTTP allows repeated headers.

HttpHeader

JSON representation
{ "name": string, "value": string }

Fields
name	string The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
value	string The header field value

GrpcAction

JSON representation
{ "port": integer, "service": string }

Fields
port	integer Port number of the gRPC service. Number must be in the range 1 to 65535.
service	string Service is the name of the service to place in the gRPC HealthCheckRequest. See https://github.com/grpc/grpc/blob/master/doc/health-checking.md. If this is not specified, the default behavior is defined by gRPC.

TcpSocketAction

JSON representation
{ "port": integer, "host": string }

Fields
port	integer Number of the port to access on the container. Number must be in the range 1 to 65535.
host	string Optional: Host name to connect to, defaults to the model serving container's IP.

DeployedModelRef

JSON representation
{ "endpoint": string, "deployedModelId": string, "checkpointId": string }

Fields
endpoint	string Immutable. A resource name of an Endpoint.
deployedModelId	string Immutable. An ID of a DeployedModel in the above Endpoint.
checkpointId	string Immutable. The ID of the Checkpoint deployed in the DeployedModel.

ExplanationSpec

JSON representation
{ "parameters": { object (ExplanationParameters) }, "metadata": { object (ExplanationMetadata) } }

Fields
parameters	object (ExplanationParameters) Required. Parameters that configure explaining of the Model's predictions.
metadata	object (ExplanationMetadata) Optional. Metadata describing the Model's input and output for explanation.

ExplanationParameters

JSON representation
{ "topK": integer, "outputIndices": array, // Union field method can be only one of the following: "sampledShapleyAttribution": { object (SampledShapleyAttribution) }, "integratedGradientsAttribution": { object (IntegratedGradientsAttribution) }, "xraiAttribution": { object (XraiAttribution) }, "examples": { object (Examples) } // End of list of possible types for union field method. }

Fields
topK	integer If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
outputIndices	array (ListValue format) If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
Union field method. method can be only one of the following:
sampledShapleyAttribution	object (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. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
integratedGradientsAttribution	object (IntegratedGradientsAttribution) An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
xraiAttribution	object (XraiAttribution) An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples	object (Examples) Example-based explanations that returns the nearest neighbors from the provided dataset.

SampledShapleyAttribution

JSON representation
{ "pathCount": integer }

Fields
pathCount	integer Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

IntegratedGradientsAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config 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. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for IG with 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

SmoothGradConfig

JSON representation
{ "noisySampleCount": integer, // Union field GradientNoiseSigma can be only one of the following: "noiseSigma": number, "featureNoiseSigma": { object (FeatureNoiseSigma) } // End of list of possible types for union field GradientNoiseSigma. }

Fields
noisySampleCount	integer The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
Union field GradientNoiseSigma. Represents the standard deviation of the gaussian kernel that will be used to add noise to the interpolated inputs prior to computing gradients. GradientNoiseSigma can be only one of the following:
noiseSigma	number This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
featureNoiseSigma	object (FeatureNoiseSigma) This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.

FeatureNoiseSigma

JSON representation
{ "noiseSigma": [ { object (NoiseSigmaForFeature) } ] }

Fields
noiseSigma[]	object (NoiseSigmaForFeature) Noise sigma per feature. No noise is added to features that are not set.

NoiseSigmaForFeature

JSON representation
{ "name": string, "sigma": number }

Fields
name	string The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma	number This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

BlurBaselineConfig

JSON representation
{ "maxBlurSigma": number }

Fields
maxBlurSigma	number 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.

XraiAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config 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. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for XRAI with 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

Examples

JSON representation
{ "neighborCount": integer, // Union field source can be only one of the following: "exampleGcsSource": { object (ExampleGcsSource) } // End of list of possible types for union field source. // Union field config can be only one of the following: "nearestNeighborSearchConfig": value, "presets": { object (Presets) } // End of list of possible types for union field config. }

Fields
neighborCount	integer The number of neighbors to return when querying for examples.
Union field source. source can be only one of the following:
exampleGcsSource	object (ExampleGcsSource) The Cloud Storage input instances.
Union field config. config can be only one of the following:
nearestNeighborSearchConfig	value (Value format) The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
presets	object (Presets) Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

ExampleGcsSource

JSON representation
{ "dataFormat": enum (DataFormat), "gcsSource": { object (GcsSource) } }

Fields
dataFormat	enum (DataFormat) The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource	object (GcsSource) The Cloud Storage location for the input instances.

GcsSource

JSON representation
{ "uris": [ string ] }

Fields
uris[]	string Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.

Presets

JSON representation
{ "modality": enum (Modality), // Union field _query can be only one of the following: "query": enum (Query) // End of list of possible types for union field _query. }

Fields
modality	enum (Modality) The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Union field _query. _query can be only one of the following:
query	enum (Query) Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

ExplanationMetadata

JSON representation
{ "inputs": { string: { object (InputMetadata) }, ... }, "outputs": { string: { object (OutputMetadata) }, ... }, "featureAttributionsSchemaUri": string, "latentSpaceSource": string }

Fields
inputs	map (key: string, value: object (InputMetadata)) Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
outputs	map (key: string, value: object (OutputMetadata)) Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
featureAttributionsSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource	string Name of the source to generate embeddings for example based explanations.

InputsEntry

JSON representation
{ "key": string, "value": { object (InputMetadata) } }

Fields
key	string
value	object (InputMetadata)

InputMetadata

JSON representation
{ "inputBaselines": [ value ], "inputTensorName": string, "encoding": enum (Encoding), "modality": string, "featureValueDomain": { object (FeatureValueDomain) }, "indicesTensorName": string, "denseShapeTensorName": string, "indexFeatureMapping": [ string ], "encodedTensorName": string, "encodedBaselines": [ value ], "visualization": { object (Visualization) }, "groupName": string }

Fields
inputBaselines[]	value (Value format) Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
inputTensorName	string Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
encoding	enum (Encoding) Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
modality	string Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
featureValueDomain	object (FeatureValueDomain) The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
indicesTensorName	string Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
denseShapeTensorName	string Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
indexFeatureMapping[]	string A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
encodedTensorName	string Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
encodedBaselines[]	value (Value format) A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
visualization	object (Visualization) Visualization configurations for image explanation.
groupName	string Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.

FeatureValueDomain

JSON representation
{ "minValue": number, "maxValue": number, "originalMean": number, "originalStddev": number }

Fields
minValue	number The minimum permissible value for this feature.
maxValue	number The maximum permissible value for this feature.
originalMean	number If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
originalStddev	number If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.

Visualization

JSON representation
{ "type": enum (Type), "polarity": enum (Polarity), "colorMap": enum (ColorMap), "clipPercentUpperbound": number, "clipPercentLowerbound": number, "overlayType": enum (OverlayType) }

Fields
type	enum (Type) Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
polarity	enum (Polarity) Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
colorMap	enum (ColorMap) The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
clipPercentUpperbound	number Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
clipPercentLowerbound	number Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
overlayType	enum (OverlayType) How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.

OutputsEntry

JSON representation
{ "key": string, "value": { object (OutputMetadata) } }

Fields
key	string
value	object (OutputMetadata)

OutputMetadata

JSON representation
{ "outputTensorName": string, // Union field display_name_mapping can be only one of the following: "indexDisplayNameMapping": value, "displayNameMappingKey": string // End of list of possible types for union field display_name_mapping. }

Fields
outputTensorName	string Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
Union field display_name_mapping. Defines how to map Attribution.output_index to Attribution.output_display_name. If neither of the fields are specified, Attribution.output_display_name will not be populated. display_name_mapping can be only one of the following:
indexDisplayNameMapping	value (Value format) Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
displayNameMappingKey	string Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.

LabelsEntry

JSON representation
{ "key": string, "value": string }

Fields
key	string
value	string

DataStats

JSON representation
{ "trainingDataItemsCount": string, "validationDataItemsCount": string, "testDataItemsCount": string, "trainingAnnotationsCount": string, "validationAnnotationsCount": string, "testAnnotationsCount": string }

Fields
trainingDataItemsCount	string (int64 format) Number of DataItems that were used for training this Model.
validationDataItemsCount	string (int64 format) Number of DataItems that were used for validating this Model during training.
testDataItemsCount	string (int64 format) Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0.
trainingAnnotationsCount	string (int64 format) Number of Annotations that are used for training this Model.
validationAnnotationsCount	string (int64 format) Number of Annotations that are used for validating this Model during training.
testAnnotationsCount	string (int64 format) Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0.

EncryptionSpec

JSON representation
{ "kmsKeyName": string }

Fields
kmsKeyName	string Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}.

ModelSourceInfo

JSON representation
{ "sourceType": enum (ModelSourceType), "copy": boolean }

Fields
sourceType	enum (ModelSourceType) Type of the model source.
copy	boolean If this Model is copy of another Model. If true then source_type pertains to the original.

OriginalModelInfo

JSON representation
{ "model": string }

Fields
model	string Output only. The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}

BaseModelSource

JSON representation
{ // Union field source can be only one of the following: "modelGardenSource": { object (ModelGardenSource) }, "genieSource": { object (GenieSource) } // End of list of possible types for union field source. }

Fields
Union field source. source can be only one of the following:
modelGardenSource	object (ModelGardenSource) Source information of Model Garden models.
genieSource	object (GenieSource) Information about the base model of Genie models.

ModelGardenSource

JSON representation
{ "publicModelName": string, "versionId": string, "skipHfModelCache": boolean }

Fields
publicModelName	string Required. The model garden source model resource name.
versionId	string Optional. The model garden source model version ID.
skipHfModelCache	boolean Optional. Whether to avoid pulling the model from the HF cache.

GenieSource

JSON representation
{ "baseModelUri": string }

Fields
baseModelUri	string Required. The public base model URI.

Checkpoint

JSON representation
{ "checkpointId": string, "epoch": string, "step": string }

Fields
checkpointId	string The ID of the checkpoint.
epoch	string (int64 format) The epoch of the checkpoint.
step	string (int64 format) The step of the checkpoint.

Tool Annotations

Destructive Hint: ✅ | Idempotent Hint: ✅ | Read Only Hint: ❌ | Open World Hint: ❌