This product is not supported for your selected
Datadog site. (
).
ancestors
Type: UNORDERED_LIST_STRING
create_time
Type: TIMESTAMP
Provider name: createTime
Description: Output only. Time when the TrainingPipeline was created.
encryption_spec
Type: STRUCT
Provider name: encryptionSpec
Description: Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
kms_key_name
Type: STRING
Provider name: kmsKeyName
Description: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
end_time
Type: TIMESTAMP
Provider name: endTime
Description: Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error
Type: STRUCT
Provider name: error
Description: Output only. Only populated when the pipeline’s state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
code
Type: INT32
Provider name: code
Description: The status code, which should be an enum value of google.rpc.Code.
message
Type: STRING
Provider name: message
Description: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
gcp_display_name
Type: STRING
Provider name: displayName
Description: Required. The user-defined name of this TrainingPipeline.
Type: STRUCT
Provider name: inputDataConfig
Description: Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline’s training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
annotation_schema_uri
Type: STRING
Provider name: annotationSchemaUri
Description: Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotations_filter
Type: STRING
Provider name: annotationsFilter
Description: Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigquery_destination
Type: STRUCT
Provider name: bigqueryDestination
Description: Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = “bigquery”. * AIP_TRAINING_DATA_URI = “bigquery_destination.dataset___.training” * AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset___.validation” * AIP_TEST_DATA_URI = “bigquery_destination.dataset___.test”
output_uri
Type: STRING
Provider name: outputUri
Description: Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
dataset_id
Type: STRING
Provider name: datasetId
Description: Required. The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filter_split
Type: STRUCT
Provider name: filterSplit
Description: Split based on the provided filters for each set.
test_filter
Type: STRING
Provider name: testFilter
Description: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
training_filter
Type: STRING
Provider name: trainingFilter
Description: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validation_filter
Type: STRING
Provider name: validationFilter
Description: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
fraction_split
Type: STRUCT
Provider name: fractionSplit
Description: Split based on fractions defining the size of each set.
test_fraction
Type: DOUBLE
Provider name: testFraction
Description: The fraction of the input data that is to be used to evaluate the Model.
training_fraction
Type: DOUBLE
Provider name: trainingFraction
Description: The fraction of the input data that is to be used to train the Model.
validation_fraction
Type: DOUBLE
Provider name: validationFraction
Description: The fraction of the input data that is to be used to validate the Model.
gcs_destination
Type: STRUCT
Provider name: gcsDestination
Description: The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: “gs://…/training-.jsonl” * AIP_DATA_FORMAT = “jsonl” for non-tabular data, “csv” for tabular data * AIP_TRAINING_DATA_URI = “gcs_destination/dataset—/training-.${AIP_DATA_FORMAT}” * AIP_VALIDATION_DATA_URI = “gcs_destination/dataset—/validation-.${AIP_DATA_FORMAT}” * AIP_TEST_DATA_URI = “gcs_destination/dataset—/test-.${AIP_DATA_FORMAT}"
output_uri_prefix
Type: STRING
Provider name: outputUriPrefix
Description: Required. Google Cloud Storage URI to output directory. If the uri doesn’t end with ‘/’, a ‘/’ will be automatically appended. The directory is created if it doesn’t exist.
persist_ml_use_assignment
Type: BOOLEAN
Provider name: persistMlUseAssignment
Description: Whether to persist the ML use assignment to data item system labels.
predefined_split
Type: STRUCT
Provider name: predefinedSplit
Description: Supported only for tabular Datasets. Split based on a predefined key.
key
Type: STRING
Provider name: key
Description: Required. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
saved_query_id
Type: STRING
Provider name: savedQueryId
Description: Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratified_split
Type: STRUCT
Provider name: stratifiedSplit
Description: Supported only for tabular Datasets. Split based on the distribution of the specified column.
key
Type: STRING
Provider name: key
Description: Required. The key is a name of one of the Dataset’s data columns. The key provided must be for a categorical column.
test_fraction
Type: DOUBLE
Provider name: testFraction
Description: The fraction of the input data that is to be used to evaluate the Model.
training_fraction
Type: DOUBLE
Provider name: trainingFraction
Description: The fraction of the input data that is to be used to train the Model.
validation_fraction
Type: DOUBLE
Provider name: validationFraction
Description: The fraction of the input data that is to be used to validate the Model.
timestamp_split
Type: STRUCT
Provider name: timestampSplit
Description: Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
key
Type: STRING
Provider name: key
Description: Required. The key is a name of one of the Dataset’s data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
test_fraction
Type: DOUBLE
Provider name: testFraction
Description: The fraction of the input data that is to be used to evaluate the Model.
training_fraction
Type: DOUBLE
Provider name: trainingFraction
Description: The fraction of the input data that is to be used to train the Model.
validation_fraction
Type: DOUBLE
Provider name: validationFraction
Description: The fraction of the input data that is to be used to validate the Model.
labels
Type: UNORDERED_LIST_STRING
model_id
Type: STRING
Provider name: modelId
Description: Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
model_to_upload
Type: STRUCT
Provider name: modelToUpload
Description: Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline’s training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline’s state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload’s resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
artifact_uri
Type: STRING
Provider name: artifactUri
Description: Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
base_model_source
Type: STRUCT
Provider name: baseModelSource
Description: Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
genie_source
Type: STRUCT
Provider name: genieSource
Description: Information about the base model of Genie models.
base_model_uri
Type: STRING
Provider name: baseModelUri
Description: Required. The public base model URI.
model_garden_source
Type: STRUCT
Provider name: modelGardenSource
Description: Source information of Model Garden models.
public_model_name
Type: STRING
Provider name: publicModelName
Description: Required. The model garden source model resource name.
skip_hf_model_cache
Type: BOOLEAN
Provider name: skipHfModelCache
Description: Optional. Whether to avoid pulling the model from the HF cache.
version_id
Type: STRING
Provider name: versionId
Description: Optional. The model garden source model version ID.
container_spec
Type: STRUCT
Provider name: containerSpec
Description: 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.
args
Type: UNORDERED_LIST_STRING
Provider name: args
Description: 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.
command
Type: UNORDERED_LIST_STRING
Provider name: command
Description: 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.
deployment_timeout
Type: STRING
Provider name: deploymentTimeout
Description: Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
env
Type: UNORDERED_LIST_STRUCT
Provider name: env
Description: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
name
Type: STRING
Provider name: name
Description: Required. Name of the environment variable. Must be a valid C identifier.
value
Type: STRING
Provider name: value
Description: 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.
grpc_ports
Type: UNORDERED_LIST_STRUCT
Provider name: grpcPorts
Description: 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.
container_port
Type: INT32
Provider name: containerPort
Description: The number of the port to expose on the pod’s IP address. Must be a valid port number, between 1 and 65535 inclusive.
health_probe
Type: STRUCT
Provider name: healthProbe
Description: Immutable. Specification for Kubernetes readiness probe.
exec
Type: STRUCT
Provider name: exec
Description: ExecAction probes the health of a container by executing a command.
command
Type: UNORDERED_LIST_STRING
Provider name: command
Description: 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.
failure_threshold
Type: INT32
Provider name: failureThreshold
Description: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument ‘failureThreshold’.
grpc
Type: STRUCT
Provider name: grpc
Description: GrpcAction probes the health of a container by sending a gRPC request.
port
Type: INT32
Provider name: port
Description: Port number of the gRPC service. Number must be in the range 1 to 65535.
service
Type: STRING
Provider name: service
Description: 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.
http_get
Type: STRUCT
Provider name: httpGet
Description: HttpGetAction probes the health of a container by sending an HTTP GET request.
host
Type: STRING
Provider name: host
Description: Host name to connect to, defaults to the model serving container’s IP. You probably want to set “Host” in httpHeaders instead.
http_headers
Type: UNORDERED_LIST_STRUCT
Provider name: httpHeaders
Description: Custom headers to set in the request. HTTP allows repeated headers.
name
Type: STRING
Provider name: name
Description: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
value
Type: STRING
Provider name: value
Description: The header field value
path
Type: STRING
Provider name: path
Description: Path to access on the HTTP server.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
scheme
Type: STRING
Provider name: scheme
Description: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are “HTTP” or “HTTPS”.
initial_delay_seconds
Type: INT32
Provider name: initialDelaySeconds
Description: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument ‘initialDelaySeconds’.
period_seconds
Type: INT32
Provider name: periodSeconds
Description: 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’.
success_threshold
Type: INT32
Provider name: successThreshold
Description: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument ‘successThreshold’.
tcp_socket
Type: STRUCT
Provider name: tcpSocket
Description: TcpSocketAction probes the health of a container by opening a TCP socket connection.
host
Type: STRING
Provider name: host
Description: Optional: Host name to connect to, defaults to the model serving container’s IP.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
timeout_seconds
Type: INT32
Provider name: timeoutSeconds
Description: 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’.
health_route
Type: STRING
Provider name: healthRoute
Description: 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.)
image_uri
Type: STRING
Provider name: imageUri
Description: 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.
liveness_probe
Type: STRUCT
Provider name: livenessProbe
Description: Immutable. Specification for Kubernetes liveness probe.
exec
Type: STRUCT
Provider name: exec
Description: ExecAction probes the health of a container by executing a command.
command
Type: UNORDERED_LIST_STRING
Provider name: command
Description: 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.
failure_threshold
Type: INT32
Provider name: failureThreshold
Description: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument ‘failureThreshold’.
grpc
Type: STRUCT
Provider name: grpc
Description: GrpcAction probes the health of a container by sending a gRPC request.
port
Type: INT32
Provider name: port
Description: Port number of the gRPC service. Number must be in the range 1 to 65535.
service
Type: STRING
Provider name: service
Description: 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.
http_get
Type: STRUCT
Provider name: httpGet
Description: HttpGetAction probes the health of a container by sending an HTTP GET request.
host
Type: STRING
Provider name: host
Description: Host name to connect to, defaults to the model serving container’s IP. You probably want to set “Host” in httpHeaders instead.
http_headers
Type: UNORDERED_LIST_STRUCT
Provider name: httpHeaders
Description: Custom headers to set in the request. HTTP allows repeated headers.
name
Type: STRING
Provider name: name
Description: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
value
Type: STRING
Provider name: value
Description: The header field value
path
Type: STRING
Provider name: path
Description: Path to access on the HTTP server.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
scheme
Type: STRING
Provider name: scheme
Description: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are “HTTP” or “HTTPS”.
initial_delay_seconds
Type: INT32
Provider name: initialDelaySeconds
Description: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument ‘initialDelaySeconds’.
period_seconds
Type: INT32
Provider name: periodSeconds
Description: 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’.
success_threshold
Type: INT32
Provider name: successThreshold
Description: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument ‘successThreshold’.
tcp_socket
Type: STRUCT
Provider name: tcpSocket
Description: TcpSocketAction probes the health of a container by opening a TCP socket connection.
host
Type: STRING
Provider name: host
Description: Optional: Host name to connect to, defaults to the model serving container’s IP.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
timeout_seconds
Type: INT32
Provider name: timeoutSeconds
Description: 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’.
ports
Type: UNORDERED_LIST_STRUCT
Provider name: ports
Description: 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: json [ { "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.
container_port
Type: INT32
Provider name: containerPort
Description: The number of the port to expose on the pod’s IP address. Must be a valid port number, between 1 and 65535 inclusive.
predict_route
Type: STRING
Provider name: predictRoute
Description: 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.)
shared_memory_size_mb
Type: INT64
Provider name: sharedMemorySizeMb
Description: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
startup_probe
Type: STRUCT
Provider name: startupProbe
Description: Immutable. Specification for Kubernetes startup probe.
exec
Type: STRUCT
Provider name: exec
Description: ExecAction probes the health of a container by executing a command.
command
Type: UNORDERED_LIST_STRING
Provider name: command
Description: 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.
failure_threshold
Type: INT32
Provider name: failureThreshold
Description: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument ‘failureThreshold’.
grpc
Type: STRUCT
Provider name: grpc
Description: GrpcAction probes the health of a container by sending a gRPC request.
port
Type: INT32
Provider name: port
Description: Port number of the gRPC service. Number must be in the range 1 to 65535.
service
Type: STRING
Provider name: service
Description: 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.
http_get
Type: STRUCT
Provider name: httpGet
Description: HttpGetAction probes the health of a container by sending an HTTP GET request.
host
Type: STRING
Provider name: host
Description: Host name to connect to, defaults to the model serving container’s IP. You probably want to set “Host” in httpHeaders instead.
http_headers
Type: UNORDERED_LIST_STRUCT
Provider name: httpHeaders
Description: Custom headers to set in the request. HTTP allows repeated headers.
name
Type: STRING
Provider name: name
Description: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
value
Type: STRING
Provider name: value
Description: The header field value
path
Type: STRING
Provider name: path
Description: Path to access on the HTTP server.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
scheme
Type: STRING
Provider name: scheme
Description: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are “HTTP” or “HTTPS”.
initial_delay_seconds
Type: INT32
Provider name: initialDelaySeconds
Description: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument ‘initialDelaySeconds’.
period_seconds
Type: INT32
Provider name: periodSeconds
Description: 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’.
success_threshold
Type: INT32
Provider name: successThreshold
Description: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument ‘successThreshold’.
tcp_socket
Type: STRUCT
Provider name: tcpSocket
Description: TcpSocketAction probes the health of a container by opening a TCP socket connection.
host
Type: STRING
Provider name: host
Description: Optional: Host name to connect to, defaults to the model serving container’s IP.
port
Type: INT32
Provider name: port
Description: Number of the port to access on the container. Number must be in the range 1 to 65535.
timeout_seconds
Type: INT32
Provider name: timeoutSeconds
Description: 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’.
create_time
Type: TIMESTAMP
Provider name: createTime
Description: Output only. Timestamp when this Model was uploaded into Vertex AI.
data_stats
Type: STRUCT
Provider name: dataStats
Description: Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config.
test_annotations_count
Type: INT64
Provider name: testAnnotationsCount
Description: 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.
test_data_items_count
Type: INT64
Provider name: testDataItemsCount
Description: 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.
training_annotations_count
Type: INT64
Provider name: trainingAnnotationsCount
Description: Number of Annotations that are used for training this Model.
training_data_items_count
Type: INT64
Provider name: trainingDataItemsCount
Description: Number of DataItems that were used for training this Model.
validation_annotations_count
Type: INT64
Provider name: validationAnnotationsCount
Description: Number of Annotations that are used for validating this Model during training.
validation_data_items_count
Type: INT64
Provider name: validationDataItemsCount
Description: Number of DataItems that were used for validating this Model during training.
default_checkpoint_id
Type: STRING
Provider name: defaultCheckpointId
Description: The default checkpoint id of a model version.
deployed_models
Type: UNORDERED_LIST_STRUCT
Provider name: deployedModels
Description: Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
deployed_model_id
Type: STRING
Provider name: deployedModelId
Description: Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint
Type: STRING
Provider name: endpoint
Description: Immutable. A resource name of an Endpoint.
description
Type: STRING
Provider name: description
Description: The description of the Model.
encryption_spec
Type: STRUCT
Provider name: encryptionSpec
Description: 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.
kms_key_name
Type: STRING
Provider name: kmsKeyName
Description: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
etag
Type: STRING
Provider name: etag
Description: Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.
explanation_spec
Type: STRUCT
Provider name: explanationSpec
Description: 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.
metadata
Type: STRUCT
Provider name: metadata
Description: Optional. Metadata describing the Model’s input and output for explanation.
feature_attributions_schema_uri
Type: STRING
Provider name: featureAttributionsSchemaUri
Description: 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.
latent_space_source
Type: STRING
Provider name: latentSpaceSource
Description: Name of the source to generate embeddings for example based explanations.
parameters
Type: STRUCT
Provider name: parameters
Description: Required. Parameters that configure explaining of the Model’s predictions.
examples
Type: STRUCT
Provider name: examples
Description: Example-based explanations that returns the nearest neighbors from the provided dataset.
example_gcs_source
Type: STRUCT
Provider name: exampleGcsSource
Description: The Cloud Storage input instances.
data_format
Type: STRING
Provider name: dataFormat
Description: The format in which instances are given, if not specified, assume it’s JSONL format. Currently only JSONL format is supported.
Possible values:
DATA_FORMAT_UNSPECIFIED - Format unspecified, used when unset.
JSONL - Examples are stored in JSONL files.
gcs_source
Type: STRUCT
Provider name: gcsSource
Description: The Cloud Storage location for the input instances.
neighbor_count
Type: INT32
Provider name: neighborCount
Description: The number of neighbors to return when querying for examples.
presets
Type: STRUCT
Provider name: presets
Description: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
modality
Type: STRING
Provider name: modality
Description: 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.
Possible values:
MODALITY_UNSPECIFIED - Should not be set. Added as a recommended best practice for enums
IMAGE - IMAGE modality
TEXT - TEXT modality
TABULAR - TABULAR modality
query
Type: STRING
Provider name: query
Description: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
Possible values:
PRECISE - More precise neighbors as a trade-off against slower response.
FAST - Faster response as a trade-off against less precise neighbors.
integrated_gradients_attribution
Type: STRUCT
Provider name: integratedGradientsAttribution
Description: 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
blur_baseline_config
Type: STRUCT
Provider name: blurBaselineConfig
Description: 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
max_blur_sigma
Type: FLOAT
Provider name: maxBlurSigma
Description: 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.
smooth_grad_config
Type: STRUCT
Provider name: smoothGradConfig
Description: 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
feature_noise_sigma
Type: STRUCT
Provider name: featureNoiseSigma
Description: 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.
noise_sigma
Type: UNORDERED_LIST_STRUCT
Provider name: noiseSigma
Description: Noise sigma per feature. No noise is added to features that are not set.
name
Type: STRING
Provider name: name
Description: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma
Type: FLOAT
Provider name: sigma
Description: 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.
noise_sigma
Type: FLOAT
Provider name: noiseSigma
Description: 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.
noisy_sample_count
Type: INT32
Provider name: noisySampleCount
Description: 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.
step_count
Type: INT32
Provider name: stepCount
Description: 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.
sampled_shapley_attribution
Type: STRUCT
Provider name: sampledShapleyAttribution
Description: 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.
path_count
Type: INT32
Provider name: pathCount
Description: Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
top_k
Type: INT32
Provider name: topK
Description: 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.
xrai_attribution
Type: STRUCT
Provider name: xraiAttribution
Description: 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.
blur_baseline_config
Type: STRUCT
Provider name: blurBaselineConfig
Description: 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
max_blur_sigma
Type: FLOAT
Provider name: maxBlurSigma
Description: 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.
smooth_grad_config
Type: STRUCT
Provider name: smoothGradConfig
Description: 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
feature_noise_sigma
Type: STRUCT
Provider name: featureNoiseSigma
Description: 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.
noise_sigma
Type: UNORDERED_LIST_STRUCT
Provider name: noiseSigma
Description: Noise sigma per feature. No noise is added to features that are not set.
name
Type: STRING
Provider name: name
Description: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma
Type: FLOAT
Provider name: sigma
Description: 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.
noise_sigma
Type: FLOAT
Provider name: noiseSigma
Description: 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.
noisy_sample_count
Type: INT32
Provider name: noisySampleCount
Description: 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.
step_count
Type: INT32
Provider name: stepCount
Description: 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.
gcp_display_name
Type: STRING
Provider name: displayName
Description: Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
metadata_artifact
Type: STRING
Provider name: metadataArtifact
Description: 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}.
metadata_schema_uri
Type: STRING
Provider name: metadataSchemaUri
Description: 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.
model_source_info
Type: STRUCT
Provider name: modelSourceInfo
Description: 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.
copy
Type: BOOLEAN
Provider name: copy
Description: If this Model is copy of another Model. If true then source_type pertains to the original.
source_type
Type: STRING
Provider name: sourceType
Description: Type of the model source.
Possible values:
MODEL_SOURCE_TYPE_UNSPECIFIED - Should not be used.
AUTOML - The Model is uploaded by automl training pipeline.
CUSTOM - The Model is uploaded by user or custom training pipeline.
BQML - The Model is registered and sync’ed from BigQuery ML.
MODEL_GARDEN - The Model is saved or tuned from Model Garden.
GENIE - The Model is saved or tuned from Genie.
CUSTOM_TEXT_EMBEDDING - The Model is uploaded by text embedding finetuning pipeline.
MARKETPLACE - The Model is saved or tuned from Marketplace.
name
Type: STRING
Provider name: name
Description: The resource name of the Model.
original_model_info
Type: STRUCT
Provider name: originalModelInfo
Description: Output only. If this Model is a copy of another Model, this contains info about the original.
model
Type: STRING
Provider name: model
Description: 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}
pipeline_job
Type: STRING
Provider name: pipelineJob
Description: Optional. This field is populated if the model is produced by a pipeline job.
predict_schemata
Type: STRUCT
Provider name: predictSchemata
Description: The schemata that describe formats of the Model’s predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
instance_schema_uri
Type: STRING
Provider name: instanceSchemaUri
Description: 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.
parameters_schema_uri
Type: STRING
Provider name: parametersSchemaUri
Description: 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.
prediction_schema_uri
Type: STRING
Provider name: predictionSchemaUri
Description: 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.
satisfies_pzi
Type: BOOLEAN
Provider name: satisfiesPzi
Description: Output only. Reserved for future use.
satisfies_pzs
Type: BOOLEAN
Provider name: satisfiesPzs
Description: Output only. Reserved for future use.
supported_deployment_resources_types
Type: UNORDERED_LIST_STRING
Provider name: supportedDeploymentResourcesTypes
Description: 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.
supported_export_formats
Type: UNORDERED_LIST_STRUCT
Provider name: supportedExportFormats
Description: Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
exportable_contents
Type: UNORDERED_LIST_STRING
Provider name: exportableContents
Description: Output only. The content of this Model that may be exported.
id
Type: STRING
Provider name: id
Description: 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.
supported_input_storage_formats
Type: UNORDERED_LIST_STRING
Provider name: supportedInputStorageFormats
Description: 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.
supported_output_storage_formats
Type: UNORDERED_LIST_STRING
Provider name: supportedOutputStorageFormats
Description: 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.
training_pipeline
Type: STRING
Provider name: trainingPipeline
Description: Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
update_time
Type: TIMESTAMP
Provider name: updateTime
Description: Output only. Timestamp when this Model was most recently updated.
version_aliases
Type: UNORDERED_LIST_STRING
Provider name: versionAliases
Description: 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{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.
version_create_time
Type: TIMESTAMP
Provider name: versionCreateTime
Description: Output only. Timestamp when this version was created.
version_description
Type: STRING
Provider name: versionDescription
Description: The description of this version.
version_id
Type: STRING
Provider name: versionId
Description: 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.
version_update_time
Type: TIMESTAMP
Provider name: versionUpdateTime
Description: Output only. Timestamp when this version was most recently updated.
name
Type: STRING
Provider name: name
Description: Output only. Resource name of the TrainingPipeline.
organization_id
Type: STRING
parent
Type: STRING
parent_model
Type: STRING
Provider name: parentModel
Description: Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
project_id
Type: STRING
project_number
Type: STRING
resource_name
Type: STRING
start_time
Type: TIMESTAMP
Provider name: startTime
Description: Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state
Type: STRING
Provider name: state
Description: Output only. The detailed state of the pipeline.
Possible values:
PIPELINE_STATE_UNSPECIFIED - The pipeline state is unspecified.
PIPELINE_STATE_QUEUED - The pipeline has been created or resumed, and processing has not yet begun.
PIPELINE_STATE_PENDING - The service is preparing to run the pipeline.
PIPELINE_STATE_RUNNING - The pipeline is in progress.
PIPELINE_STATE_SUCCEEDED - The pipeline completed successfully.
PIPELINE_STATE_FAILED - The pipeline failed.
PIPELINE_STATE_CANCELLING - The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
PIPELINE_STATE_CANCELLED - The pipeline has been cancelled.
PIPELINE_STATE_PAUSED - The pipeline has been stopped, and can be resumed.
Type: UNORDERED_LIST_STRING
training_task_definition
Type: STRING
Provider name: trainingTaskDefinition
Description: Required. A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
update_time
Type: TIMESTAMP
Provider name: updateTime
Description: Output only. Time when the TrainingPipeline was most recently updated.
