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gcp_aiplatform_batch_prediction_job

`ancestors`

Type: UNORDERED_LIST_STRING

`completion_stats`

Type: STRUCT
Provider name: completionStats
Description: Output only. Statistics on completed and failed prediction instances.

failed_count
Type: INT64
Provider name: failedCount
Description: Output only. The number of entities for which any error was encountered.
incomplete_count
Type: INT64
Provider name: incompleteCount
Description: Output only. In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successful_count
Type: INT64
Provider name: successfulCount
Description: Output only. The number of entities that had been processed successfully.
successful_forecast_point_count
Type: INT64
Provider name: successfulForecastPointCount
Description: Output only. The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.

`create_time`

Type: TIMESTAMP
Provider name: createTime
Description: Output only. Time when the BatchPredictionJob was created.

`dedicated_resources`

Type: STRUCT
Provider name: dedicatedResources
Description: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn’t support AUTOMATIC_RESOURCES, this config must be provided.

machine_spec
Type: STRUCT
Provider name: machineSpec
Description: Required. Immutable. The specification of a single machine.
- accelerator_count
  Type: INT32
  Provider name: acceleratorCount
  Description: The number of accelerators to attach to the machine.
- accelerator_type
  Type: STRING
  Provider name: acceleratorType
  Description: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
  Possible values:
  - ACCELERATOR_TYPE_UNSPECIFIED - Unspecified accelerator type, which means no accelerator.
  - NVIDIA_TESLA_K80 - Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol.
  - NVIDIA_TESLA_P100 - Nvidia Tesla P100 GPU.
  - NVIDIA_TESLA_V100 - Nvidia Tesla V100 GPU.
  - NVIDIA_TESLA_P4 - Nvidia Tesla P4 GPU.
  - NVIDIA_TESLA_T4 - Nvidia Tesla T4 GPU.
  - NVIDIA_TESLA_A100 - Nvidia Tesla A100 GPU.
  - NVIDIA_A100_80GB - Nvidia A100 80GB GPU.
  - NVIDIA_L4 - Nvidia L4 GPU.
  - NVIDIA_H100_80GB - Nvidia H100 80Gb GPU.
  - NVIDIA_H100_MEGA_80GB - Nvidia H100 Mega 80Gb GPU.
  - NVIDIA_H200_141GB - Nvidia H200 141Gb GPU.
  - TPU_V2 - TPU v2.
  - TPU_V3 - TPU v3.
  - TPU_V4_POD - TPU v4.
  - TPU_V5_LITEPOD - TPU v5.
- machine_type
  Type: STRING
  Provider name: machineType
  Description: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
- reservation_affinity
  Type: STRUCT
  Provider name: reservationAffinity
  Description: Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
  - key
    Type: STRING
    Provider name: key
    Description: Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
  - reservation_affinity_type
    Type: STRING
    Provider name: reservationAffinityType
    Description: Required. Specifies the reservation affinity type.
    Possible values:
    - TYPE_UNSPECIFIED - Default value. This should not be used.
    - NO_RESERVATION - Do not consume from any reserved capacity, only use on-demand.
    - ANY_RESERVATION - Consume any reservation available, falling back to on-demand.
    - SPECIFIC_RESERVATION - Consume from a specific reservation. When chosen, the reservation must be identified via the key and values fields.
  - values
    Type: UNORDERED_LIST_STRING
    Provider name: values
    Description: Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- tpu_topology
  Type: STRING
  Provider name: tpuTopology
  Description: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: “2x2x1”).
max_replica_count
Type: INT32
Provider name: maxReplicaCount
Description: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
starting_replica_count
Type: INT32
Provider name: startingReplicaCount
Description: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

`disable_container_logging`

Type: BOOLEAN
Provider name: disableContainerLogging
Description: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.

`encryption_spec`

Type: STRUCT
Provider name: encryptionSpec
Description: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption 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.

`end_time`

Type: TIMESTAMP
Provider name: endTime
Description: Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

`error`

Type: STRUCT
Provider name: error
Description: Output only. Only populated when the job’s state is JOB_STATE_FAILED or JOB_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.

`explanation_spec`

Type: STRUCT
Provider name: explanationSpec
Description: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.

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.
      - uris
        Type: UNORDERED_LIST_STRING
        Provider name: uris
        Description: 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.
  - 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 user-defined name of this BatchPredictionJob.

`generate_explanation`

Type: BOOLEAN
Provider name: generateExplanation
Description: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.

`input_config`

Type: STRUCT
Provider name: inputConfig
Description: Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model’s PredictSchemata’s instance_schema_uri.

bigquery_source
Type: STRUCT
Provider name: bigquerySource
Description: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
- input_uri
  Type: STRING
  Provider name: inputUri
  Description: Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
gcs_source
Type: STRUCT
Provider name: gcsSource
Description: The Cloud Storage location for the input instances.
- uris
  Type: UNORDERED_LIST_STRING
  Provider name: uris
  Description: 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.
instances_format
Type: STRING
Provider name: instancesFormat
Description: Required. The format in which instances are given, must be one of the Model’s supported_input_storage_formats.

`instance_config`

Type: STRUCT
Provider name: instanceConfig
Description: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

excluded_fields
Type: UNORDERED_LIST_STRING
Provider name: excludedFields
Description: Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.
included_fields
Type: UNORDERED_LIST_STRING
Provider name: includedFields
Description: Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.
instance_type
Type: STRING
Provider name: instanceType
Description: The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
key_field
Type: STRING
Provider name: keyField
Description: The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

`labels`

Type: UNORDERED_LIST_STRING

`manual_batch_tuning_parameters`

Type: STRUCT
Provider name: manualBatchTuningParameters
Description: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).

batch_size
Type: INT32
Provider name: batchSize
Description: Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation’s execution, but too high value will result in a whole batch not fitting in a machine’s memory, and the whole operation will fail. The default value is 64.

`model`

Type: STRING
Provider name: model
Description: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}

`model_version_id`

Type: STRING
Provider name: modelVersionId
Description: Output only. The version ID of the Model that produces the predictions via this job.

`name`

Type: STRING
Provider name: name
Description: Output only. Resource name of the BatchPredictionJob.

`organization_id`

Type: STRING

`output_config`

Type: STRUCT
Provider name: outputConfig
Description: Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model’s PredictSchemata’s instance_schema_uri and prediction_schema_uri.

bigquery_destination
Type: STRUCT
Provider name: bigqueryDestination
Description: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model’s instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single “errors” column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
- 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.
gcs_destination
Type: STRUCT
Provider name: gcsDestination
Description: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., …, predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,…, errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
- 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.
predictions_format
Type: STRING
Provider name: predictionsFormat
Description: Required. The format in which Vertex AI gives the predictions, must be one of the Model’s supported_output_storage_formats.

`output_info`

Type: STRUCT
Provider name: outputInfo
Description: Output only. Information further describing the output of this job.

bigquery_output_dataset
Type: STRING
Provider name: bigqueryOutputDataset
Description: Output only. The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigquery_output_table
Type: STRING
Provider name: bigqueryOutputTable
Description: Output only. The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcs_output_directory
Type: STRING
Provider name: gcsOutputDirectory
Description: Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.

`parent`

Type: STRING

`partial_failures`

Type: UNORDERED_LIST_STRUCT
Provider name: partialFailures
Description: Output only. Partial failures encountered. For example, single files that can’t be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.

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.

`project_id`

Type: STRING

`project_number`

Type: STRING

`resource_name`

Type: STRING

`resources_consumed`

Type: STRUCT
Provider name: resourcesConsumed
Description: Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.

replica_hours
Type: DOUBLE
Provider name: replicaHours
Description: Output only. The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

`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.

`service_account`

Type: STRING
Provider name: serviceAccount
Description: The service account that the DeployedModel’s container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.

`start_time`

Type: TIMESTAMP
Provider name: startTime
Description: Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.

`state`

Type: STRING
Provider name: state
Description: Output only. The detailed state of the job.
Possible values:

JOB_STATE_UNSPECIFIED - The job state is unspecified.
JOB_STATE_QUEUED - The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING - The service is preparing to run the job.
JOB_STATE_RUNNING - The job is in progress.
JOB_STATE_SUCCEEDED - The job completed successfully.
JOB_STATE_FAILED - The job failed.
JOB_STATE_CANCELLING - The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED - The job has been cancelled.
JOB_STATE_PAUSED - The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED - The job has expired.
JOB_STATE_UPDATING - The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED - The job is partially succeeded, some results may be missing due to errors.

`tags`

Type: UNORDERED_LIST_STRING

`unmanaged_container_model`

Type: STRUCT
Provider name: unmanagedContainerModel
Description: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.

artifact_uri
Type: STRING
Provider name: artifactUri
Description: The path to the directory containing the Model artifact and any of its supporting files.
container_spec
Type: STRUCT
Provider name: containerSpec
Description: Input only. The specification of the container that is to be used when deploying this Model.
- 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’.
predict_schemata
Type: STRUCT
Provider name: predictSchemata
Description: Contains the schemata used in Model’s predictions and explanations
- 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.

`update_time`

Type: TIMESTAMP
Provider name: updateTime
Description: Output only. Time when the BatchPredictionJob was most recently updated.