The resource name of the Model.

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.

user provided version aliases so that a model version can be referenced via alias (i.e.projects/{project}/locations/{location}/models/{modelId}@{version_alias} instead of auto-generated version id (i.e.projects/{project}/locations/{location}/models/{modelId}@{versionId}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from versionId. 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.

Output only. timestamp when this version was created.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Output only. timestamp when this version was most recently updated.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

The description of the Model.

The description of this version.

The default checkpoint id of a model version.

The schemata that describe formats of the Model's predictions and explanations as given and returned viaPredictionService.Predict andPredictionService.Explain.

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 3.0.2Schema 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.

Immutable. An additional information about the Model; the schema of the metadata can be found inmetadataSchema. Unset if the Model does not have any additional information.

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

Optional. This field is populated if the model is produced by a pipeline job.

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested uponModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

Output only. When this Model is deployed, its prediction resources are described by theprediction_resources field of theEndpoint.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 anEndpoint and does not support online predictions (PredictionService.Predict orPredictionService.Explain). Such a Model can serve predictions by using aBatchPredictionJob, if it has at least one entry each insupportedInputStorageFormats andsupportedOutputStorageFormats.

Output only. The formats this Model supports inBatchPredictionJob.input_config. IfPredictSchemata.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. usesGcsSource.

• 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. usesGcsSource.

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. usesGcsSource.

• tf-record-gzip Similar totf-record, but the file is gzipped. usesGcsSource.

• bigquery Each instance is a single row in BigQuery. usesBigQuerySource.

• file-list Each line of the file is the location of an instance to process, usesgcsSource field of theInputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with aBatchPredictionJob. However, if it hassupportedDeploymentResourcesTypes, it could serve online predictions by usingPredictionService.Predict orPredictionService.Explain.

Output only. The formats this Model supports inBatchPredictionJob.output_config. If bothPredictSchemata.instance_schema_uri andPredictSchemata.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. usesGcsDestination.

• 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. usesGcsDestination.

• bigquery Each prediction is a single row in a BigQuery table, usesBigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with aBatchPredictionJob. However, if it hassupportedDeploymentResourcesTypes, it could serve online predictions by usingPredictionService.Predict orPredictionService.Explain.

Output only. timestamp when this Model was uploaded into Vertex AI.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Output only. timestamp when this Model was most recently updated.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to endpoints in different Locations.

The default explanation specification for this Model.

The Model can be used forrequesting explanation after beingdeployed if it is populated. The Model can be used forbatch explanation if it is populated.

All fields of the explanationSpec can be overridden byexplanationSpec ofDeployModelRequest.deployed_model, orexplanationSpec ofBatchPredictionJob.

If the default explanation specification is not set for this Model, this Model can still be used forrequesting explanation by settingexplanationSpec ofDeployModelRequest.deployed_model and forbatch explanation by settingexplanationSpec ofBatchPredictionJob.

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

The labels with user-defined metadata to organize your Models.

label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline withdata_input_config.

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.

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.

Output only. If this Model is a copy of another Model, this contains info about the original.

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern isprojects/{project}/locations/{location}/metadataStores/{metadataStore}/artifacts/{artifact}.

Optional. user input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

Output only. reserved for future use.

Output only. reserved for future use.

Optional. Output only. The checkpoints of the model.

Output only. The id of the export format. The possible format IDs are:

• tflite Used for Android mobile devices.

• edgetpu-tflite Used forEdge TPU devices.

• tf-saved-model A tensorflow model in SavedModel format.

• tf-js ATensorFlow.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.

Output only. The content of this Model that may be exported.

Immutable. A resource name of an Endpoint.

Immutable. An id of a DeployedModel in the above Endpoint.

Immutable. The id of the Checkpoint deployed in the DeployedModel.

Number of DataItems that were used for training this Model.

Number of DataItems that were used for validating this Model during training.

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.

Number of Annotations that are used for training this Model.

Number of Annotations that are used for validating this Model during training.

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.

type of the model source.

If this Model is copy of another Model. If true thensourceType pertains to the original.

Output only. The resource name of the Model this Model is a copy of, including the revision. Format:projects/{project}/locations/{location}/models/{modelId}@{versionId}

Required. The model garden source model resource name.

Optional. The model garden source model version id.

Optional. Whether to avoid pulling the model from the HF cache.

Required. The public base model URI.

The id of the checkpoint.

The epoch of the checkpoint.

The step of the checkpoint.