// Copyright 2022 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

syntax = "proto3";

package google.cloud.aiplatform.v1;

import "google/api/field_behavior.proto";
import "google/api/resource.proto";
import "google/cloud/aiplatform/v1/deployed_model_ref.proto";
import "google/cloud/aiplatform/v1/encryption_spec.proto";
import "google/cloud/aiplatform/v1/env_var.proto";
import "google/cloud/aiplatform/v1/explanation.proto";
import "google/protobuf/struct.proto";
import "google/protobuf/timestamp.proto";

option csharp_namespace = "Google.Cloud.AIPlatform.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/aiplatform/v1;aiplatform";
option java_multiple_files = true;
option java_outer_classname = "ModelProto";
option java_package = "com.google.cloud.aiplatform.v1";
option php_namespace = "Google\\Cloud\\AIPlatform\\V1";
option ruby_package = "Google::Cloud::AIPlatform::V1";

// A trained machine learning Model.
message Model {
  option (google.api.resource) = {
    type: "aiplatform.googleapis.com/Model"
    pattern: "projects/{project}/locations/{location}/models/{model}"
  };

  // Represents export format supported by the Model.
  // All formats export to Google Cloud Storage.
  message ExportFormat {
    // The Model content that can be exported.
    enum ExportableContent {
      // Should not be used.
      EXPORTABLE_CONTENT_UNSPECIFIED = 0;

      // Model artifact and any of its supported files. Will be exported to the
      // location specified by the `artifactDestination` field of the
      // [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.
      ARTIFACT = 1;

      // The container image that is to be used when deploying this Model. Will
      // be exported to the location specified by the `imageDestination` field
      // of the [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.
      IMAGE = 2;
    }

    // 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](https://cloud.google.com/edge-tpu/) devices.
    //
    // * `tf-saved-model`
    // A tensorflow model in SavedModel format.
    //
    // * `tf-js`
    // A [TensorFlow.js](https://www.tensorflow.org/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.
    string id = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

    // Output only. The content of this Model that may be exported.
    repeated ExportableContent exportable_contents = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
  }

  // Identifies a type of Model's prediction resources.
  enum DeploymentResourcesType {
    // Should not be used.
    DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED = 0;

    // Resources that are dedicated to the [DeployedModel][google.cloud.aiplatform.v1.DeployedModel], and that need a
    // higher degree of manual configuration.
    DEDICATED_RESOURCES = 1;

    // Resources that to large degree are decided by Vertex AI, and require
    // only a modest additional configuration.
    AUTOMATIC_RESOURCES = 2;

    // Resources that can be shared by multiple [DeployedModels][google.cloud.aiplatform.v1.DeployedModel].
    // A pre-configured [DeploymentResourcePool][] is required.
    SHARED_RESOURCES = 3;
  }

  // The resource name of the Model.
  string name = 1;

  // 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.
  string version_id = 28 [
    (google.api.field_behavior) = IMMUTABLE,
    (google.api.field_behavior) = OUTPUT_ONLY
  ];

  // User provided version aliases so that a model version can be referenced via
  // alias (i.e.
  // projects/{project}/locations/{location}/models/{model_id}@{version_alias}
  // instead of auto-generated version id (i.e.
  // projects/{project}/locations/{location}/models/{model_id}@{version_id}).
  // The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
  // version_id. A default version alias will be created for the first version
  // of the model, and there must be exactly one default version alias for a
  // model.
  repeated string version_aliases = 29;

  // Output only. Timestamp when this version was created.
  google.protobuf.Timestamp version_create_time = 31 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Timestamp when this version was most recently updated.
  google.protobuf.Timestamp version_update_time = 32 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Required. The display name of the Model.
  // The name can be up to 128 characters long and can be consist of any UTF-8
  // characters.
  string display_name = 2 [(google.api.field_behavior) = REQUIRED];

  // The description of the Model.
  string description = 3;

  // The description of this version.
  string version_description = 30;

  // The schemata that describe formats of the Model's predictions and
  // explanations as given and returned via
  // [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] and [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
  PredictSchemata predict_schemata = 4;

  // 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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
  // 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.
  string metadata_schema_uri = 5 [(google.api.field_behavior) = IMMUTABLE];

  // Immutable. An additional information about the Model; the schema of the metadata can
  // be found in [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
  // Unset if the Model does not have any additional information.
  google.protobuf.Value metadata = 6 [(google.api.field_behavior) = IMMUTABLE];

  // Output only. The formats in which this Model may be exported. If empty, this Model is
  // not available for export.
  repeated ExportFormat supported_export_formats = 20 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. The resource name of the TrainingPipeline that uploaded this Model, if
  // any.
  string training_pipeline = 7 [
    (google.api.field_behavior) = OUTPUT_ONLY,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/TrainingPipeline"
    }
  ];

  // Input only. The specification of the container that is to be used when deploying
  // this Model. The specification is ingested upon
  // [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel], and all binaries it contains are copied
  // and stored internally by Vertex AI.
  // Not present for AutoML Models.
  ModelContainerSpec container_spec = 9 [(google.api.field_behavior) = INPUT_ONLY];

  // Immutable. The path to the directory containing the Model artifact and any of its
  // supporting files.
  // Not present for AutoML Models.
  string artifact_uri = 26 [(google.api.field_behavior) = IMMUTABLE];

  // Output only. When this Model is deployed, its prediction resources are described by the
  // `prediction_resources` field of the [Endpoint.deployed_models][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support
  // online predictions ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
  // [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]). Such a Model can serve predictions by
  // using a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it has at least one entry each in
  // [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats] and
  // [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
  repeated DeploymentResourcesType supported_deployment_resources_types = 10 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. The formats this Model supports in
  // [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If
  // [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
  //
  // * `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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
  //
  // * `tf-record`
  // The TFRecord format, where each instance is a single record in tfrecord
  // syntax. Uses [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
  //
  // * `tf-record-gzip`
  // Similar to `tf-record`, but the file is gzipped. Uses
  // [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
  //
  // * `bigquery`
  // Each instance is a single row in BigQuery. Uses
  // [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
  //
  // * `file-list`
  // Each line of the file is the location of an instance to process, uses
  // `gcs_source` field of the
  // [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig] object.
  //
  //
  // If this Model doesn't support any of these formats it means it cannot be
  // used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has
  // [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online
  // predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
  // [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
  repeated string supported_input_storage_formats = 11 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. The formats this Model supports in
  // [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config]. If both
  // [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and
  // [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
  //
  // * `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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
  //
  // * `bigquery`
  // Each prediction is a single row in a BigQuery table, uses
  // [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
  // .
  //
  //
  // If this Model doesn't support any of these formats it means it cannot be
  // used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has
  // [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online
  // predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
  // [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
  repeated string supported_output_storage_formats = 12 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Timestamp when this Model was uploaded into Vertex AI.
  google.protobuf.Timestamp create_time = 13 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Timestamp when this Model was most recently updated.
  google.protobuf.Timestamp update_time = 14 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. The pointers to DeployedModels created from this Model. Note that
  // Model could have been deployed to Endpoints in different Locations.
  repeated DeployedModelRef deployed_models = 15 [(google.api.field_behavior) = OUTPUT_ONLY];

  // The default explanation specification for this Model.
  //
  // The Model can be used for [requesting
  // explanation][PredictionService.Explain] after being
  // [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is populated.
  // The Model can be used for [batch
  // explanation][BatchPredictionJob.generate_explanation] if it is populated.
  //
  // All fields of the explanation_spec can be overridden by
  // [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of
  // [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model], or
  // [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of
  // [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
  //
  // If the default explanation specification is not set for this Model, this
  // Model can still be used for [requesting
  // explanation][PredictionService.Explain] by setting
  // [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of
  // [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model] and for [batch
  // explanation][BatchPredictionJob.generate_explanation] by setting
  // [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of
  // [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
  ExplanationSpec explanation_spec = 23;

  // Used to perform consistent read-modify-write updates. If not set, a blind
  // "overwrite" update happens.
  string etag = 16;

  // The labels with user-defined metadata to organize your Models.
  //
  // Label keys and values can be no longer than 64 characters
  // (Unicode codepoints), can only contain lowercase letters, numeric
  // characters, underscores and dashes. International characters are allowed.
  //
  // See https://goo.gl/xmQnxf for more information and examples of labels.
  map<string, string> labels = 17;

  // 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.
  EncryptionSpec encryption_spec = 24;

  // Output only. Source of a model. It can either be automl training pipeline, custom
  // training pipeline, BigQuery ML, or existing Vertex AI Model.
  ModelSourceInfo model_source_info = 38 [(google.api.field_behavior) = OUTPUT_ONLY];
}

// Contains the schemata used in Model's predictions and explanations via
// [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict], [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain] and
// [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
message PredictSchemata {
  // Immutable. Points to a YAML file stored on Google Cloud Storage describing the format
  // of a single instance, which are used in [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances],
  // [ExplainRequest.instances][google.cloud.aiplatform.v1.ExplainRequest.instances] and
  // [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
  // The schema is defined as an OpenAPI 3.0.2 [Schema
  // Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
  // 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.
  string instance_schema_uri = 1 [(google.api.field_behavior) = IMMUTABLE];

  // Immutable. Points to a YAML file stored on Google Cloud Storage describing the
  // parameters of prediction and explanation via
  // [PredictRequest.parameters][google.cloud.aiplatform.v1.PredictRequest.parameters], [ExplainRequest.parameters][google.cloud.aiplatform.v1.ExplainRequest.parameters] and
  // [BatchPredictionJob.model_parameters][google.cloud.aiplatform.v1.BatchPredictionJob.model_parameters].
  // The schema is defined as an OpenAPI 3.0.2 [Schema
  // Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
  // 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.
  string parameters_schema_uri = 2 [(google.api.field_behavior) = IMMUTABLE];

  // 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][google.cloud.aiplatform.v1.PredictResponse.predictions], [ExplainResponse.explanations][google.cloud.aiplatform.v1.ExplainResponse.explanations], and
  // [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
  // The schema is defined as an OpenAPI 3.0.2 [Schema
  // Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
  // 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.
  string prediction_schema_uri = 3 [(google.api.field_behavior) = IMMUTABLE];
}

// Specification of a container for serving predictions. Some fields in this
// message correspond to fields in the [Kubernetes Container v1 core
// specification](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
message ModelContainerSpec {
  // 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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#publishing),
  // including permissions requirements for the Vertex AI Service Agent.
  //
  // The container image is ingested upon [ModelService.UploadModel][google.cloud.aiplatform.v1.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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#).
  //
  // You can use the URI to one of Vertex AI's [pre-built container images for
  // prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers)
  // in this field.
  string image_uri = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.field_behavior) = IMMUTABLE
  ];

  // Immutable. Specifies the command that runs when the container starts. This overrides
  // the container's
  // [ENTRYPOINT](https://docs.docker.com/engine/reference/builder/#entrypoint).
  // Specify this field as an array of executable and arguments, similar to a
  // Docker `ENTRYPOINT`'s "exec" form, not its "shell" form.
  //
  // If you do not specify this field, then the container's `ENTRYPOINT` runs,
  // in conjunction with the [args][google.cloud.aiplatform.v1.ModelContainerSpec.args] field or the
  // container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd),
  // if either exists. If this field is not specified and the container does not
  // have an `ENTRYPOINT`, then refer to the Docker documentation about [how
  // `CMD` and `ENTRYPOINT`
  // interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact).
  //
  // If you specify this field, then you can also specify the `args` field to
  // provide additional arguments for this command. However, if you specify this
  // field, then the container's `CMD` is ignored. See the
  // [Kubernetes documentation about how the
  // `command` and `args` fields interact with a container's `ENTRYPOINT` and
  // `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes).
  //
  // In this field, you can reference [environment variables set by Vertex
  // AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables)
  // and environment variables set in the [env][google.cloud.aiplatform.v1.ModelContainerSpec.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:
  // <code>$(<var>VARIABLE_NAME</var>)</code>
  // 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 `$$`; for example:
  // <code>$$(<var>VARIABLE_NAME</var>)</code>
  // This field corresponds to the `command` field of the Kubernetes Containers
  // [v1 core
  // API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  repeated string command = 2 [(google.api.field_behavior) = IMMUTABLE];

  // Immutable. Specifies arguments for the command that runs when the container starts.
  // This overrides the container's
  // [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify
  // this field as an array of executable and arguments, similar to a Docker
  // `CMD`'s "default parameters" form.
  //
  // If you don't specify this field but do specify the
  // [command][google.cloud.aiplatform.v1.ModelContainerSpec.command] field, then the command from the
  // `command` field runs without any additional arguments. See the
  // [Kubernetes documentation about how the
  // `command` and `args` fields interact with a container's `ENTRYPOINT` and
  // `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes).
  //
  // If you don't specify this field and don't specify the `command` field,
  // then the container's
  // [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and
  // `CMD` determine what runs based on their default behavior. See the Docker
  // documentation about [how `CMD` and `ENTRYPOINT`
  // interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact).
  //
  // In this field, you can reference [environment variables
  // set by Vertex
  // AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables)
  // and environment variables set in the [env][google.cloud.aiplatform.v1.ModelContainerSpec.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:
  // <code>$(<var>VARIABLE_NAME</var>)</code>
  // 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 `$$`; for example:
  // <code>$$(<var>VARIABLE_NAME</var>)</code>
  // This field corresponds to the `args` field of the Kubernetes Containers
  // [v1 core
  // API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  repeated string args = 3 [(google.api.field_behavior) = IMMUTABLE];

  // 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][google.cloud.aiplatform.v1.ModelContainerSpec.command] and
  // [args][google.cloud.aiplatform.v1.ModelContainerSpec.args] fields can reference these variables. Later
  // entries in this list can also reference earlier entries. For example, the
  // following example sets the variable `VAR_2` to have the value `foo bar`:
  //
  // ```json
  // [
  //   {
  //     "name": "VAR_1",
  //     "value": "foo"
  //   },
  //   {
  //     "name": "VAR_2",
  //     "value": "$(VAR_1) bar"
  //   }
  // ]
  // ```
  //
  // If you switch the order of the variables in the example, then the expansion
  // does not occur.
  //
  // This field corresponds to the `env` field of the Kubernetes Containers
  // [v1 core
  // API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  repeated EnvVar env = 4 [(google.api.field_behavior) = IMMUTABLE];

  // 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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#liveness)
  // 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](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  repeated Port ports = 5 [(google.api.field_behavior) = IMMUTABLE];

  // Immutable. HTTP path on the container to send prediction requests to. Vertex AI
  // forwards requests sent using
  // [projects.locations.endpoints.predict][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.ModelContainerSpec.ports] field.
  //
  // If you don't specify this field, it defaults to the following value when
  // you [deploy this Model to an Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]:
  // <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code>
  // The placeholders in this value are replaced as follows:
  //
  // * <var>ENDPOINT</var>: 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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
  //
  // * <var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the `DeployedModel`.
  //   (Vertex AI makes this value available to your container code
  //   as the [`AIP_DEPLOYED_MODEL_ID` environment
  //   variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
  string predict_route = 6 [(google.api.field_behavior) = IMMUTABLE];

  // 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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#health).
  //
  // 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][google.cloud.aiplatform.v1.ModelContainerSpec.ports] field.
  //
  // If you don't specify this field, it defaults to the following value when
  // you [deploy this Model to an Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]:
  // <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code>
  // The placeholders in this value are replaced as follows:
  //
  // * <var>ENDPOINT</var>: 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](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
  //
  // * <var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the `DeployedModel`.
  //   (Vertex AI makes this value available to your container code as the
  //   [`AIP_DEPLOYED_MODEL_ID` environment
  //   variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
  string health_route = 7 [(google.api.field_behavior) = IMMUTABLE];
}

// Represents a network port in a container.
message Port {
  // The number of the port to expose on the pod's IP address.
  // Must be a valid port number, between 1 and 65535 inclusive.
  int32 container_port = 3;
}

// Detail description of the source information of the model.
message ModelSourceInfo {
  // Source of the model.
  enum ModelSourceType {
    // Should not be used.
    MODEL_SOURCE_TYPE_UNSPECIFIED = 0;

    // The Model is uploaded by automl training pipeline.
    AUTOML = 1;

    // The Model is uploaded by user or custom training pipeline.
    CUSTOM = 2;

    // The Model is registered and sync'ed from BigQuery ML.
    BQML = 3;
  }

  // Type of the model source.
  ModelSourceType source_type = 1;
}