Package viam.app.v1

Interface AppOuterClass.MLTrainingMetadataOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder
All Known Implementing Classes:
AppOuterClass.MLTrainingMetadata, AppOuterClass.MLTrainingMetadata.Builder
Enclosing class:
AppOuterClass
public static interface AppOuterClass.MLTrainingMetadataOrBuilder extends com.google.protobuf.MessageLiteOrBuilder

  • Method Details

    • getVersionsList

      List<AppOuterClass.MLTrainingVersion> getVersionsList()
       A list of package versions for ML training source distribution
      repeated .viam.app.v1.MLTrainingVersion versions = 5 [json_name = "versions"];

    • getVersions

      AppOuterClass.MLTrainingVersion getVersions(int index)
       A list of package versions for ML training source distribution
      repeated .viam.app.v1.MLTrainingVersion versions = 5 [json_name = "versions"];

    • getVersionsCount

      int getVersionsCount()
       A list of package versions for ML training source distribution
      repeated .viam.app.v1.MLTrainingVersion versions = 5 [json_name = "versions"];

    • getModelTypeValue

      int getModelTypeValue()
      .viam.app.mltraining.v1.ModelType model_type = 2 [json_name = "modelType"];
      Returns:
      The enum numeric value on the wire for modelType.

    • getModelType

      MlTraining.ModelType getModelType()
      .viam.app.mltraining.v1.ModelType model_type = 2 [json_name = "modelType"];
      Returns:
      The modelType.

    • getModelFrameworkValue

      int getModelFrameworkValue()
      .viam.app.mltraining.v1.ModelFramework model_framework = 3 [json_name = "modelFramework"];
      Returns:
      The enum numeric value on the wire for modelFramework.

    • getModelFramework

      MlTraining.ModelFramework getModelFramework()
      .viam.app.mltraining.v1.ModelFramework model_framework = 3 [json_name = "modelFramework"];
      Returns:
      The modelFramework.

    • getDraft

      boolean getDraft()
      bool draft = 4 [json_name = "draft"];
      Returns:
      The draft.

    • getDeploymentModularResourcesList

      List<String> getDeploymentModularResourcesList()
       Colon-delimited triplets of components or services from the registry used when
 model_framework is MODEL_FRAMEWORK_REGISTRY. This ties the ML model file to the
 associated component or service that consumes the files and should be deployed
 along with the model.
 Copied onto packages published by this training script.
      repeated string deployment_modular_resources = 6 [json_name = "deploymentModularResources"];
      Returns:
      A list containing the deploymentModularResources.

    • getDeploymentModularResourcesCount

      int getDeploymentModularResourcesCount()
       Colon-delimited triplets of components or services from the registry used when
 model_framework is MODEL_FRAMEWORK_REGISTRY. This ties the ML model file to the
 associated component or service that consumes the files and should be deployed
 along with the model.
 Copied onto packages published by this training script.
      repeated string deployment_modular_resources = 6 [json_name = "deploymentModularResources"];
      Returns:
      The count of deploymentModularResources.

    • getDeploymentModularResources

      String getDeploymentModularResources(int index)
       Colon-delimited triplets of components or services from the registry used when
 model_framework is MODEL_FRAMEWORK_REGISTRY. This ties the ML model file to the
 associated component or service that consumes the files and should be deployed
 along with the model.
 Copied onto packages published by this training script.
      repeated string deployment_modular_resources = 6 [json_name = "deploymentModularResources"];
      Parameters:
      index - The index of the element to return.
      Returns:
      The deploymentModularResources at the given index.

    • getDeploymentModularResourcesBytes

      com.google.protobuf.ByteString getDeploymentModularResourcesBytes(int index)
       Colon-delimited triplets of components or services from the registry used when
 model_framework is MODEL_FRAMEWORK_REGISTRY. This ties the ML model file to the
 associated component or service that consumes the files and should be deployed
 along with the model.
 Copied onto packages published by this training script.
      repeated string deployment_modular_resources = 6 [json_name = "deploymentModularResources"];
      Parameters:
      index - The index of the element to return.
      Returns:
      The deploymentModularResources at the given index.