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ML Metadata proto module.
class Artifact: An artifact represents an input or an output of individual steps in a ML workflow, e.g., a trained model, an input dataset, and evaluation metrics.
class ArtifactType: A user defined type about a collection of artifacts and their properties that are stored in the metadata store.
class Association: An association represents the relationship between executions and contexts.
class Attribution: An attribution represents the relationship between artifacts and contexts.
class ConnectionConfig: A connection configuration specifying the persistent backend to be used with MLMD.
class Context: A context defines a group of artifacts and/or executions.
class ContextType: A user defined type about a collection of contexts and their properties that are stored in the metadata store.
class Event: An event records the relationship between artifacts and executions.
class Execution: An execution describes a component run or a step in an ML workflow along with its runtime parameters, e.g., a Trainer run, a data transformation step.
class ExecutionType: A user defined type about a collection of executions and their properties that are stored in the metadata store.
class FakeDatabaseConfig: An in-memory database configuration for testing purpose.
class MetadataStoreClientConfig: A connection configuration to use a MLMD server as the persistent backend.
class MySQLDatabaseConfig: A connection configuration to use a MySQL db instance as a MLMD backend.
class ParentContext: A parental context represents the relationship between contexts.
class SqliteMetadataSourceConfig: A connection configuration to use a Sqlite db file as a MLMD backend.