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ML Metadata

ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows. MLMD is an integral part of TensorFlow Extended (TFX), but is designed so that it can be used independently. As part of a broader platform like TFX, most users only interact with MLMD when examining the results of pipeline components, for example in notebooks or in TensorBoard.

The graph below shows the components that are part of MLMD. The storage backend is pluggable and can be extended. MLMD provides reference implementations for SQLite (which supports in-memory and disk) and MySQL out of the box. The MetadataStore provides APIs to record and retrieve metadata to and from the storage backend. MLMD can register metadata about the artifacts generated through the components/steps of the pipelines, metadata about the executions of these components/steps, and the associated lineage information. The concepts are explained in more detail below.

ML Metadata Overview

Functionality Enabled by MLMD

Tracking the inputs and outputs of all components/steps in an ML workflow and their lineage allows ML platforms to enable several important features. The following list provides a non-exhaustive overview of some of the major benefits.

  • List all Artifacts of a specific type, e.g. all Models that have been trained.
  • Load two Artifacts of the same type for comparison, e.g. to compare results from two experiments.
  • Show a DAG of all executions and their input and output artifacts, e.g. to visualize the workflow for debugging and discovery.
  • Recurse back through all events to see how an artifact was created, e.g. to see what data went into a model, or to enforce data retention plans.
  • Identify all artifacts that were created using a given artifact, e.g. to see all Models trained from a specific dataset, to mark models based upon bad data.
  • Determine if an execution has been run on the same inputs before, e.g. to determine whether a component/step has already completed the same work and the previous output can just be reused.
  • Etc.

Metadata Storage Backends and Store Connection Configuration

The MetadataStore object receives a connection configuration that corresponds to the storage backend used.

  • Fake Database provides an in-memory DB (using SQLite) for fast experimentation and local runs. Database is deleted when store object is destroyed.
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.fake_database.SetInParent() # Empty fake database proto
store = metadata_store.MetadataStore(connection_config)
  • SQLite reads and writes files from disk.
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = '...'
connection_config.sqlite.connection_mode = 3 # READWRITE_OPENCREATE
store = metadata_store.MetadataStore(connection_config)
  • MySQL connects to a MySQL server.
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.mysql.host = '...'
connection_config.mysql.port = '...'
connection_config.mysql.database = '...'
connection_config.mysql.user = '...'
connection_config.mysql.password = '...'
store = metadata_store.MetadataStore(connection_config)