tensorflow-metadata
Library and standards for schema and statistics.
Decision gist · record as of 2026-08-14
Yes, if you are building machine learning pipelines with TensorFlow or TFX and need standardized metadata representations for schemas and statistics. The package is actively maintained, has no known vulnerabilities, low installation friction, and a permissive license. It is most valuable in team environments or complex pipelines where metadata standardization aids reproducibility and data governance; less critical for simple single-script experiments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (3.10, 3.11, 3.12, or 3.13)
- Low friction installation with three lightweight runtime dependencies (absl-py, protobuf, googleapis-common-protos).
- Active maintenance with a recent release 66 days ago and ongoing repository activity.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows use in commercial and open-source projects with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-06-09 (66 days) · last repo commit 2026-08-14 · 110 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,087,649 downloads/mo, #2,760 on PyPI
Alternatives
Verify before relying
pip install tensorflow-metadata
import tensorflow_metadata as tfmd
from tensorflow_metadata.proto import schema_pb2
schema = schema_pb2.Schema()- Whether the package works with TensorFlow versions other than those in its own dependency tree
- Whether summary statistics generation supports all common data types and distributions
- Whether schema validation performance scales to very large datasets
What it is and what it does
TensorFlow Metadata is a library for defining and working with machine learning metadata in a standardized way. It provides protocol buffer-based representations for three key artifacts: schemas that describe the structure of tabular data (such as TensorFlow Examples), summary statistics computed over datasets, and problem statements that quantify model objectives. These representations can be created manually or generated automatically during data analysis workflows.
The library is designed to integrate with TensorFlow and TFX (TensorFlow Extended) pipelines, where metadata is consumed for data validation (checking that incoming data matches expected schemas), exploration (understanding dataset characteristics), and transformation (preparing data for model training). It sits at the intersection of data engineering and machine learning, providing a common language for describing data properties that both automated systems and human analysts need to understand.
Use it for
- Define and validate schemas for tabular datasets before feeding them into TensorFlow training pipelines
- Generate and store summary statistics about training datasets for data drift detection and monitoring
- Document data requirements and constraints as part of a TFX workflow for reproducibility
- Automate data exploration by programmatically inspecting schema and statistics representations
- Share standardized metadata definitions across teams working on the same machine learning projects
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building machine learning pipelines with TensorFlow or TFX and need standardized metadata representations for schemas and statistics.
The package is actively maintained, has no known vulnerabilities, low installation friction, and a permissive license. It is most valuable in team environments or complex pipelines where metadata standardization aids reproducibility and data governance; less critical for simple single-script experiments.
Install
tensorflow-metadata on PyPI
Before you install
Low friction installation with three lightweight runtime dependencies (absl-py, protobuf, googleapis-common-protos). Active maintenance with a recent release 66 days ago and ongoing repository activity.
Requires Python 3.10 or later (3.10, 3.11, 3.12, or 3.13)
License in practice
Apache 2.0 permissive license allows use in commercial and open-source projects with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install tensorflow-metadata
import tensorflow_metadata as tfmd
from tensorflow_metadata.proto import schema_pb2
schema = schema_pb2.Schema()Verify before relying
- Whether the package works with TensorFlow versions other than those in its own dependency tree
- Whether summary statistics generation supports all common data types and distributions
- Whether schema validation performance scales to very large datasets
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesabsl-pyprotobufgoogleapis-common-protos |
| Maintenance | Actively maintained 66 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,087,649 / month, #2,760 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflow_metadata-1.21.0-py3-none-any.whl
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See also tensorflow-data-validation · tensorflow-datasets · tensorflow-transform · tfds-nightly · tfx-bsl · tensorflow-addons · tf-keras · facets-overview · parquet-metadata · tf-keras-nightly