A library for maintaining metadata for artifacts.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for tfx_oss from gravatar.com tfx_oss

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License (Apache 2.0)
  • Author: Google LLC
  • Tags machine, learning, metadata, tfx
  • Requires: Python >=3.7, <4
Classifiers

Project description

ML Metadata

Python PyPI

ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.

NOTE: ML Metadata may be backwards incompatible before version 1.0.

Getting Started

For more background on MLMD and instructions on using it, see the getting started guide

Installing from PyPI

The recommended way to install ML Metadata is to use the PyPI package:

pip install ml-metadata

Then import the relevant packages:

from ml_metadata import metadata_store
from ml_metadata.proto import metadata_store_pb2

Nightly Packages

ML Metadata (MLMD) also hosts nightly packages at https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest nightly package, please use the following command:

pip install -i https://pypi-nightly.tensorflow.org/simple ml-metadata

Installing with Docker

This is the recommended way to build ML Metadata under Linux, and is continuously tested at Google.

Please first install docker and docker-compose by following the directions: docker; docker-compose.

Then, run the following at the project root:

DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}

where PY_VERSION is one of {37, 38, 39}.

A wheel will be produced under dist/, and installed as follows:

pip install dist/*.whl

Installing from source

1. Prerequisites

To compile and use ML Metadata, you need to set up some prerequisites.

Install Bazel

If Bazel is not installed on your system, install it now by following these directions.

Install cmake

If cmake is not installed on your system, install it now by following these directions.

2. Clone ML Metadata repository

git clone https://github.com/google/ml-metadata
cd ml-metadata

Note that these instructions will install the latest master branch of ML Metadata. If you want to install a specific branch (such as a release branch), pass -b <branchname> to the git clone command.

3. Build the pip package

ML Metadata uses Bazel to build the pip package from source:

python setup.py bdist_wheel

You can find the generated .whl file in the dist subdirectory.

4. Install the pip package

pip install dist/*.whl

5.(Optional) Build the grpc server

ML Metadata uses Bazel to build the c++ binary from source:

bazel build -c opt --define grpc_no_ares=true  //ml_metadata/metadata_store:metadata_store_server

Supported platforms

MLMD is built and tested on the following 64-bit operating systems:

  • macOS 10.14.6 (Mojave) or later.
  • Ubuntu 16.04 or later.
  • Windows 7 or later.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for tfx_oss from gravatar.com tfx_oss

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License (Apache 2.0)
  • Author: Google LLC
  • Tags machine, learning, metadata, tfx
  • Requires: Python >=3.7, <4
Classifiers

Release history Release notifications | RSS feed

1.16.0

1.15.0

1.14.0

1.13.1

1.13.0

1.12.0

1.11.0

1.10.0

1.9.0

This version

1.8.0

1.7.0

1.6.0

1.5.0

1.4.0

1.3.0

1.2.0

1.1.0

1.0.0

0.30.0

0.29.0

0.28.0

0.28.0.dev20210126 pre-release

0.27.0

0.26.0

0.25.1

0.25.0

0.24.0

0.23.0

0.22.1

0.22.0

0.21.2

0.21.1

0.21.0

0.21.0rc0 pre-release

0.15.2

0.15.1

0.15.0

0.15.0rc0 pre-release

0.14.0

0.13.2

0.13.1.dev0 pre-release

0.13.0.dev0 pre-release

0.12.0.dev0 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

ml_metadata-1.8.0-cp39-cp39-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

ml_metadata-1.8.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64

ml_metadata-1.8.0-cp39-cp39-macosx_10_14_x86_64.whl (18.9 MB view details)

Uploaded CPython 3.9 macOS 10.14+ x86-64

ml_metadata-1.8.0-cp38-cp38-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

ml_metadata-1.8.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

ml_metadata-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl (18.9 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

ml_metadata-1.8.0-cp37-cp37m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.7m Windows x86-64

ml_metadata-1.8.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

ml_metadata-1.8.0-cp37-cp37m-macosx_10_9_x86_64.whl (18.9 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file ml_metadata-1.8.0-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 9f149a290be6c69640d01f18547cc8fe23d2862ba7aa72258401f844ecd07cf2
MD5 b257007e4a5067348b4122f9ad49d481
BLAKE2b-256 a3c24a796611667b33dad7ea6e23824f99c97d5483684c71cc470c0a4928f47d

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ed60db0580f2d9949bbc8daa89281a4229d5bb1d6e2803148ed6cf60633a63eb
MD5 c9d2e47510c091061f81e9b34f62d7c9
BLAKE2b-256 1c63aab660660b3d660f9bf49815d63ba84ad32b3f03824695c1f61c2029ffeb

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 52e0e2dcabf861b0d892f9bed62ceec932309d08cf72026e3c15538165fe19c5
MD5 0c08f6d785dbc34202ef6b22c0e8b579
BLAKE2b-256 b0ed806b3349a5115b3d312250571791fefe17f5b0d7a7ff036bbda260cd50ab

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 fc611fbc09b955526b3b8a52d264467c687730ea2dc03a1209bb13858e166708
MD5 805cbe3792bd0bc8ce5dbe4aa1d17b03
BLAKE2b-256 21e74878329d7caaf2730cff797a04ae49f9f7a3ad840c91141d180a4a08591b

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 337ed17bd503a6f05a1aee9ed712dd08186305acb4339d465afbf2ffb488a7e4
MD5 3d3babcfe0ff19e01856ff67ec152b99
BLAKE2b-256 6ac09b0430e5077276ec97c67019aed00df21c65d144c35aa0e987dbd586cc4c

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8cc92d7ed1cfc35f050a968050f128efb1595b43d8954900434c887aa156347b
MD5 f839c8fb4f34fb361b155e2d5994f0a2
BLAKE2b-256 7e2ebe696b421da387588c1ea0ff502ed699e130aab9107f8cd59447937e42f4

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 1efbbf63a61e373ec5b14ed83badd3623fd9f4e96f915afa6887280252fe0b18
MD5 453b1e3976793d92f737a9ce0b48d91f
BLAKE2b-256 48dd7f3be8143d55c21409cd22c1e3e8705fe82e01c1bd3edc72e4f09851411b

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 3bc846b10a56eef8d3538559a5df772028926de887ccfd209524021df74d1348
MD5 f6c97b240a3a7b2d4d4844597e7313af
BLAKE2b-256 43f348a3efc56df24fd42857309714b4940663b67669c98835e3f4d5dc69af8a

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-1.8.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ml_metadata-1.8.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 53d20f50e2beb3749c6b294ed4d4a74af25809e8c18857676e656377f16fb544
MD5 17ccdbcff5b598ece494b9f55f8fec63
BLAKE2b-256 9591bab7c6664fb57b30b531f5b12c5d674655f92b3360fb45b06a41ca6c8fec

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page