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 >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <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

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 {27, 35, 36, 37}.

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:

bazel run -c opt --define grpc_no_ares=true ml_metadata:build_pip_package

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 10.12.6 (Sierra) 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 >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <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

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

This version

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-0.15.2-cp37-cp37m-win_amd64.whl (2.3 MB view details)

Uploaded CPython 3.7m Windows x86-64

ml_metadata-0.15.2-cp37-cp37m-manylinux2010_x86_64.whl (4.9 MB view details)

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

ml_metadata-0.15.2-cp37-cp37m-macosx_10_9_x86_64.whl (5.2 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

ml_metadata-0.15.2-cp36-cp36m-win_amd64.whl (2.3 MB view details)

Uploaded CPython 3.6m Windows x86-64

ml_metadata-0.15.2-cp36-cp36m-manylinux2010_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

ml_metadata-0.15.2-cp36-cp36m-macosx_10_9_x86_64.whl (5.2 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

ml_metadata-0.15.2-cp35-cp35m-win_amd64.whl (2.3 MB view details)

Uploaded CPython 3.5m Windows x86-64

ml_metadata-0.15.2-cp35-cp35m-manylinux2010_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ x86-64

ml_metadata-0.15.2-cp35-cp35m-macosx_10_6_intel.whl (5.2 MB view details)

Uploaded CPython 3.5m macOS 10.6+ intel

ml_metadata-0.15.2-cp27-cp27mu-manylinux2010_x86_64.whl (4.9 MB view details)

Uploaded CPython 2.7mu manylinux: glibc 2.12+ x86-64

ml_metadata-0.15.2-cp27-cp27m-macosx_10_9_x86_64.whl (5.2 MB view details)

Uploaded CPython 2.7m macOS 10.9+ x86-64

File details

Details for the file ml_metadata-0.15.2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 2.3 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 847b38887b7161d4cd8e7894e60f8321cceaa2faed0a9d57728b1870db01a6c0
MD5 4c3b315dd150354c19e5a229d10d4e68
BLAKE2b-256 a2212f0f89693c134b5f0eac3773c9326c282d43ad4400d11a0b7af6695333da

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 582d87b8140b6e8b85d1d9638590c1179ecb8563e12683164ea9264a2a53a1b1
MD5 7be212fa0e76fb58550f07d1b41cd7a0
BLAKE2b-256 451b45a5fcb533c4bb9c68986d34638e2af252b3c596a83be7cb78e31c006b55

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.2 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ee9b499ccd6f5267c587e72a56fcad319b38e26530b621e0f91de132b05dbaf4
MD5 be4b9fd3f15dfb89f475e3656dc1d658
BLAKE2b-256 da99e3a47b2fe73b5ebe018bbd4c27938bd226f0a8f5cd0adcb9c94b2d540a0c

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 2.3 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 13f35344e1e92c379d529096295a1eb72500e67c29e3765247c803943fe34d09
MD5 6f714220ceb8d0b014b6c05f9ee559d4
BLAKE2b-256 385f49dab890bb2381275882b73880a46d3173279a56ad074a6c5c1595e0ce08

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e9111a4793d9fd1407e78095030493b2232b6bdaa694fa9aaf289e294607f581
MD5 002f83f63c1207502bc23b865b03a415
BLAKE2b-256 8d40be58b7994dd169b18828da20d363104ae2af7e4040aed799bd54081d1149

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.2 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1bd3e40262c987492dd2783cd78c924b380531ac45868eb7144a7b58449330ea
MD5 c727dbf5654f1f5a9e48063789aa2003
BLAKE2b-256 a7240b137829de6cff19dd04dcf042a4b2a056c1628a3d8e657024a4655789f2

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 2.3 MB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 9cf190d7573177c9281448f58fec55410a570d8c05c2c4f97cba0e18eaa3dafa
MD5 b4b8ec1656c5525554ae994f2bc83ef6
BLAKE2b-256 996bab6c8f25dd4469a4e9b75dcc6cf84860f420a2543ec4065b61ee0f0f2555

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp35-cp35m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.5m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 2963307959f6f6921615aec4de1c6f442b11b55db9252b163b83981f86989517
MD5 33baf6109f1ef439318e9500ab1d3ae9
BLAKE2b-256 a6a64a47c4f35b2ba8026f3f6fa0b16cc6964b074a3fc1a3825af729d4b0d71b

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp35-cp35m-macosx_10_6_intel.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp35-cp35m-macosx_10_6_intel.whl
  • Upload date:
  • Size: 5.2 MB
  • Tags: CPython 3.5m, macOS 10.6+ intel
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp35-cp35m-macosx_10_6_intel.whl
Algorithm Hash digest
SHA256 38d3671dfe86de691f33d411d1200a1e46e4f809c3c34bd5bc5c6e61c5a7910d
MD5 28fec05610f7c23d3b01c0cbe6bf0e85
BLAKE2b-256 59296b55e273920a656174ffd848205bb7b500bd45f29f5f3f6bbeb42e0d207c

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp27-cp27mu-manylinux2010_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp27-cp27mu-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 2.7mu, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp27-cp27mu-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 91404d38846e7cb3acc18248395d1b2551954d83ee2473e4c24f352d4fdface3
MD5 b9188fea530ea931de48c1593f75403a
BLAKE2b-256 66853a41a7c310909b5bfc2c00b718af670385bce768e6919296939de322f515

See more details on using hashes here.

Provenance

File details

Details for the file ml_metadata-0.15.2-cp27-cp27m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: ml_metadata-0.15.2-cp27-cp27m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.2 MB
  • Tags: CPython 2.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.1.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.5rc1

File hashes

Hashes for ml_metadata-0.15.2-cp27-cp27m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 20c5d26cfb65fa778c21cd326c500215f19d0ad345d2f6fd8a50e02ee35eee59
MD5 4ac160e963b157de373706438b7dc4f0
BLAKE2b-256 690691219d70a7848b978e166681ebb421078360693652786159c9ae58f59a74

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