A library for maintaining metadata for artifacts.
Project description
ML Metadata
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 --extra-index-url 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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
File details
Details for the file ml_metadata-1.9.0-cp39-cp39-win_amd64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp39-cp39-win_amd64.whl
- Upload date:
- Size: 1.6 MB
- Tags: CPython 3.9, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 20dc7617f5ddb542e98b2d0e312fff57c1ca8268a2feb363df8fd4ef7f6005b5 |
|
| MD5 | e6b5b88563ca7d2fcc519379f86d1a2b |
|
| BLAKE2b-256 | 73feaa47f7262c723eb1fc164393334351d50d97da992af916cdb63926c281b5 |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
- Upload date:
- Size: 6.6 MB
- Tags: CPython 3.9, manylinux: glibc 2.12+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d19ceb072f8690ea3cec1fa7aa5e7837f80b4258fccd12ad48928b7643732692 |
|
| MD5 | e5629c133873866b76f08f2df6e471a3 |
|
| BLAKE2b-256 | 56ad8feac0a4cdc9bbb53169addd1473358b27d04ad54bb340ecf65b0943f20d |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp39-cp39-macosx_10_14_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp39-cp39-macosx_10_14_x86_64.whl
- Upload date:
- Size: 18.9 MB
- Tags: CPython 3.9, macOS 10.14+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 4c32a0eee9d2edbaa56e34a3002c2aa7e9379ab9ada78bfa809781f76d62e273 |
|
| MD5 | 3d5f1afebc2d7eafa2e46b4c55ced62c |
|
| BLAKE2b-256 | 395477b5e776b35500c1791bbf3e38b9a666133d461191ea7bd2f9334a05fc8a |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp38-cp38-win_amd64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp38-cp38-win_amd64.whl
- Upload date:
- Size: 1.6 MB
- Tags: CPython 3.8, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 83f862b10dd9452bc1b3d19c742eafe8ec9966e6c0029cfda9728023965734b9 |
|
| MD5 | 5f2ee1784e3addf7df12416bc6c6273d |
|
| BLAKE2b-256 | 344339aac9b729cdc1e1652c00f9b56dde37f3b152d8ab3609d964ecdf1c0dba |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
- Upload date:
- Size: 6.6 MB
- Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 757e2d1d0570a50889b30f6eb0de301d63b3a597eb3876ac3c0648adf82ee1ce |
|
| MD5 | d2016a27fc004dc882d84a1a915104aa |
|
| BLAKE2b-256 | defea061e3d3b4fd3d1183eca3c86ab9f1133ec63f3b66c657559d9b8669f6ae |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp38-cp38-macosx_10_9_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp38-cp38-macosx_10_9_x86_64.whl
- Upload date:
- Size: 18.9 MB
- Tags: CPython 3.8, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | fea45201e6832d16934173605d415a3af5fcb68e3356f72b7a6f039f43f49672 |
|
| MD5 | 47fb13c6537c39bd012a5dcb1335d158 |
|
| BLAKE2b-256 | 4250e1f3cfca015f2878063ed688f93cb34bde9081a273e447257927cfcc9b9f |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp37-cp37m-win_amd64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp37-cp37m-win_amd64.whl
- Upload date:
- Size: 1.6 MB
- Tags: CPython 3.7m, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.7.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7e6e65b0d2e8aab58f9cf8675775bac4eaf09ca51a763bb8d85e52a3377abef7 |
|
| MD5 | 47e24f88372cc5baa1d1e2f7ed7a93e6 |
|
| BLAKE2b-256 | 12b82b0876b06113095f61adcad4bfcc009e366e2f10597463c06c0712527f3e |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
- Upload date:
- Size: 6.6 MB
- Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.7.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2c11c801bc45c002d2ce950630a5d1d7e4fe00025acf5ae48f2817f085464186 |
|
| MD5 | 7ee024623978e734d647876daeb66131 |
|
| BLAKE2b-256 | 34042d7a39d610c5ebe697aaf579e67df854073ee9f5d8976db7552a99d1fa3d |
Provenance
File details
Details for the file ml_metadata-1.9.0-cp37-cp37m-macosx_10_9_x86_64.whl.
File metadata
- Download URL: ml_metadata-1.9.0-cp37-cp37m-macosx_10_9_x86_64.whl
- Upload date:
- Size: 18.9 MB
- Tags: CPython 3.7m, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 4a0fc23d753c14263f9a7d9f06b78ba62a8dc9c686e2c7ce2f14b03be8cc72f4 |
|
| MD5 | 276587ff09608d9572862c0e9a9a1918 |
|
| BLAKE2b-256 | e339d726589c5aa5a97715317c0737f69c04433972ed9e9052261729372dd0c2 |
