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 -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 {36, 37, 38}.

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 files for ml-metadata 0.28.0.dev20210126

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for ml-metadata 0.28.0.dev20210126
File	Interpreter	ABI	Platform	Reset
ml_metadata-0.28.0.dev20210126-cp36-cp36m-win_amd64.whl 2.4 MB Jan 26, 2021	CPython 3.6	CPython 3.6 pymalloc	Windows x86-64	Details

Release files / ml_metadata-0.28.0.dev20210126-cp36-cp36m-win_amd64.whl

Download URL	ml_metadata-0.28.0.dev20210126-cp36-cp36m-win_amd64.whl
Size	2.4 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`76fb4d66d3808b9e01c85b5f85afdb4d9d20a5927c6369e66a2383fd6d0f6fa5`
BLAKE2b-256 checksum How to use checksums	`204c46b14454a82a9f20616413016cdb3016b26efc87b99cca48993a219c7173`
Upload date	Jan 26, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.3.0 pkginfo/1.4.2 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.7`