# ML Metadata
[](https://github.com/google/ml-metadata)
[](https://badge.fury.io/py/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](https://github.com/google/ml-metadata/blob/master/g3doc/get_started.md)
## Installing from PyPI
The recommended way to install ML Metadata is to use the
[PyPI package](https://pypi.org/project/ml-metadata/):
```bash
pip install ml-metadata
```
Then import the relevant packages:
```python
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:
```bash
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](https://docs.docker.com/install/);
[docker-compose](https://docs.docker.com/compose/install/).
Then, run the following at the project root:
```bash
DOCKER_SERVICE=manylinux-python${PY_VERSION}
sudo docker-compose build ${DOCKER_SERVICE}
sudo docker-compose run ${DOCKER_SERVICE}
```
where `PY_VERSION` is one of `{39, 310, 311}`.
A wheel will be produced under `dist/`, and installed as follows:
```shell
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](https://bazel.build/versions/master/docs/install.html).
#### Install cmake
If cmake is not installed on your system, install it now by following [these
directions](https://cmake.org/install/).
### 2. Clone ML Metadata repository
```shell
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:
```shell
python setup.py bdist_wheel
```
You can find the generated `.whl` file in the `dist` subdirectory.
### 4. Install the pip package
```shell
pip install dist/*.whl
```
### 5.(Optional) Build the grpc server
ML Metadata uses Bazel to build the c++ binary from source:
```shell
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 20.04 or later.
* [DEPRECATED] Windows 10 or later. For a Windows-compatible library, please
refer to MLMD 1.14.0 or earlier versions.
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"description": "\n# ML Metadata\n\n[](https://github.com/google/ml-metadata)\n[](https://badge.fury.io/py/ml-metadata)\n\n*ML Metadata (MLMD)* is a library for recording and retrieving metadata\nassociated with ML developer and data scientist workflows.\n\nNOTE: ML Metadata may be backwards incompatible before version 1.0.\n\n## Getting Started\n\nFor more background on MLMD and instructions on using it, see the\n[getting started guide](https://github.com/google/ml-metadata/blob/master/g3doc/get_started.md)\n\n## Installing from PyPI\n\nThe recommended way to install ML Metadata is to use the\n[PyPI package](https://pypi.org/project/ml-metadata/):\n\n```bash\npip install ml-metadata\n```\n\nThen import the relevant packages:\n\n```python\nfrom ml_metadata import metadata_store\nfrom ml_metadata.proto import metadata_store_pb2\n```\n\n### Nightly Packages\n\nML Metadata (MLMD) also hosts nightly packages at\nhttps://pypi-nightly.tensorflow.org on Google Cloud. To install the latest\nnightly package, please use the following command:\n\n```bash\npip install --extra-index-url https://pypi-nightly.tensorflow.org/simple ml-metadata\n```\n\n## Installing with Docker\n\nThis is the recommended way to build ML Metadata under Linux, and is\ncontinuously tested at Google.\n\nPlease first install `docker` and `docker-compose` by following the directions:\n[docker](https://docs.docker.com/install/);\n[docker-compose](https://docs.docker.com/compose/install/).\n\nThen, run the following at the project root:\n\n```bash\nDOCKER_SERVICE=manylinux-python${PY_VERSION}\nsudo docker-compose build ${DOCKER_SERVICE}\nsudo docker-compose run ${DOCKER_SERVICE}\n```\n\nwhere `PY_VERSION` is one of `{39, 310, 311}`.\n\nA wheel will be produced under `dist/`, and installed as follows:\n\n```shell\npip install dist/*.whl\n```\n\n## Installing from source\n\n\n### 1. Prerequisites\n\nTo compile and use ML Metadata, you need to set up some prerequisites.\n\n\n#### Install Bazel\n\nIf Bazel is not installed on your system, install it now by following [these\ndirections](https://bazel.build/versions/master/docs/install.html).\n\n#### Install cmake\nIf cmake is not installed on your system, install it now by following [these\ndirections](https://cmake.org/install/).\n\n### 2. Clone ML Metadata repository\n\n```shell\ngit clone https://github.com/google/ml-metadata\ncd ml-metadata\n```\n\nNote that these instructions will install the latest master branch of ML\nMetadata. If you want to install a specific branch (such as a release branch),\npass `-b <branchname>` to the `git clone` command.\n\n### 3. Build the pip package\n\nML Metadata uses Bazel to build the pip package from source:\n\n```shell\npython setup.py bdist_wheel\n```\n\nYou can find the generated `.whl` file in the `dist` subdirectory.\n\n### 4. Install the pip package\n\n```shell\npip install dist/*.whl\n```\n\n### 5.(Optional) Build the grpc server\n\nML Metadata uses Bazel to build the c++ binary from source:\n\n```shell\nbazel build -c opt --define grpc_no_ares=true //ml_metadata/metadata_store:metadata_store_server\n```\n\n## Supported platforms\n\nMLMD is built and tested on the following 64-bit operating systems:\n\n* macOS 10.14.6 (Mojave) or later.\n* Ubuntu 20.04 or later.\n* [DEPRECATED] Windows 10 or later. For a Windows-compatible library, please\n refer to MLMD 1.14.0 or earlier versions.\n",
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