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        "author": "Google LLC",
        "author_email": "tensorflow-extended-dev@googlegroups.com",
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            "License :: OSI Approved :: Apache Software License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
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            "Topic :: Scientific/Engineering",
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        "description": "\n# ML Metadata\n\n[![Python](https://img.shields.io/badge/python%20-3.7%7C3.8%7C3.9-blue)](https://github.com/google/ml-metadata)\n[![PyPI](https://badge.fury.io/py/ml-metadata.svg)](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 -i 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 `{37, 38, 39}`.\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 16.04 or later.\n*   Windows 7 or later.\n",
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