{ "result": { "1.17.0+ppc64le1": { "name": "ml-metadata", "version": "1.17.0+ppc64le1", "metadata_version": "2.4", "summary": "A library for maintaining metadata for artifacts.", "home_page": "https://github.com/google/ml-metadata", "author": "Google LLC", "author_email": "tensorflow-extended-dev@googlegroups.com", "maintainer": "", "maintainer_email": "", "license": "Apache 2.0", "description": "\n# ML Metadata\n\n[![Python](https://img.shields.io/badge/python%7C3.9%7C3.10%7C3.11-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 --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 ` 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", "keywords": "machine learning metadata tfx", "platform": [], "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Environment :: MetaData :: IBM Python Ecosystem" ], "download_url": "https://github.com/google/ml-metadata/tags", "supported_platform": [], "comment": "", "provides": [], "requires": [], "obsoletes": [], "project_urls": [], "provides_dist": [], "obsoletes_dist": [], "requires_dist": [ "absl-py<2.0.0,>=0.9", "attrs<24,>=20.3", "grpcio<2,>=1.8.6", "protobuf<5,>=4.25.2", "six<2,>=1.10", "pre-commit; extra == \"lint\"", "mkdocs; extra == \"docs\"", "mkdocs-material; extra == \"docs\"", "mkdocstrings[python]; extra == \"docs\"", "griffe-inherited-docstrings; extra == \"docs\"", "mkdocs-autorefs; extra == \"docs\"", "black; extra == \"docs\"", "mkdocs-jupyter; extra == \"docs\"", "mkdocs-caption; extra == \"docs\"" ], "requires_external": [], "requires_python": ">=3.9,<4", "description_content_type": "text/markdown", "provides_extras": [ "lint", "docs" ], "dynamic": [ "author", "author-email", "classifier", "description", "description-content-type", "download-url", "home-page", "keywords", "license", "license-file", "provides-extra", "requires-dist", "requires-python", "summary" ], "license_expression": "", "license_file": [ "LICENSE" ], "+links": [ { "rel": "releasefile", "hash_spec": "sha256=977b720ac8ae64cbcd94717e1434d6507a87d005f656587261a7e700c2636eab", "hashes": { "sha256": "977b720ac8ae64cbcd94717e1434d6507a87d005f656587261a7e700c2636eab" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/977/b720ac8ae64cb/ml_metadata-1.17.0+ppc64le1-cp311-cp311-manylinux_2_34_ppc64le.whl", "log": [ { "what": "upload", "who": "ppc64le", "when": [ 2026, 5, 11, 11, 44, 53 ], "dst": "ppc64le/linux" } ] } ] }, "1.21.0": { "name": "ml-metadata", "version": "1.21.0", "metadata_version": "2.4", "summary": "A library for maintaining metadata for artifacts.", "home_page": "https://github.com/google/ml-metadata", "author": "Google LLC", "author_email": "tensorflow-extended-dev@googlegroups.com", "maintainer": "", "maintainer_email": "", "license": "Apache 2.0", "description": "\n# ML Metadata\n\n[![Python](https://img.shields.io/badge/python%7C3.10%7C3.11%7C3.12%7C3.13-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 --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 `{310, 311, 312, 313}`.\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 ` 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\n## Releasing Wheels to PyPI\n\n### Setup (Required for both release methods)\n\nBefore releasing, you need to set up the PyPI environment and token once:\n\n**Step 1: Create PyPI environment**\n\nCreate a new environment named `pypi` in the GitHub repository:\n- Go to https://github.com/google/ml-metadata/settings/environments/new\n- Name it `pypi`\n- Click \"Configure environment\"\n\n**Step 2: Add PYPI_API_TOKEN secret**\n\nAdd your PyPI token to the `pypi` environment:\n- In the `pypi` environment settings, scroll to \"Environment secrets\"\n- Click \"Add secret\"\n- Name: `PYPI_API_TOKEN` (use this exact name)\n- Value: Your PyPI API token\n- Click \"Add secret\"\n\n**Step 3: Commit and push your release branch**\n\nEnsure your release branch has the correct version set in `ml_metadata/version.py`, then:\n\n```bash\ngit add ml_metadata/version.py\ngit commit -m \"Prepare release vX.Y.Z\"\ngit push origin your-release-branch\n```\n\n### Part 1: Releasing via `workflow_dispatch`\n\nThis method allows you to manually trigger a release from any branch without creating a GitHub release.\n\n**Steps** (after completing setup above):\n\n1. Navigate to the GitHub Actions page: https://github.com/google/ml-metadata/actions\n2. Find and select the `Build ml-metadata with Conda` workflow: https://github.com/google/ml-metadata/actions/workflows/conda-build.yml\n3. Click the \"Run workflow\" dropdown button.\n4. Select your release branch from the dropdown menu.\n5. Click \"Run workflow\".\n\nThe workflow will build wheels for all supported Python versions and automatically upload them to PyPI if the token is configured correctly.\n\n### Part 2: Releasing via GitHub Release\n\nThis method creates a formal GitHub release with a tag, which automatically triggers the build and upload workflow.\n\n**Steps** (after completing setup above):\n\n1. Go to the Releases tab: https://github.com/google/ml-metadata/releases\n2. Click the `Draft new release` button (you'll be redirected to https://github.com/google/ml-metadata/releases/new)\n3. Click the `Select tag` button and create a new tag for your release (e.g., `v1.21.0`)\n4. Click the `Target` dropdown and select your release branch\n5. Fill in the **Release title** and **Release notes** sections\n6. Choose the release type:\n - Check `Set as a pre-release` if this is a beta/test release\n - Leave unchecked for `Set as the latest release` for stable releases\n7. Click the `Publish release` button\n8. Verify the workflow is running by going to the Actions tab: https://github.com/google/ml-metadata/actions/workflows/conda-build.yml\n\nThe `Build ml-metadata with Conda` workflow will automatically trigger and build/upload wheels to PyPI if the token is configured correctly.\n", "keywords": "machine learning metadata tfx", "platform": [], "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Environment :: MetaData :: IBM Python Ecosystem" ], "download_url": "https://github.com/google/ml-metadata/tags", "supported_platform": [], "comment": "", "provides": [], "requires": [], "obsoletes": [], "project_urls": [], "provides_dist": [], "obsoletes_dist": [], "requires_dist": [ "absl-py<2.0.0,>=0.9", "attrs<24,>=20.3", "grpcio<2,>=1.8.6", "protobuf<7,>=6.31.1", "six<2,>=1.10", "pre-commit; 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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 ` 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", "keywords": "machine learning metadata tfx", "platform": [], "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Environment :: MetaData :: IBM Python Ecosystem" ], "download_url": "https://github.com/google/ml-metadata/tags", "supported_platform": [], "comment": "", "provides": [], "requires": [], "obsoletes": [], "project_urls": [], "provides_dist": [], "obsoletes_dist": [], "requires_dist": [ "absl-py<2.0.0,>=0.9", "attrs<24,>=20.3", "grpcio<2,>=1.8.6", "protobuf<5,>=4.25.2", "six<2,>=1.10", "pre-commit; extra == \"lint\"", "mkdocs; extra == \"docs\"", "mkdocs-material; extra == \"docs\"", "mkdocstrings[python]; extra == \"docs\"", "griffe-inherited-docstrings; extra == \"docs\"", "mkdocs-autorefs; extra == \"docs\"", "black; extra == \"docs\"", "mkdocs-jupyter; extra == \"docs\"", "mkdocs-caption; extra == \"docs\"" ], "requires_external": [], "requires_python": ">=3.9,<4", "description_content_type": "text/markdown", "provides_extras": [ "lint", "docs" ], "dynamic": [ "author", "author-email", "classifier", "description", "description-content-type", "download-url", "home-page", "keywords", "license", "license-file", "provides-extra", "requires-dist", "requires-python", "summary" ], "license_expression": "", "license_file": [ "LICENSE" ], "+links": [ { "rel": "releasefile", "hash_spec": "sha256=989d6b8e3427f4dc304622e6f6807fdf241f6aab93a4a9775bd1061017388f11", "hashes": { "sha256": "989d6b8e3427f4dc304622e6f6807fdf241f6aab93a4a9775bd1061017388f11" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/989/d6b8e3427f4dc/ml_metadata-1.17.0-cp311-cp311-manylinux_2_34_ppc64le.whl", "log": [ { "what": "upload", "who": "ppc64le", "when": [ 2026, 7, 27, 12, 50, 31 ], "dst": "ppc64le/linux" } ] } ] } }, "type": "projectconfig" }