Tensorflow Metadata is used in Python projects. Library and standards for schema and statistics. It has 3 direct runtime dependencies. Check its dependency graph on PyDeps to understand the full transitive dependency tree, reverse dependents, known CVEs, and license compatibility before installing.
Library and standards for schema and statistics.
tensorflow-metadata declares 3 direct runtime dependencies on PyPI. Each one is resolved into the full dependency tree below:
Beyond its direct dependencies, tensorflow-metadata can pull in further packages through its dependency tree. PyDeps resolves the entire chain from PyPI and deps.dev so you can see every transitive (nested) dependency of tensorflow-metadata, expand any node on demand, and understand the full set of code that ships when you run pip install tensorflow-metadata.
PyDeps checks tensorflow-metadata and every package in its dependency tree against the OSV vulnerability database in real time. For each CVE you can see the severity, the affected version ranges, and the first fixed version, so you know exactly which tensorflow-metadata version is safe to install before you ship.
tensorflow-metadata is distributed under the Apache 2.0 license. PyDeps also shows the license of every dependency in the tree so you can audit license compatibility across your whole tensorflow-metadata install, not just the top-level package.
Install from PyPI with pip install tensorflow-metadata. For offline or air-gapped environments, PyDeps can download tensorflow-metadata together with every resolved dependency as wheel files in a single bundle, matched to your target Python version and operating system.
Switch to the dependents view to see the reverse dependencies of tensorflow-metadata — the PyPI packages that list tensorflow-metadata as a requirement. Reverse dependencies are a strong signal of how widely a package is trusted and how disruptive a breaking change would be.