Ml Collections is used in Python projects. ML Collections is a library of Python collections designed for ML usecases. It has 2 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.
ML Collections is a library of Python collections designed for ML usecases.
ml-collections declares 2 direct runtime dependencies on PyPI. Each one is resolved into the full dependency tree below:
Beyond its direct dependencies, ml-collections 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 ml-collections, expand any node on demand, and understand the full set of code that ships when you run pip install ml-collections.
PyDeps checks ml-collections 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 ml-collections version is safe to install before you ship.
ml-collections is distributed under the Apache Software License. PyDeps also shows the license of every dependency in the tree so you can audit license compatibility across your whole ml-collections install, not just the top-level package.
Install from PyPI with pip install ml-collections. For offline or air-gapped environments, PyDeps can download ml-collections 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 ml-collections — the PyPI packages that list ml-collections as a requirement. Reverse dependencies are a strong signal of how widely a package is trusted and how disruptive a breaking change would be.