{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Jraph provides data structures and utilities for building and working with graph neural networks in JAX, including a GraphsTuple representation for graphs and a model zoo with reference implementations of message-passing algorithms.","skillfed_tags":["graph-neural-networks","jax-ecosystem","archived"],"use_cases":["Building graph neural network models for molecular property prediction or other graph-structured data using JAX's functional programming paradigm.","Batching and processing multiple graphs of different sizes efficiently with automatic padding and masking for JIT compilation.","Implementing custom message-passing algorithms by defining update functions for edges, nodes, and global graph attributes.","Distributing very large graphs (millions of edges) across multiple devices using the experimental distributed MPNN implementation.","Prototyping graph neural network architectures with reference models from the zoo that you can fork and adapt."],"what_it_does":"Jraph is a lightweight library for graph neural networks built on top of JAX. It provides a GraphsTuple data structure\u2014a named tuple that represents one or more directed graphs with node features, edge features, connectivity information (senders and receivers), and optional global attributes. The library includes utilities for batching multiple graphs, padding and masking for JIT compilation of variable-shaped graphs, and a model zoo with reference implementations of graph neural network architectures like GraphNetwork that use message-passing algorithms.\n\nThe package is designed for flexibility: it does not manage neural network parameters itself, leaving that to frameworks like Haiku or Flax. Instead, it provides the graph representation and message-passing infrastructure, allowing you to define custom update functions for edges, nodes, and global features. It also includes experimental support for distributing large graphs across multiple devices. However, the repository is archived and no longer maintained as of March 2024, so it may face compatibility issues with newer JAX releases.","worth_installing":"Yes, if you are committed to JAX for graph neural networks and can tolerate an abandoned codebase. The library is stable and well-designed, with low install friction and no known vulnerabilities. However, verify compatibility with your JAX version before adopting it for new projects, since there will be no maintenance updates. It is most suitable for research or prototyping rather than production systems requiring ongoing support."},"id":"jraph","links":{"html":"https://skillfed.io/packages/jraph","md":"https://skillfed.io/packages/jraph.md","pypi":"https://pypi.org/project/jraph/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2022-08-12","license_spdx":null,"license_treatment":"permissive","name":"jraph","python_support":"supports_current","summary":"Jraph: A library for Graph Neural Networks in Jax"},"popularity":{"monthly_downloads":88585,"position":13713,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.6.dev0"}