{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"Provides a stable, minimal C ABI and FFI for machine learning systems to expose kernels, DSLs, and runtime extensions across frameworks like PyTorch, JAX, and NumPy with zero-copy interop.","skillfed_tags":["ml-interop","kernel-library","ffi-abi"],"use_cases":["Ship a single compiled kernel library wheel that works with PyTorch, JAX, and CuPy without rebuilding","Expose a custom kernel DSL (like TileLang or cuteDSL) through a stable ABI for consumption by multiple frameworks","Build ML infrastructure that accepts ABI-compliant extensions from third-party kernel libraries","Enable zero-copy tensor passing between PyTorch and JAX using DLPack without data copying","Integrate generated code from coding agents into production ML systems via a unified ABI"],"what_it_does":"Apache TVM FFI is an open ABI and FFI standard designed to let machine learning libraries, kernel DSLs, and runtimes interoperate across frameworks without recompilation. It defines a stable C ABI for exposing kernels and extensions, and provides Python bindings with zero-copy tensor interchange via the DLPack protocol. The package enables a single compiled wheel to work across PyTorch, JAX, CuPy, and NumPy, and supports multi-language bindings (Python, C++, Rust). It is currently in RFC status, meaning the core ABI is stable but the project is actively gathering community feedback before moving to semantic versioning.\n\nThe package depends only on typing-extensions and is available as pre-built wheels for modern Python versions (3.10\u20133.14) across Linux, macOS, and Windows. It is intended for kernel library authors, DSL framework developers, and ML infrastructure teams who need a framework-agnostic way to ship optimized code and integrate with multiple downstream consumers.","worth_installing":"Yes, if you are building or consuming ML kernel libraries or DSLs that need to work across multiple frameworks. The stable C ABI, zero-copy interop, and multi-language support make it a solid choice for infrastructure teams. However, be aware that the project is in RFC stage and may evolve; if you require absolute API stability, wait for the first semantic-versioning release. No known security vulnerabilities."},"id":"apache-tvm-ffi","links":{"html":"https://skillfed.io/packages/apache-tvm-ffi","md":"https://skillfed.io/packages/apache-tvm-ffi.md","pypi":"https://pypi.org/project/apache-tvm-ffi/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":null,"license_treatment":"permissive","name":"apache-tvm-ffi","python_support":"supports_current","summary":"tvm ffi"},"popularity":{"monthly_downloads":7843396,"position":1689,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.13.post3"}