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The following commit(s) were added to refs/heads/main by this push:
new bd19463 README updates (#120)
bd19463 is described below
commit bd1946370ec6b8cf6527d691c17c92813aa165c7
Author: Tianqi Chen <[email protected]>
AuthorDate: Tue Oct 14 15:51:28 2025 -0400
README updates (#120)
Co-authored-by: gemini-code-assist[bot]
<176961590+gemini-code-assist[bot]@users.noreply.github.com>
---
README.md | 9 +++++----
1 file changed, 5 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index c21e46f..afea5ec 100644
--- a/README.md
+++ b/README.md
@@ -23,16 +23,17 @@ Apache TVM FFI is an open ABI and FFI for machine learning
systems. It is a mini
yet flexible open convention with the following systems in mind:
- Kernel libraries: ship one wheel to support multiple frameworks, Python
versions, and different languages.
-- Kernel DSLs: reusable open ABI for JIT and AOT kernel exposure to PyTorch,
JAX, and other machine learning systems.
+- Kernel DSLs: reusable open ABI for JIT and AOT kernel exposure to PyTorch,
JAX, and other ML runtimes.
- ML frameworks and runtimes: unified mechanism to connect libraries and DSLs
that adopt the ABI convention.
- Coding agents: unified mechanism to package and ship generated code to
production environments.
-- ML infrastructure: cross-language support for Python, C++, Rust, and other
languages that interface with the ABI.
+- ML infrastructure: cross-language support for Python, C++, and Rust, and
DSLs.
It has the following technical features:
+- DLPack-compatible Tensor data ABI to seamlessly support many frameworks such
as PyTorch, JAX, CuPy and others that support DLPack convention.
+- Compact value and function calling convention for common data types in
machine learning.
- Stable, minimal, and flexible C ABI to support machine learning system
use-cases.
-- First class support for PyTorch, JAX, and other array libraries.
-- Out-of-the-box multi-language support for Python, C++, Rust, and future
compatibility to other languages that bind to the ABI.
+- Out-of-the-box multi-language support for Python, C++, Rust, and future path
for other languages.
With these technical solutions, we can enable better **interoperability**
across machine learning frameworks,
libraries, kernel DSLs, and coding agents, **ship one wheel** to support
multiple frameworks and Python versions (including free-threaded python),
