Dear Community: We are excited to announce apache-tvm-ffi v0.1.0
Apache TVM FFI is an open ABI and FFI for ML systems. It is a minimal, framework-agnostic, 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 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++, 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. - Out-of-the-box multi-language support for Python, C++, Rust, and future path for other languages. You can check out the source release here Index of /tvm/tvm-ffi-v0.1.0 <https://downloads.apache.org/tvm/tvm-ffi-v0.1.0/> and also can try out the wheels through apache-tvm-ffi ------------------------------ Visit Topic <https://discuss.tvm.apache.org/t/announcing-apache-tvm-ffi-v0-1-0/18696/1> to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, click here <https://discuss.tvm.apache.org/email/unsubscribe/dd9ff610ea382c052b305f139341d19f672ffaefcad5dcb2dd901574e8bde8f6> .
