Taichi compatibility#
2025-10-21
4 min read time
Taichi is an open-source, imperative, and parallel programming language designed for high-performance numerical computation. Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate compute-intensive Python code by compiling it to native GPU or CPU instructions.
Taichi is widely used across various domains, including real-time physical simulation, numerical computing, augmented reality, artificial intelligence, computer vision, robotics, visual effects in film and gaming, and general-purpose computing.
Support overview#
The ROCm-supported version of Taichi is maintained in the official ROCm/taichi repository, which differs from the taichi-dev/taichi upstream repository.
To get started and install Taichi on ROCm, use the prebuilt Docker image, which includes ROCm, Taichi, and all required dependencies.
See the ROCm Taichi installation guide for installation and setup instructions.
You can also consult the upstream Installation guide for additional context.
Version support#
Taichi is supported on ROCm 6.3.2.
Supported devices#
Officially Supported: AMD Instinct™ MI250X, MI210X (with the exception of Taichi’s GPU rendering system, CGUI)
Upcoming Support: AMD Instinct™ MI300X
Use cases and recommendations#
The Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs blog highlights Taichi as an open-source programming language designed for high-performance numerical computation, particularly in domains like real-time physical simulation, artificial intelligence, computer vision, robotics, and visual effects. Taichi is embedded in Python and uses just-in-time (JIT) compilation frameworks like LLVM to optimize execution on GPUs and CPUs. The blog emphasizes the versatility of Taichi in enabling complex simulations and numerical algorithms, making it ideal for developers working on compute-intensive tasks. Developers are encouraged to follow recommended coding patterns and utilize Taichi decorators for performance optimization, with examples available in the ROCm/taichi_examples repository. Prebuilt Docker images integrating ROCm, PyTorch, and Taichi are provided for simplified installation and deployment, making it easier to leverage Taichi for advanced computational workloads.
Docker image compatibility#
AMD validates and publishes ready-made ROCm Taichi Docker images with ROCm backends on Docker Hub. The following Docker image tag and associated inventories represent the latest Taichi version from the official Docker Hub. Click to view the image on Docker Hub.
Docker image |
ROCm |
Taichi |
Ubuntu |
Python |
|---|---|---|---|---|
| rocm/taichi | 22.04 |