Linux (CUDA)	OS X	Chat

# CPU only. No GPU/CUDA needed
python3 -m pip install taichi-nightly

# With GPU (CUDA 10.0) support
python3 -m pip install taichi-nightly-cuda-10-0

# With GPU (CUDA 10.1) support
python3 -m pip install taichi-nightly-cuda-10-1

High-Performance Computation on Sparse Data Structures [Paper] [Video] [Language Details] [Taichi Compiler Developer Installation]

Examples

Updates

• (Nov 1, 2019) v0.0.77 released.
  • Pip wheels now support OS X 10.14+;
  • LLVM backend is the default backend. No need to install gcc-7 or clang-7 anymore. To use legacy backends, export TI_LLVM=0;
  • LLVM compilation is improved by 2x;
  • More friendly syntax error messages.
• (Oct 30, 2019) v0.0.72 released.
  • LLVM GPU backend now as fast as the legacy (yet optimized) CUDA backend. To enable, export TI_LLVM=1;
  • Bug fixes: LLVM struct for list generation.
• (Oct 29, 2019) v0.0.71 released. LLVM GPU backend performance greatly improved. Frontend compiler now emits readable syntax error messages.
• (Oct 28, 2019) v0.0.70 released. This version comes with experimental LLVM backends for x86_64 and CUDA (via NVVM/PTX). GPU kernel compilation speed is improved by 10x. To enable, update the taichi package and export TI_LLVM=1.
• (Oct 24, 2019) Python wheels (v0.0.61) released for Python 3.6/3.7 and CUDA 10.0/10.1 on Ubuntu 16.04+. Contributors of this release include Yuanming Hu, robbertvc, Zhoutong Zhang, Tao Du, Srinivas Kaza, and Kenneth Lozes.
• (Oct 22, 2019) Added support for kernel templates. Kernel templates allow users to pass in taichi tensors and compile-time constants as kernel parameters.
• (Oct 9, 2019) Compatibility improvements. Added a basic PyTorch interface. [Example].

Notes:

• You still need to clone this repo for demo scripts under examples. You do not need to execute install.py or dev_setup.py. After installation using pip you can simply go to examples and execute, e.g., python3 mpm_fluid.py.
• Make sure you clear your legacy Taichi installation (if applicable) by cleaning the environment variables (delete TAICHI_REPO_DIR, and remove legacy taichi from PYTHONPATH) in your .bashrc or .zshrc. Or you can simply do this in your shell to temporarily clear them:

export PYTHONPATH=
export TAICHI_REPO_DIR=

The Taichi Library [Legacy branch]

Taichi is an open-source computer graphics library that aims to provide easy-to-use infrastructures for computer graphics R&D. It's written in C++14 and wrapped friendly with Python.

News

• May 17, 2019: Giga-Voxel SPGrid Topology Optimization Solver is released!
• March 4, 2019: MLS-MPM/CPIC solver is now MIT-licensed!
• August 14, 2018: MLS-MPM/CPIC solver reloaded! It delivers 4-14x performance boost over the previous state of the art on CPUs.

Getting Started (Legacy)