The Taichi Programming Language [Details]
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
- (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].
- (Oct 7, 2019) Released experimental python 3.6 wheels on Linux (tested on Ubuntu 16.04/18.04) for those who are eager to try without building from source. More stable releases are coming in a few days. To install them:
Notes:
- You still need to clone this repo for demo scripts under
examples
. You do not need to executeinstall.py
. After installation usingpip
you can simply go toexamples
and execute, e.g.,python3 mpm.py
. - Make sure you have
clang-7
. On Ubuntu 18.04 you can install it withsudo apt-get install clang-7
. See here for installingclang-7
on Ubuntu 16.04. You will also needg++-7
on Ubuntu16.04 (for c++17 headers). To install:
# Ubuntu 16.04 only
sudo apt-get install -y software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update
sudo apt install g++-7 -y
- Make sure you clear your legacy Taichi installation (if applicable) by cleaning the environment variables (delete
TAICHI_REPO_DIR
, and remove legacy taichi fromPYTHONPATH
) 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.
- 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.