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# CPU only. No GPU/CUDA needed. (Linux, OS X and Windows)
python3 -m pip install taichi-nightly

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

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

	Linux (CUDA)	OS X	Windows
Build
PyPI

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

Short-term goals (by the end of Dec 2019)

• Fully implement the LLVM backend to replace the legacy source-to-source C++/CUDA backends
  • Dense computation done
  • Sparse data structures to be implemented
• Tune the performance of the LLVM backend to match that of the legacy source-to-source backends
• Language documentation

Updates

• (Dec 16, 2019) v0.2.6 released.
  • ti.GUI.set_image(np.ndarray/Taichi tensor)
  • Inplace adds are atomic by default. E.g., x[i] += j is equivalent to ti.atomic_add(x[i], j)
  • ti.func arguments are forced to pass by value
  • min/max can now take more than two arguments, e.g. max(a, b, c, d)
  • Matrix operators transposed, trace, polar_decompose, determinant promoted to ti scope. I.e., users can now use ti.transposed(M) instead of ti.Matrix.transposed(M)
  • ti.get_runtime().set_verbose(False) to eliminate verbose outputs
  • LLVM backend now supports multithreading on CPUs
  • LLVM backend now supports random number generators (ti.random(ti.i32/i64/f32/f64)
• (Dec 5, 2019) v0.2.3 released.
  • Simplified interaction between Taichi, numpy and PyTorch
    • taichi_scalar_tensor.to_numpy()/from_numpy(numpy_array)
    • taichi_scalar_tensor.to_torch()/from_torch(torch_array)
• (Dec 4, 2019) v0.2.2 released.
  • Argument type ti.ext_arr() now takes PyTorch tensors
• (Dec 3, 2019) v0.2.1 released.
  • Improved type mismatch error message
  • native min/max supprt
  • Tensor access index dimensionality checking
  • Matrix.to_numpy, Matrix.zero, Matrix.identity, Matrix.fill
  • Warning instead of error on lossy stores
  • Added some initial support for cross-referencing local variables in different offloaded blocks.
• (Nov 28, 2019) v0.2.0 released.
  • More friendly syntax error when passing non-compile-time-constant values to ti.static
  • Systematically resolved the variable name resolution issue
  • Better interaction with numpy:
    • numpy arrays passed as a ti.ext_arr() [examples]
      • i32/f32/i64/f64 data type support for numpy
      • Multidimensional numpy arrays now supported in Taichi kernels
    • Tensor.to_numpy() and Tensor.from_numpy(numpy.ndarray) supported [examples]
    • Corresponding PyTorch tensor interaction will be supported very soon. Now only 1D f32 PyTorch tensors supproted when using ti.ext_arr(). Please use numpy arrays as intermediate buffers for now
  • Indexing arrays with an incorrect number of indices now results in a syntax error
  • Tensor shape reflection: [examples]
    • Tensor.dim() to retrieve the dimensionality of a global tensor
    • Tensor.shape() to retrieve the shape of a global tensor
    • Note the above queries will cause data structures to be materialized
  • struct-for (e.g. for i, j in x) now supports iterating over tensors with non power-of-two dimensions
  • Handy tensor filling: [examples]
    • Tensor.fill(x) to set all entries to x
    • Matrix.fill(x) to set all entries to x, where x can be a scalar or ti.Matrix of the same size
  • Reduced python package size
  • struct-for with grouped indices for better metaprogramming, especially in writing dimensionality-independent code, in e.g. physical simulation: [examples]

for I in ti.grouped(x): # I is a vector of size x.dim() and data type i32
  x[I] = 0
  
# If tensor x is 2D 
for I in ti.grouped(x): # I is a vector of size x.dim() and data type i32
  y[I + ti.Vector([0, 1])] = I[0] + I[1]
# is equivalent to
for i, j in x:
  y[i, j + 1] = i + j

• (Nov 27, 2019) v0.1.5 released.

  • Better modular programming support
  • Disalow the use of ti.static outside Taichi kernels
  • Documentation improvements (WIP)
  • Codegen bug fixes
  • Special thanks to Andrew Spielberg and KLozes for bug report and feedback.

• (Nov 22, 2019) v0.1.3 released.

  • Object-oriented programming. [Example]
  • native Python function translation in Taichi kernels:
    • Use print instead of ti.print
    • Use int() instead of ti.cast(x, ti.i32) (or ti.cast(x, ti.i64) if your default integer precision is 64 bit)
    • Use float() instead of ti.cast(x, ti.f32) (or ti.cast(x, ti.f64) if your default float-point precision is 64 bit)
    • Use abs instead of ti.abs
    • Use ti.static_print for compile-time printing

• (Nov 16, 2019) v0.1.0 released. Fixed PyTorch interface.

• (Nov 12, 2019) v0.0.87 released.

  • Added experimental Windows support with a [known issue] regarding virtual memory allocation, which will potentially limit the scalability of Taichi programs (If you are a Windows expert, please let me know how to solve this. Thanks!). Most examples work on Windows now.
  • CUDA march autodetection;
  • Complex kernel to override autodiff.

• (Nov 4, 2019) v0.0.85 released.

  • ti.stop_grad for stopping gradients during backpropagation. [Example];
  • Compatibility improvements on Linux and OS X;
  • Minor bug fixes.

• (Nov 1, 2019) v0.0.77 released.

  • Python wheels now support OS X 10.14+;
  • LLVM is now the default backend. No need to install gcc-7 or clang-7 anymore. To use legacy backends, export TI_LLVM=0;
  • LLVM compilation speed 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)