This library currently does not have a stable release. The API and implementation may change. Future changes may not be backward compatible.

TorchArrow is a torch.Tensor-like Python DataFrame library for data preprocessing in PyTorch models, with two high-level features:

• DataFrame library (like Pandas) with strong GPU or other hardware acceleration (under development) and PyTorch ecosystem integration.
• Columnar memory layout based on Apache Arrow with strong variable-width and nested data support (such as string, list, map) and Arrow ecosystem integration.

You will need Python 3.7 or later. Also, we highly recommend installing an Miniconda environment.

First, set up an environment. If you are using conda, create a conda environment:

conda create --name torcharrow python=3.7
conda activate torcharrow

The following is the corresponding torcharrow versions and supported Python versions.

Follow the instructions in this Colab notebook

Experimental nightly binary on macOS (requires macOS SDK >= 10.15) and Linux (requires glibc >= 2.17) for Python 3.7, 3.8, and 3.9 can be installed via pip wheels:

pip install --pre torcharrow -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

If you are installing from source, you will need Python 3.7 or later and a C++17 compiler.

git clone --recursive https://github.com/pytorch/torcharrow
cd torcharrow
# if you are updating an existing checkout
git submodule sync --recursive
git submodule update --init --recursive

On macOS

HomeBrew is required to install development tools on macOS.

# Install dependencies from Brew
brew install --formula ninja flex bison cmake ccache icu4c boost gflags glog libevent

# Build and install other dependencies
scripts/build_mac_dep.sh ranges_v3 fmt double_conversion folly re2

On Ubuntu (20.04 or later)

# Install dependencies from APT
apt install -y g++ cmake ccache ninja-build checkinstall \
    libssl-dev libboost-all-dev libdouble-conversion-dev libgoogle-glog-dev \
    libgflags-dev libevent-dev libre2-dev libfl-dev libbison-dev
# Build and install folly and fmt
scripts/setup-ubuntu.sh

For local development, you can build with debug mode:

DEBUG=1 python setup.py develop

And run unit tests with

python -m unittest -v

To build and install TorchArrow with release mode:

python setup.py install

TorchArrow is BSD licensed, as found in the LICENSE file.