# torcharrow

**Repository Path**: mirrors_pytorch/torcharrow

## Basic Information

- **Project Name**: torcharrow
- **Description**: High performance model preprocessing library on PyTorch
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2022-06-19
- **Last Updated**: 2025-10-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TorchArrow: a data processing library for PyTorch

**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](https://github.com/pytorch/pytorch).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](https://arrow.apache.org/docs/format/Columnar.html#physical-memory-layout) with strong variable-width and nested data support (such as string, list, map) and Arrow ecosystem integration.

## Installation

You will need Python 3.7 or later. Also, we highly recommend installing an [Miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) 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
```

### Version Compatibility

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

| `torch`            | `torcharrow`        | `python`          |
| ------------------ | ------------------ | ----------------- |
| `main` / `nightly` | `main` / `nightly` | `>=3.7`, `<=3.10` |
| `1.13.0`           | `0.2.0`            | `>=3.7`, `<=3.10` |


### Colab

Follow the instructions [in this Colab notebook](https://colab.research.google.com/drive/1S0ldwN7qNM37E4WZnnAEnzn1DWnAQ6Vt)

### Nightly Binaries

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
```

### From Source

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

#### Get the TorchArrow Source
```bash
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
```

#### Install Dependencies

On macOS

[HomeBrew](https://brew.sh/) is required to install development tools on macOS.

```bash
# 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)
```bash
# 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
```

#### Install TorchArrow
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
```

## License

TorchArrow is BSD licensed, as found in the [LICENSE](LICENSE) file.