PKG-INFO

Metadata-Version: 2.1
Name: torch-scatter
Version: 2.1.2
Summary: PyTorch Extension Library of Optimized Scatter Operations
Home-page: https://github.com/rusty1s/pytorch_scatter
Author: Matthias Fey
Author-email: matthias.fey@tu-dortmund.de
License: UNKNOWN
Download-URL: https://github.com/rusty1s/pytorch_scatter/archive/2.1.2.tar.gz
Description: [pypi-image]: https://badge.fury.io/py/torch-scatter.svg
        [pypi-url]: https://pypi.python.org/pypi/torch-scatter
        [testing-image]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/testing.yml/badge.svg
        [testing-url]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/testing.yml
        [linting-image]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/linting.yml/badge.svg
        [linting-url]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/linting.yml
        [docs-image]: https://readthedocs.org/projects/pytorch-scatter/badge/?version=latest
        [docs-url]: https://pytorch-scatter.readthedocs.io/en/latest/?badge=latest
        [coverage-image]: https://codecov.io/gh/rusty1s/pytorch_scatter/branch/master/graph/badge.svg
        [coverage-url]: https://codecov.io/github/rusty1s/pytorch_scatter?branch=master
        
        # PyTorch Scatter
        
        [![PyPI Version][pypi-image]][pypi-url]
        [![Testing Status][testing-image]][testing-url]
        [![Linting Status][linting-image]][linting-url]
        [![Docs Status][docs-image]][docs-url]
        [![Code Coverage][coverage-image]][coverage-url]
        
        <p align="center">
          <img width="50%" src="https://raw.githubusercontent.com/rusty1s/pytorch_scatter/master/docs/source/_figures/add.svg?sanitize=true" />
        </p>
        
        --------------------------------------------------------------------------------
        
        **[Documentation](https://pytorch-scatter.readthedocs.io)**
        
        This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.
        Scatter and segment operations can be roughly described as reduce operations based on a given "group-index" tensor.
        Segment operations require the "group-index" tensor to be sorted, whereas scatter operations are not subject to these requirements.
        
        The package consists of the following operations with reduction types `"sum"|"mean"|"min"|"max"`:
        
        * [**scatter**](https://pytorch-scatter.readthedocs.io/en/latest/functions/scatter.html) based on arbitrary indices
        * [**segment_coo**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_coo.html) based on sorted indices
        * [**segment_csr**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_csr.html) based on compressed indices via pointers
        
        In addition, we provide the following **composite functions** which make use of `scatter_*` operations under the hood: `scatter_std`, `scatter_logsumexp`, `scatter_softmax` and `scatter_log_softmax`.
        
        All included operations are broadcastable, work on varying data types, are implemented both for CPU and GPU with corresponding backward implementations, and are fully traceable.
        
        ## Installation
        
        ### Anaconda
        
        **Update:** You can now install `pytorch-scatter` via [Anaconda](https://anaconda.org/pyg/pytorch-scatter) for all major OS/PyTorch/CUDA combinations 🤗
        Given that you have [`pytorch >= 1.8.0` installed](https://pytorch.org/get-started/locally/), simply run
        
        ```
        conda install pytorch-scatter -c pyg
        ```
        
        ### Binaries
        
        We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
        
        #### PyTorch 2.2
        
        To install the binaries for PyTorch 2.2.0, simply run
        
        ```
        pip install torch-scatter -f https://data.pyg.org/whl/torch-2.2.0+${CUDA}.html
        ```
        
        where `${CUDA}` should be replaced by either `cpu`, `cu118`, or `cu121` depending on your PyTorch installation.
        
        |             | `cpu` | `cu118` | `cu121` |
        |-------------|-------|---------|---------|
        | **Linux**   | ✅    | ✅      | ✅      |
        | **Windows** | ✅    | ✅      | ✅      |
        | **macOS**   | ✅    |         |         |
        
        #### PyTorch 2.1
        
        To install the binaries for PyTorch 2.1.0, simply run
        
        ```
        pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.0+${CUDA}.html
        ```
        
        where `${CUDA}` should be replaced by either `cpu`, `cu118`, or `cu121` depending on your PyTorch installation.
        
        |             | `cpu` | `cu118` | `cu121` |
        |-------------|-------|---------|---------|
        | **Linux**   | ✅    | ✅      | ✅      |
        | **Windows** | ✅    | ✅      | ✅      |
        | **macOS**   | ✅    |         |         |
        
        **Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1, PyTorch 1.9.0, PyTorch 1.10.0/1.10.1/1.10.2, PyTorch 1.11.0, PyTorch 1.12.0/1.12.1, PyTorch 1.13.0/1.13.1, and PyTorch 2.0.0 (following the same procedure).
        For older versions, you need to explicitly specify the latest supported version number or install via `pip install --no-index` in order to prevent a manual installation from source.
        You can look up the latest supported version number [here](https://data.pyg.org/whl).
        
        ### From source
        
        Ensure that at least PyTorch 1.4.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
        
        ```
        $ python -c "import torch; print(torch.__version__)"
        >>> 1.4.0
        
        $ echo $PATH
        >>> /usr/local/cuda/bin:...
        
        $ echo $CPATH
        >>> /usr/local/cuda/include:...
        ```
        
        Then run:
        
        ```
        pip install torch-scatter
        ```
        
        When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail.
        In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*:
        
        ```
        export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"
        ```
        
        ## Example
        
        ```py
        import torch
        from torch_scatter import scatter_max
        
        src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
        index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
        
        out, argmax = scatter_max(src, index, dim=-1)
        ```
        
        ```
        print(out)
        tensor([[0, 0, 4, 3, 2, 0],
                [2, 4, 3, 0, 0, 0]])
        
        print(argmax)
        tensor([[5, 5, 3, 4, 0, 1]
                [1, 4, 3, 5, 5, 5]])
        ```
        
        ## Running tests
        
        ```
        pytest
        ```
        
        ## C++ API
        
        `torch-scatter` also offers a C++ API that contains C++ equivalent of python models.
        For this, we need to add `TorchLib` to the `-DCMAKE_PREFIX_PATH` (*e.g.*, it may exists in `{CONDA}/lib/python{X.X}/site-packages/torch` if installed via `conda`):
        
        ```
        mkdir build
        cd build
        # Add -DWITH_CUDA=on support for CUDA support
        cmake -DCMAKE_PREFIX_PATH="..." ..
        make
        make install
        ```
        
Keywords: pytorch,scatter,segment,gather
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: test