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final build
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.travis.yml

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os:
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- linux
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- osx
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# - windows
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- windows
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env:
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global:
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- CUDA_HOME=/usr/local/cuda
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jobs:
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cpu
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu92
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu100
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cpu
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu92
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu100
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu101
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cpu
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu92
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu100
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cpu
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu92
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu100
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu101
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cpu
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu92
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu100
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# - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu101
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu92
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu100
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- TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu101
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jobs:
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exclude: # Exclude *all* macOS CUDA jobs and Windows CUDA 9.2/10.0 jobs.

README.md

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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.
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## Installation
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# Installation
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### Binaries
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We provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](http://pytorch-scatter.s3-website.eu-central-1.amazonaws.com/whl).
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```
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pip install torch-scatter==latest+${CUDA} -f http://pytorch-scatter.s3-website.eu-central-1.amazonaws.com/whl/torch-1.4.0.html --trusted-host pytorch-scatter.s3-website.eu-central-1.amazonaws.com
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```
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where `${CUDA}` should be replaced by either `cpu`, `cu92`, `cu100` or `cu101` depending on your PyTorch installation.
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| | `cpu` | `cu92` | `cu100` | `cu101` |
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|-------------|-------|--------|---------|---------|
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| **Linux** |||||
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| **Windows** |||||
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| **macOS** |||||
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### From source
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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.*:
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>>> /usr/local/cuda/include:...
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```
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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`
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Then run:
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```
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export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"
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python setup.py install
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```
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### Windows
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If you are installing this on Windows specifically, **you will need to point the setup to your Visual Studio installation** for some neccessary libraries and header files.
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To do this, add the include and library paths of your installation to the path lists in setup.py as described in the respective comments in the code.
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When running in a docker container without nvidia driver, PyTorch needs to evaluate the compute capabilities and may fail.
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In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*:
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If you are running into any installation problems, please create an [issue](https://github.com/rusty1s/pytorch_scatter/issues).
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Be sure to import `torch` first before using this package to resolve symbols the dynamic linker must see.
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```
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export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"
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```
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## Example
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test/utils.py

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devices = [torch.device('cpu')]
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if torch.cuda.is_available():
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devices += [torch.device('cuda:{}'.format(torch.cuda.current_device()))]
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devices += [torch.device(f'cuda:{torch.cuda.current_device()}')]
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def tensor(x, dtype, device):

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