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@@ -36,7 +36,25 @@ In addition, we provide the following **composite functions** which make use of
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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).
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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@@ -51,19 +69,18 @@ $ echo $CPATH
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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`
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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