38 lines
1.8 KiB
Markdown
38 lines
1.8 KiB
Markdown
# Installation
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## Installing from source
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Installing from source requires the latest CUDA Toolkit that matches the major.minor of CUDA Python installed.
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Prior to installing the CUTLASS Python interface, one may optionally set the following environment variables:
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* `CUTLASS_PATH`: the path to the cloned CUTLASS repository
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* `CUDA_INSTALL_PATH`: the path to the installation of CUDA
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If these environment variables are not set, the installation process will infer them to be the following:
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* `CUTLASS_PATH`: one directory level above the current directory (i.e., `$(pwd)/..`)
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* `CUDA_INSTALL_PATH`: the directory holding `/bin/nvcc` for the first version of `nvcc` on `$PATH` (i.e., `which nvcc | awk -F'/bin/nvcc' '{print $1}'`)
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**NOTE:** The version of `cuda-python` installed must match the CUDA version in `CUDA_INSTALL_PATH`.
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### Installing a developer-mode package
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The CUTLASS Python interface can currently be installed via:
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```bash
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python setup.py develop --user
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```
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This will allow changes to the Python interface source to be reflected when using the Python interface.
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We plan to add support for installing via `python setup.py install` in a future release.
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## Docker
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To ensure that you have all of the necessary Python modules for running the examples using the
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CUTLASS Python interface, we recommend using one of the Docker images for CUDA [11.8](../../../python/docker/Dockerfile-cuda11.8-pytorch)
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and [12.0](../../../python/docker/Dockerfile-cuda12.0-pytorch) are located in the docker directory.
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For example, to build and launch a container that uses CUDA 12.0 via an NGC PyTorch container, run:
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```bash
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docker build -t cutlass-cuda12.0:latest -f docker/Dockerfile-cuda12.0-pytorch .
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docker run --gpus all -it --rm cutlass-cuda12.0:latest
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```
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The CUTLASS Python interface has been tested with CUDA 11.8 and CUDA 12.0 on Python 3.8.10 and 3.9.7.
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