c008b4aea8
* Release 3.3.0 Adds support for mixed precision GEMMs On Hopper and Ampere Adds support for < 16B aligned GEMMs on Hopper Enhancements to EVT Enhancements to Python interface Enhancements to Sub-byte type handling in CuTe Several other bug-fixes and performance improvements. * minor doc update
34 lines
1.5 KiB
Markdown
34 lines
1.5 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`: either one directory level above the current directory (i.e., `$(pwd)/..`) if installed locally or in the `source` directory of the location in which `cutlass_library` was installed
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* `CUDA_INSTALL_PATH`: the directory holding `/bin/nvcc` for the first version of `nvcc` on `$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 by navigating to the root of the CUTLASS directory and performing
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```bash
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pip install .
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```
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If you would like to be able to make changes to CULASS Python interface and have them reflected when using the interface, perform:
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```bash
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pip install -e .
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```
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## Docker
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We recommend using the CUTLASS Python interface via an [NGC PyTorch Docker container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch):
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```bash
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docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:23.08-py3
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```
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