Updated documentation on compiling from source

docs/source/compiling.mdx

@@ -1,40 +1,49 @@
 # Compiling from Source[[compiling]]
 
-To compile from source, the CUDA Toolkit is required. Ensure `nvcc` is installed; if not, follow these steps to install it along with the CUDA Toolkit:
+## Linux
+
+To compile from source, you need the following:
+
+* The ability to compile C++ (gcc, make, headers, etc)
+* CMake (version 3.22.1 or newer)
+* Python 3.10 or newer
+* [The CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) (nvcc)
+
+On Ubuntu, install the first two with `apt-get install -y build-essential cmake`.
+
+To install the CUDA toolkit, follow the [instructions from your distro](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html).
 
-```bash
-wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
-# Use the following syntax: cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
-#   CUDA_VERSION options include 110 to 122
-#   EXPORT_TO_BASH: 0 for False, 1 for True
 
-# Example for installing CUDA 11.7 at ~/local/cuda-11.7 and exporting the path to .bashrc:
-bash install_cuda.sh 117 ~/local 1
-```
 
-For a single compile run with a specific CUDA version, set `CUDA_HOME` to point to your CUDA installation directory. For instance, to compile using CUDA 11.7 located at `~/local/cuda-11.7`, use:
+To install the package from source, then run
 
 ```
-CUDA_HOME=~/local/cuda-11.7 CUDA_VERSION=117 make cuda11x
+pip install -r requirements-dev.txt
+cmake -DCOMPUTE_BACKEND=cuda -S .
+make
+pip install .
 ```
 
-## General Compilation Steps
+If you have multiple versions of CUDA installed, or have installed it in a non-standard location, please refer to [cmake CUDA documentation](https://cliutils.gitlab.io/modern-cmake/chapters/packages/CUDA.html) for how to configure the CUDA compiler used.
 
-1. Use `CUDA_VERSION=XXX make [target]` to compile, where `[target]` includes options like `cuda92`, `cuda10x`, `cuda11x`, and others.
-2. Install with `python setup.py install`.
+## Windows
 
-Ensure `nvcc` is available in your system. If using Anaconda, determine your CUDA version with PyTorch using `conda list | grep cudatoolkit` and match it by downloading the corresponding version from the [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive).
+The following is required to install from source on Windows
 
-To install CUDA locally without administrative rights:
+* [Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/) with C++ support
+* CMake (version 3.22.1 or newer)
+* Python 3.10 or newer
+* [The CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) (nvcc)
 
-```bash
-wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
-# Follow the same syntax and example as mentioned earlier
-```
-
-The compilation process relies on the `CUDA_HOME` environment variable to locate CUDA. If `CUDA_HOME` is unset, it will attempt to infer the location from `nvcc`. If `nvcc` is not in your path, you may need to add it or set `CUDA_HOME` manually. For example, if `python -m bitsandbytes` indicates your CUDA path as `/usr/local/cuda-11.7`, you can set `CUDA_HOME` to this path.
+To install the CUDA toolkit, follow the [instructions for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html).
 
-If compilation issues arise, please report them.
+To install the package from source, then run
+```
+pip install -r requirements-dev.txt
+cmake -DCOMPUTE_BACKEND=cuda -S .
+cmake --build . --config Release
+pip install .
+```
 
 ## Compilation for Kepler Architecture