Programming Languages/Python: Added scikit-learn and cuml acceleration

wiki/programming_language/python.md

@@ -164,6 +164,13 @@ It will create a `.venv` directory containing the `bin/activate` file.
 uv venv
 ```
 
+Packages can then be installed using the following command.
+`<package>` is the name of the package to install.
+
+```sh
+uv add <package>
+```
+
 #### venv Virtual Environments
 
 [venv](https://docs.python.org/3/library/venv.html) can be used to create a virtual environment.
@@ -217,6 +224,32 @@ This flag is to be used with care.
 
 This section addresses various different modules.
 
+### scikit-learn
+
+[scikit-learn](https://scikit-learn.org/stable/index.html) is a free and open-source library that
+provides various machine learning algorithms.
+By default, [scikit-learn](https://scikit-learn.org/stable/index.html) does only utilize the CPU.
+This, however, can be easily changed to also utilize the GPU and speed up calculation as explained
+in the [corresponding section](#run-scikit-learn-algorithms-on-the-gpu)
+
+#### Run scikit-learn Algorithms on the GPU
+
+Using [cuml](https://docs.rapids.ai/api/cuml/latest/) the GPU can be utilized with only two lines
+to add to existing code.
+As explained in the
+[cuml guide on zero code change acceleration](https://docs.rapids.ai/api/cuml/latest/cuml-accel/)
+only the following two lines have to be added to run the scikit-learn algorithms on the GPU.
+Additionally, cuml has to be installed using a [Python package manager](#using-virtual-environments)
+like [uv](#uv-virtual-environments).
+It is important that these lines are put before importing any scikit-learn packages.
+
+```py
+import cuml
+cuml.accel.install()
+```
+
+Afterwards all possible scikit-learn algorithms will run on the GPU instead of the CPU.
+
 ### PyTorch
 
 This section addresses the [PyTorch module](https://pytorch.org/).