What is cuML similar to in its function?

cuML is a GPU-accelerated machine learning library designed to bring the functionalities of ScikitLearn to the GPU environment, allowing for faster computations and scaling on larger datasets. It is specifically geared towards processing data in a way that mirrors the API and functionality of ScikitLearn, thus making the transition for users who are familiar with ScikitLearn easier and more efficient.

This similarity means that users can apply machine learning techniques like regression, clustering, and classification using the same methods and formatting as they would in ScikitLearn, but with significantly enhanced performance due to the GPU acceleration inherent in cuML. This is particularly advantageous for data scientists and machine learning engineers who are looking to harness the benefits of parallel processing without having to learn a completely new set of tools or methods.

In contrast, while the other options involve important NVIDIA technologies relevant to parallel computing, data management, or graph analytics, they do not serve the same purpose as cuML in the machine learning space. The CUDA toolkit is a foundation for GPU computing rather than a direct library for machine learning. cuDF is a GPU DataFrame library for data manipulation, and cuGraph provides graph analytics capabilities. Therefore, the correct answer highlights cuML's direct correlation with ScikitLearn, emphasizing its role