flyscrape VS cuml

Shameless plug:

Flyscrape[0] lets you eliminate a lot of boilerplate code that is otherwise necessary when building a scraper from scratch, while still giving you the flexibility to extract data that perfectly fit your needs.

It comes as a single binary executable and runs small JavaScript files without having to deal with npm or node.

You can have a collection of small and isolated scraping scripts, rather than full on node projects.

[0]: https://github.com/philippta/flyscrape

Thanks for sharing! Just a small nit: the links at the bottom of this page are broken [1].

[1]: https://github.com/philippta/flyscrape/blob/master/docs/read...

sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.

As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).

CuML

Did they implement the clustering algorithm themselves? cuML is a GPU-accelerated scikit-learn-like package that covers many of the common ML algorithms.

https://github.com/rapidsai/cuml

> cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.

Cython code

Take a look at RAPIDS CUML https://github.com/rapidsai/cuml. It's useful for most common ML algorithms. Feel free to create Github issues for feature requests & bugs.