Tools

Photo Credit Introduction Parallel programming is hard, and you probably should not use any low-level API to do it in most cases (I’d argue that Python’s built-in multiprocessing package is low-level). I’ve been using Joblib’s Parallel class for tasks that are embarrassingly parallel and it works wonderfully. However, sometimes the task at hand is not simple enough for the Parallel class (e.g., you need to share something from the main process that is not pickle-able, or you want to maintain states in each child process). I’ve recently found this library — MPIRE (MultiProcessing Is Really Easy) — that significantly mitigates this problem of not having enough flexibility, while still having a high-level and user-friendly API. ...

A_Different_Perspective from Pixabay RAPIDS RAPIDS is a collection of Python libraries from NVIDIA that enables the users to do their data science pipelines entirely on GPUs. The two main components are cuDF and cuML. The cuDF library provides Pandas-like data frames, and cuML mimics scikit-learn. There’s also a cuGRAPH graph analytics library that have been introduced in the latest release (0.6 on March 28). The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. RAPIDS is incubated by NVIDIA® based on years of accelerated data science experience. RAPIDS relies on NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. ...