Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

A hierachical community detection algorithm by Girvan Newman. A Girvan Newman step is defined as a couple of successive edge removes such that a new community occurs.

A PyTorch package for biomedical image processing

Very unstable library containing utilities to measure and visualize statistical properties of machine learning models.

2D partially observable dynamic world for RL experiments. PodWorld is ( ) environment for (http://…/the-book-2nd.html ) experimentations. PodWorld is specifically designed to be partially observable and dynamic (hence abbreviation P.O.D.). We emphasize these two attributes to force agents learn spatial as well as temporal representations that must go beyond simple memorization. In addition, all entities in PodWorld must obey laws of physics allowing for long tail of emergent observations that may not appear in games designed with arbitrary hand crafted rules for human entertainment. PodWorld is designed to be highly customizable as well as hackable with fairly simple and minimal code base. PodWorld is designed to be fast (>500 FPS on usual laptops) without needing GPU and run cross platform in headless mode or with render.

Splits a dataset (in Pandas dataframe format) to train/test sets.

A simple scheduler for running commands on multiple GPUs. A simple scheduler to run your commands on individual GPUs. Following the (https://…/KISS_principle ), this script simply accepts commands via `stdin` and executes them on a specific GPU by setting the `CUDA_VISIBLE_DEVICES` variable.

Simple S3 uploads

Use the power of hypothesis property based testing in PySpark tests. Data heavy tests benefit from Hypothesis to generate your data and desinging your tests. Sparkle-hypothesis makes it easy to use Hypothesis strategies to generate dataframes.

A systematic strategy toolkit.

Automated Modeling in Financial Domain. TeaML is a simple and design friendly automatic modeling learning framework. It can automatically model from beginning to end, and in the end, it will also help you output a model report about the model.