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Source channel @githubtrending · Post #15340 · Dec 17

#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym

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djangoproject

@djangoproject · Post #270 · 02/26/2017, 08:08 AM

https://www.obeythetestinggoat.com/testing-async-asyncio-and-performance.html #Testing, #async, #asyncio, and #performance Sun 27 December 2015 By Harry I recently did some experimenting with asyncio, and wanted to report back on how I got on with writing tests for it. While I was at it I was also able to compare its performance with a couple of other approaches to #mutlitasking in Python, namely #threads and #gevent, so I'll report on that here too. (tl;dr: it's much of a muchness).