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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 #119 · 08/10/2016, 02:37 PM

18.5.8. Queues Queues: #Queue #PriorityQueue #LifoQueue #asyncio queue #API was designed to be close to classes of the queue module (Queue, PriorityQueue, LifoQueue), but it has no timeout parameter. The asyncio.wait_for() function can be used to cancel a task after a timeout. https://docs.python.org/3/library/asyncio-queue.html