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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
Search: #interpreter
@djangoproject · Post #156 · 09/06/2016, 01:43 AM
https://wiki.python.org/moin/GlobalInterpreterLock In #CPython, the #global#interpreter lock, or #GIL, is a mutex that prevents multiple native #threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.)
@djangoproject · Post #159 · 09/12/2016, 05:37 PM
https://docs.python.org/3/library/atexit.html The #atexit module defines #functions to #register and #unregister cleanup functions. Functions thus registered are automatically executed upon normal interpreter termination. atexit runs these functions in the reverse order in which they were registered; if you register A, B, and C, at #interpreter#termination time they will be run in the order C, B, A.