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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: #processes
@amazingeo · Post #647 · 02/25/2026, 08:31 PM
🌍 Submarine hydrothermal vents on the ocean floor release superheated water and minerals, fueling unique ecosystems powered by chemical energy instead of sunlight. ✨ #processes⚡#ocean⚡#ecosystems⚡#geography⚡#nature⚡#earth 👉subscribe Amazing Geography 👉more Channels
@amazingeo · Post #39 · 08/13/2025, 12:12 AM
🌍 Earth's crust is in constant motion due to convection currents—slow, swirling movement of hot rock deep below the surface. This drives plate movement, causing earthquakes and forming new land. ✨ #processes⚡#plate⚡#tectonics⚡#geology⚡#geography⚡#nature⚡#earth 👉subscribe Amazing Geography🌍
@djangoproject · Post #430 · 09/02/2017, 03:23 AM
https://docs.python.org/3/library/subprocess.html The #subprocess module allows you to spawn new #processes, connect to their input/output/error pipes, and obtain their return codes. This module intends to replace several older #modules and #functions. #python
@djangoproject · Post #290 · 04/04/2017, 09:36 PM
https://pymotw.com/3/asyncio/executors.html Combining Coroutines with Threads and Processes A lot of existing libraries are not ready to be used with #asyncio natively. They may block, or depend on concurrency features not available through the module. It is still possible to use those libraries in an application based on asyncio by using an #executor from #concurrent.futures to run the code either in a separate thread or a separate process. #Threads The #run_in_executor() method of the event loop takes an executor instance, a regular callable to invoke, and any arguments to be passed to the callable. It returns a Future that can be used to wait for the function to finish its work and return something. If no executor is passed in, a #ThreadPoolExecutor is created. This example explicitly creates an executor to limit the number of worker threads it will have available. #Processes A ProcessPoolExecutor works in much the same way, creating a set of worker #processes instead of threads. Using separate processes requires more system resources, but for computationally-intensive operations it can make sense to run a separate task on each CPU core. #learn