This is the Windows app named Cosmos-RL whose latest release can be downloaded as v0.4.5sourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Cosmos-RL with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS:
Cosmos-RL
DESCRIPTION:
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. It is built with compatibility in mind, supporting popular model families such as LLaMA, Qwen, and diffusion-based world models, as well as integration with Hugging Face ecosystems. cosmos-rl also includes support for advanced RL algorithms, low-precision training, and fault-tolerant execution, making it suitable for large-scale production workloads.
Features
- Distributed reinforcement learning with asynchronous architecture
- Support for multiple parallelism strategies including tensor and pipeline
- Compatibility with LLMs, vision-language models, and diffusion models
- Low-precision training support such as FP8 and FP4
- Fault-tolerant and elastic distributed execution
- Integration with PyTorch and Hugging Face ecosystems
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/cosmos-rl.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.