slm-lab-benchmark

To run deep reinforcement learning benchmarks, you can launch and manage automated training runs on remote GPUs. This process initiates the training, monitors job completion, and extracts final performance metrics like total reward.

Monitoring dstack jobs for PyTorch RL training involves tracking job progress and extracting key metrics automatically. You can observe the status of remote training runs and capture the final scores upon completion.

Yes, you need huggingface-cli to update benchmark documentation. It manages data and uploads artifacts, allowing you to automatically update benchmark tables with new scores and Hugging Face dataset links.

You can automate updating benchmark tables and plots after a training run. The workflow extracts performance metrics and automatically updates documentation with new scores, Hugging Face links, and generated performance plots.

Yes, this benchmarking workflow supports PPO algorithms on environments like Pong-v5. When asked to run PPO on Pong-v5, it initiates training, monitors completion, extracts the final score, and updates the benchmark documentation.