{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "daoyuanli2816-mini-verl--reuse-existing-verl-config",
 "repo": "DaoyuanLi2816/mini-verl",
 "repo_url": "https://github.com/DaoyuanLi2816/mini-verl",
 "repo_slug": "daoyuanli2816-mini-verl",
 "title": "Run an existing verl config on one GPU",
 "page_title": "How to run an existing verl config on one GPU with Mini Verl",
 "summary": "Point miniVERL at a verl config tree, switch to GRPO and check the run with a dry run.",
 "who": "A team with a verl experiment that wants to test it on one consumer GPU without rewriting the config.",
 "problem": "A verl config written for a cluster cannot be tried locally without changing its structure and overrides.",
 "outcome": "A validated dry run of the verl config with GRPO and local GPU settings, ready for a real local run.",
 "steps": [
  "Install miniVERL with `python -m pip install \"miniverl[train,hydra]\"`.",
  "Locate the verl trainer config directory on disk, for example /path/to/verl/trainer/config.",
  "Run `miniverl run --verl-config-path /path/to/verl/trainer/config --verl-config-name ppo_trainer --dry-run algorithm.adv_estimator=grpo actor_rollout_ref.rollout.n=8 trainer.n_gpus_per_node=8` and read the output. (inferred, check the README)",
  "Read the direct-config guide to bind your data and reward function before a real run."
 ],
 "agent_prompt": "Use the miniverl repo (DaoyuanLi2816/mini-verl) to dry-run a verl config at <verl-config-path> with the ppo_trainer config name and GRPO overrides. Report whether the dry run succeeds and list any overrides that failed. Verify that no training starts during the dry run.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README says the tool supports documented verl configuration profiles, so configs outside those profiles may not map cleanly; check the compatibility matrix first.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "A single NVIDIA GPU with a CUDA-enabled PyTorch build installed from the PyTorch installer",
  "Python with pip available",
  "Enough GPU memory for the chosen model (see the single-GPU guide and hardware planning docs)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/reuse-existing-verl-config/",
 "md": "https://dedupedai.com/use-cases/daoyuanli2816-mini-verl/reuse-existing-verl-config/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/daoyuanli2816-mini-verl--reuse-existing-verl-config.json",
 "api_version": "v1"
}