--profile | Target hardware and resources in one argument: h200 (the registry default shape), h200:4 (4 GPUs), h200:16 (2 full nodes). Repeat it to split pools per role (the heterogeneous extension point): —profile train=h200:8 —profile rollout=h100:2. Comma-separate roles that share a pool: —profile actor,rollout=h200:8 —profile teacher=h200:8 (可重复) |
--method, -m | Method identifier <framework>/<method> (e.g. nemo-rl/grpo, verl/grpo); read from the experiment directory’s recipe.lock.json when omitted |
--set, -s | Override a hyperparameter, repeatable. Types and ranges are checked locally against the method declaration, so a typo fails immediately (可重复) |
--init-from | Start from the previous stage’s artifact (the SFT -> DPO -> GRPO pipeline) |
--model | Base model path or Hub id; required for verl/TRL |
--train-data | Training data path; required for verl/TRL. With a platform dataset declared (in the config or via —train-dataset), write the file name relative to the dataset (e.g. train.parquet) and the job side resolves it into the cache directory |
--validation-data | Validation data path; required for verl/TRL, used like —train-data |
--train-dataset | Platform dataset reference: pulled into the shared cache when the job starts, with <NAME>_DATA_DIR injected. Prefer declaring it as data.train.dataset in the experiment config; this flag is only a temporary override |
--validation-dataset | Platform dataset reference for the validation set; matches data.validation.dataset in the config |
--output-dataset | Publish what this run produces as this dataset version. The job writes files into FORGE_DATASET_OUT_DIR and the platform publishes them when the run succeeds — the job is never given a credential for the registry, and the published dataset inherits the classification of the material the run read. Used by a preprocessing run over a corpus |
--environment | Agent RL environment reference. Delivered like a corpus and for a stronger reason: a private environment is non-exportable, so the platform resolves the version into the job runtime and mounts it read-only. Run sf env ls to see the environments you can reference |
--image | Job image (a tag is enough, e.g. registry/nemo-rl-ext:v0.7.0). Required for custom; for other frameworks it overrides the default image the console configured for them |
--then | Run automatically after training succeeds (export/eval), repeatable (可重复) |
--observability-url | For an external observability recipe only; a platform recipe must not set it |
--framework-version | Only together with —upgrade-recipe; on its own it does not rewrite the lock file |
--upgrade-recipe | Upgrade this experiment’s lock to the current catalog before submitting, reusing sf recipe upgrade |
--allow-dirty | Allow uncommitted changes in the working tree (refused by default, to keep submissions traceable) |
--no-validate | Skip the pre-submission validation |
