> ## Documentation Index > Fetch the complete documentation index at: https://starforge.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # sf submit > Complete parameter reference for submitting training jobs ```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} sf submit [options] ``` Config and hyperparameters are validated automatically before submission; see [Submitting Training](/en/guides/submit) for the workflow walkthrough. ## Resources | Option | Description | | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--profile [ROLE=]name[:total-gpus]` | Target hardware and resources in a single argument: `h200` (registry default shape), `h200:4`, `h200:16` (2 full nodes). Repeatable to split pools by role: `--profile train=h200:8 --profile rollout=h100:2` | The project name comes from `starforge.yaml` at the repository root and is attached automatically. Do not pass `--project`. ## Method and Hyperparameters | Option | Description | | --------------------- | ----------------------------------------------------------------------------------------------------------- | | `-m, --method` | Method id; if omitted, read from the experiment's `recipe.lock.json` | | `-s, --set KEY=VALUE` | Override hyperparameters, repeatable; types and ranges are validated locally against the method declaration | | `--upgrade-recipe` | Upgrade the experiment lock to the current catalog before submitting | | `--framework-version` | Switch framework version together with `--upgrade-recipe` | ## Model and Data | Option | Description | | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | `--model` | Base model path or Hub id; **required for verl / TRL** | | `--train-data` / `--validation-data` | Data paths; required for verl / TRL. When referencing a platform dataset, use the relative file name within the dataset | | `--train-dataset` / `--validation-dataset` | Platform dataset reference `/[@version]`; recommended to put in the config, with the CLI used only for temporary overrides | | `--init-from run//checkpoint[@step=N]` | Start training from a previous stage's artifact (pipelines) | ## Framework and Observability (custom only) | Option | Description | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | `--image` | Override the OCI image for this job; optional for first-class frameworks, required for custom. Tags work; prefer a digest in production | | `--observability-url` | External observability recipes only; forbidden for platform recipes | ## Flow Control | Option | Description | | --------------- | --------------------------------------------------------------------------------------------- | | `--then ACTION` | Run automatically after successful training (`export` / `eval`), repeatable | | `--allow-dirty` | Allow uncommitted changes in the working tree (rejected by default to guarantee traceability) | | `--no-validate` | Skip pre-submission validation | ## Examples ```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} # NeMo-RL GRPO, 8 GPUs sf submit my-grpo --profile h200:8 # verl 0.9 + platform dataset + temporary hyperparameter override sf submit my-verl --profile h200:4 \ --model Qwen/Qwen3.5-9B \ --train-dataset alice/gsm8k-zh@v2 --train-data train.parquet \ -s actor_rollout_ref.actor.optim.lr=1e-6 # Heterogeneous multi-pool + automatic export after training sf submit my-grpo --profile train=h200:8 --profile rollout=h100:2 --then export # Custom sf submit my-custom --profile h100:1 \ --image registry.example.com/train@sha256:abc… # Catalog default is still external observability: add --observability-url # Curves in the console: see Custom images ``` ## `sf submit` Submit a training job ```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} sf submit [options] ``` | Argument | Description | | -------- | -------------------------------------- | | `EXP` | Experiment name or path (**required**) | | Option | Description | | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--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 (repeatable) | | `--method`, `-m` | Method identifier \/\ (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 (repeatable) | | `--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 \\_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 (repeatable) | | `--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 |