version: "1.0.1" name: launch-run description: Review, bundle, upload, and explicitly launch a Castform GPU training run from the project script.

Launch a run

Use this only after verify-environment reports a believable green baseline. Launching spends GPU credits. The workflow lives in main.py, not a CLI launch command:

uv run python main.py launch

Do not pass --yes unless the user has already explicitly authorized the cost. Never launch after validate_environment reports a static or runtime sampling or history-contract error. Review output-cap warnings (max_tokens or max_completion_tokens) and confirm any effective clamp is intentional.

Required ordering

Accept user configuration as explicit main.py arguments, normalize it once in _constructor_args(args), and reuse that dictionary for local construction and dump_bundle. Avoid ambient os.environ reads in environments, tools, rewards, and harness configuration.

Read the script and confirm that the launch action does all of the following in order:

1. builds one Bundle with dump_bundle;
2. passes that exact object to upload_assets(bundle=bundle, ...);
3. validates the uploaded assets (locally and in the hosted sandbox) and stops

on failure;

1. asks the human to confirm a credit-spending GPU launch;
2. passes the same uploaded paths to TrainerClient.launch_training_run — the

run trains on precisely what was validated.

The upload helper must not silently rebundle the environment, and launch must not re-upload.

Dataset upload is explicit and optional. Supply train_dataset and/or eval_dataset only for splits Castform should upload. Omit them for data resolved by the environment at runtime (for example Harbor- or Git-managed data). Do not use an empty list as an omission sentinel: it uploads an empty JSONL file.

Dependencies

RUNTIME_DEPENDENCIES is explicit and authoritative for the remote rollout runtime:

bundle = dump_bundle(
    CustomEnv,
    constructor_args=constructor_args,
    pip_dependencies=RUNTIME_DEPENDENCIES,
)

List every external package imported while the environment, tools or rewards run. Do not copy the whole project dependency list automatically: data-preparation and development packages may not belong in the rollout image. benchmax captures local modules under the environment project automatically. Source from another project must be explicit: use local_modules= to capture it, or list its installed distribution in pip_dependencies to keep it as a remote reference.

For Harbor, add the selected provider extra explicitly, such as harbor[modal]>=0.18,<0.19 or harbor[daytona]>=0.18,<0.19.

Launch configuration

Review LAUNCH_CONFIG in source. In particular:

• max_context_tokens is the whole-rollout prompt-plus-response token budget;
• keep trainer turn/tool limits compatible with the environment's own limits;
• start with modest epochs and judge the eval curve, not only train reward;
• use TrainerClient.list_launch_args() when you need the live accepted schema

instead of guessing an argument name.

<!-- rag:start --> For search environments, budget for repeated tool output across turns. Confirm the rollout bundle includes the runtime search client but not large local corpus- preparation dependencies unless the environment imports them. <!-- rag:end -->

Credentials

Use InjectedAuth for model and judge calls through the Castform LLM endpoint so the hosted runtime supplies the current Castform credential. User-managed external endpoints use explicit StaticBearerAuth. Harbor sandbox credentials are currently explicit constructor inputs. Review static credentials before bundling and limit their scope.

Handoff

Record the run ID printed by the script, then load view-progress. If upload or launch fails, preserve the error, correct the script or credentials, and rerun the smallest failed stage. Never bypass a failed validation gate.