Addresses Caroline's review comment on PR #3856: the local import of
ensure_dataset_available inside submit_to_hf was vestigial. dataset.py
does not import hf.py, so there is no circular-import risk and no extra
load cost (its heavy deps stay lazy), so make it a top-level import.
Resolve the claude[bot] review on #3856:
- Reject reward-model training under --job.target with a clear error instead
of crashing on a None policy inside build_remote_config_file.
- Support --policy.path remote runs: validate() no longer requires repo_id for
remote runs (it is auto-generated in submit_to_hf), and repo_id/push_to_hub
are now set after validate() resolves the policy.
- Narrow the bare `except Exception` in _tail_logs/_poll_until_done to
(OSError, httpx.HTTPError) so programming errors surface instead of being
silently retried or counted as job failures.
- Install the SIGINT detach handler only on the main thread.
- Generate model repo timestamps in UTC.
huggingface_hub is a core dependency, so the per-function dynamic imports
had no lazy-loading rationale. Move them to a single module-level import
and update test monkeypatch targets to lerobot.jobs.hf.* accordingly.
When --job.target names a GPU flavor, train() dispatches to lerobot.jobs.submit_to_hf
instead of training locally: it authenticates, ensures the dataset is on the Hub
(pushing a local-only one privately), serializes a pod-compatible train_config.json
(strips client-only fields, points at the model repo), submits via HfApi.run_job
with HF_TOKEN/WANDB_API_KEY secrets, then streams logs and finishes when the model
is pushed. Wires push_checkpoint_to_hub into the training loop behind
save_checkpoint_to_hub, and tags jobs/datasets/model with 'lerobot' + --job.tags.
Nicolas Rabault