feat(train): add JobConfig group, save_checkpoint_to_hub flag, Hub checkpoint helper

Introduce a JobConfig draccus group on TrainPipelineConfig (--job.target/image/
timeout/detach/tags) whose is_remote property gates remote dispatch, plus a
save_checkpoint_to_hub flag and validation. Add push_checkpoint_to_hub(), which
uploads a saved checkpoint directory to the model repo under checkpoints/<step>/
and creates the repo idempotently (private propagates from policy.private).

src/lerobot/common/train_utils.py

@@ -283,3 +283,28 @@ def load_fsdp_optimizer_state(model, optimizer, checkpoint_dir: Path) -> None:
     with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
         sharded_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=full_osd)
     optimizer.load_state_dict(sharded_osd)
+
+
+def push_checkpoint_to_hub(
+    checkpoint_dir: Path,
+    repo_id: str,
+    *,
+    private: bool | None = None,
+) -> None:
+    """Upload a saved checkpoint directory to the Hub under checkpoints/<name>/.
+
+    Called once per save step when save_checkpoint_to_hub is enabled, so a
+    timed-out or crashed run still leaves recoverable checkpoints on the Hub.
+    The model repo is created idempotently.
+    """
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    api.create_repo(repo_id=repo_id, repo_type="model", private=private, exist_ok=True)
+    api.upload_folder(
+        folder_path=str(checkpoint_dir),
+        repo_id=repo_id,
+        repo_type="model",
+        path_in_repo=f"checkpoints/{checkpoint_dir.name}",
+        commit_message=f"checkpoint {checkpoint_dir.name}",
+    )

src/lerobot/configs/default.py

@@ -123,3 +123,31 @@ class PeftConfig:
     # If None, the PEFT library defaults to alpha=8, which may dampen high-rank adapters.
     # Common values are r (alpha == rank) or 2*r.
     lora_alpha: int | None = None
+
+
+@dataclass
+class JobConfig:
+    # Where training runs. None (omitted) or "local" runs on this machine.
+    # Any other value is an HF Jobs flavor and submits the run to HF Jobs.
+    # List available flavors + pricing with `hf jobs hardware` command.
+    target: str | None = None
+    # Runtime image for the remote job (ignored for local runs).
+    image: str = "huggingface/lerobot-gpu:latest"
+    # Max wall-clock for the remote job as an HF Jobs duration string (e.g. "2h").
+    # None (default) imposes no timeout — the job runs until the command finishes.
+    timeout: str | None = None
+    # Submit and exit instead of streaming the job logs in the foreground.
+    detach: bool = False
+    # Extra tags attached to the HF job and to any dataset this run pushes to the
+    # Hub. A "lerobot" tag is always added; e.g. --job.tags '["lelab"]' adds more.
+    tags: list[str] = field(default_factory=list)
+
+    @staticmethod
+    def is_remote_target(target: str | None) -> bool:
+        """True when `target` names an HF Jobs flavor rather than a local run."""
+        return target not in (None, "local")
+
+    @property
+    def is_remote(self) -> bool:
+        """True when training should run on HF Jobs rather than this machine."""
+        return self.is_remote_target(self.target)

src/lerobot/configs/train.py

@@ -30,7 +30,7 @@ from lerobot.utils.hub import HubMixin
 from lerobot.utils.sample_weighting import SampleWeightingConfig
 
 from . import parser
-from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .rewards import RewardModelConfig
 
@@ -113,6 +113,13 @@ class TrainPipelineConfig(HubMixin):
     wandb: WandBConfig = field(default_factory=WandBConfig)
     peft: PeftConfig | None = None
 
+    # Where to run training (local default, or an HF Jobs flavor). See JobConfig.
+    job: JobConfig = field(default_factory=JobConfig)
+    # Push each saved checkpoint to the Hub (policy.repo_id) as it is written, not
+    # just the final model (useful to monitor progress mid-run). Optional; the
+    # final model is pushed regardless. Works the same locally and remotely.
+    save_checkpoint_to_hub: bool = False
+
     # Sample weighting configuration (e.g., for RA-BC training)
     sample_weighting: SampleWeightingConfig | None = None
 
@@ -211,6 +218,9 @@ class TrainPipelineConfig(HubMixin):
         if hasattr(active_cfg, "push_to_hub") and active_cfg.push_to_hub and not active_cfg.repo_id:
             raise ValueError("'repo_id' argument missing. Please specify it to push the model to the hub.")
 
+        if self.save_checkpoint_to_hub and not (self.policy is not None and self.policy.repo_id):
+            raise ValueError("save_checkpoint_to_hub requires --policy.repo_id.")
+
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
         """Keys for draccus pretrained-path loading."""

tests/jobs/conftest.py

@@ -0,0 +1,17 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Importing concrete policy configs registers their draccus `--policy.type`
+# choices (e.g. "act") so tests can parse them.
+from lerobot.policies.act.configuration_act import ACTConfig  # noqa: F401

tests/jobs/test_job_config.py

@@ -0,0 +1,64 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import draccus
+import pytest
+
+from lerobot.configs.default import JobConfig
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def test_jobconfig_defaults_are_local():
+    cfg = JobConfig()
+    assert cfg.target is None
+    assert cfg.is_remote is False
+    assert cfg.image == "huggingface/lerobot-gpu:latest"
+    assert cfg.timeout is None
+    assert cfg.detach is False
+
+
+def test_jobconfig_local_string_is_not_remote():
+    assert JobConfig(target="local").is_remote is False
+
+
+def test_jobconfig_flavor_is_remote():
+    assert JobConfig(target="a10g-small").is_remote is True
+
+
+def test_train_config_parses_job_target():
+    parsed = draccus.parse(
+        TrainPipelineConfig,
+        args=["--dataset.repo_id", "u/d", "--policy.type", "act", "--job.target", "a10g-small"],
+    )
+    assert parsed.job.target == "a10g-small"
+    assert parsed.job.is_remote is True
+    assert parsed.save_checkpoint_to_hub is False
+
+
+def test_save_checkpoint_to_hub_requires_repo_id():
+    cfg = draccus.parse(
+        TrainPipelineConfig,
+        args=[
+            "--dataset.repo_id",
+            "u/d",
+            "--policy.type",
+            "act",
+            "--policy.push_to_hub",
+            "false",
+            "--save_checkpoint_to_hub",
+            "true",
+        ],
+    )
+    with pytest.raises(ValueError, match="requires --policy.repo_id"):
+        cfg.validate()

tests/utils/test_train_utils.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 
 from pathlib import Path
-from unittest.mock import Mock, patch
+from unittest.mock import MagicMock, Mock, patch
 
 from lerobot.common.train_utils import (
     get_step_checkpoint_dir,
@@ -24,6 +24,7 @@ from lerobot.common.train_utils import (
     load_training_num_processes,
     load_training_state,
     load_training_step,
+    push_checkpoint_to_hub,
     save_checkpoint,
     save_training_state,
     save_training_step,
@@ -151,3 +152,35 @@ def test_load_training_state_skip_optimizer(tmp_path, optimizer, scheduler):
     assert loaded_step == 10
     assert loaded_optimizer is optimizer
     assert loaded_scheduler is scheduler
+
+
+def test_push_checkpoint_to_hub_creates_repo_and_uploads(tmp_path, monkeypatch):
+    import huggingface_hub
+
+    ckpt = tmp_path / "010000"
+    (ckpt / "pretrained_model").mkdir(parents=True)
+    api = MagicMock()
+    monkeypatch.setattr(huggingface_hub, "HfApi", lambda *a, **k: api)
+    push_checkpoint_to_hub(ckpt, "user/run", private=True)
+    api.create_repo.assert_called_once()
+    assert api.create_repo.call_args.kwargs["private"] is True
+    assert api.create_repo.call_args.kwargs["repo_type"] == "model"
+    api.upload_folder.assert_called_once()
+    kwargs = api.upload_folder.call_args.kwargs
+    assert kwargs["repo_id"] == "user/run"
+    assert kwargs["repo_type"] == "model"
+    assert kwargs["path_in_repo"] == "checkpoints/010000"
+    assert kwargs["folder_path"] == str(ckpt)
+    assert kwargs["commit_message"] == "checkpoint 010000"
+
+
+def test_push_checkpoint_to_hub_defaults_to_hub_default_visibility(tmp_path, monkeypatch):
+    import huggingface_hub
+
+    ckpt = tmp_path / "010000"
+    (ckpt / "pretrained_model").mkdir(parents=True)
+    api = MagicMock()
+    monkeypatch.setattr(huggingface_hub, "HfApi", lambda *a, **k: api)
+    push_checkpoint_to_hub(ckpt, "user/run")
+    api.create_repo.assert_called_once()
+    assert api.create_repo.call_args.kwargs["private"] is None