Remove offline training, refactor train.py and logging/checkpointing (#670)

Co-authored-by: Remi <remi.cadene@huggingface.co>

tests/test_control_robot.py

@@ -29,7 +29,6 @@ from unittest.mock import patch
 
 import pytest
 
-from lerobot.common.logger import Logger
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.robot_devices.control_configs import (
@@ -38,9 +37,7 @@ from lerobot.common.robot_devices.control_configs import (
     ReplayControlConfig,
     TeleoperateControlConfig,
 )
-from lerobot.configs.default import DatasetConfig
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.train import TrainPipelineConfig
 from lerobot.scripts.control_robot import calibrate, record, replay, teleoperate
 from tests.test_robots import make_robot
 from tests.utils import DEVICE, TEST_ROBOT_TYPES, mock_calibration_dir, require_robot
@@ -185,20 +182,8 @@ def test_record_and_replay_and_policy(tmpdir, request, robot_type, mock):
 
     out_dir = tmpdir / "logger"
 
-    ds_cfg = DatasetConfig(repo_id, local_files_only=True)
-    train_cfg = TrainPipelineConfig(
-        dataset=ds_cfg,
-        policy=policy_cfg,
-        output_dir=out_dir,
-        device=DEVICE,
-    )
-    logger = Logger(train_cfg)
-    logger.save_checkpoint(
-        train_step=0,
-        identifier=0,
-        policy=policy,
-    )
     pretrained_policy_path = out_dir / "checkpoints/last/pretrained_model"
+    policy.save_pretrained(pretrained_policy_path)
 
     # In `examples/9_use_aloha.md`, we advise using `num_image_writer_processes=1`
     # during inference, to reach constent fps, so we test this here.