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

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

tests/test_utils.py

@@ -1,8 +1,3 @@
-import random
-from typing import Callable
-
-import numpy as np
-import pytest
 import torch
 from datasets import Dataset
 
@@ -10,50 +5,6 @@ from lerobot.common.datasets.push_dataset_to_hub.utils import calculate_episode_
 from lerobot.common.datasets.utils import (
     hf_transform_to_torch,
 )
-from lerobot.common.utils.utils import (
-    get_global_random_state,
-    seeded_context,
-    set_global_random_state,
-    set_global_seed,
-)
-
-# Random generation functions for testing the seeding and random state get/set.
-rand_fns = [
-    random.random,
-    np.random.random,
-    lambda: torch.rand(1).item(),
-]
-if torch.cuda.is_available():
-    rand_fns.append(lambda: torch.rand(1, device="cuda"))
-
-
-@pytest.mark.parametrize("rand_fn", rand_fns)
-def test_seeding(rand_fn: Callable[[], int]):
-    set_global_seed(0)
-    a = rand_fn()
-    with seeded_context(1337):
-        c = rand_fn()
-    b = rand_fn()
-    set_global_seed(0)
-    a_ = rand_fn()
-    b_ = rand_fn()
-    # Check that `set_global_seed` lets us reproduce a and b.
-    assert a_ == a
-    # Additionally, check that the `seeded_context` didn't interrupt the global RNG.
-    assert b_ == b
-    set_global_seed(1337)
-    c_ = rand_fn()
-    # Check that `seeded_context` and `global_seed` give the same reproducibility.
-    assert c_ == c
-
-
-def test_get_set_random_state():
-    """Check that getting the random state, then setting it results in the same random number generation."""
-    random_state_dict = get_global_random_state()
-    rand_numbers = [rand_fn() for rand_fn in rand_fns]
-    set_global_random_state(random_state_dict)
-    rand_numbers_ = [rand_fn() for rand_fn in rand_fns]
-    assert rand_numbers_ == rand_numbers
 
 
 def test_calculate_episode_data_index():