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

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

tests/fixtures/optimizers.py

@@ -0,0 +1,26 @@
+import pytest
+import torch
+
+from lerobot.common.optim.optimizers import AdamConfig
+from lerobot.common.optim.schedulers import VQBeTSchedulerConfig
+
+
+@pytest.fixture
+def model_params():
+    return [torch.nn.Parameter(torch.randn(10, 10))]
+
+
+@pytest.fixture
+def optimizer(model_params):
+    optimizer = AdamConfig().build(model_params)
+    # Dummy step to populate state
+    loss = sum(param.sum() for param in model_params)
+    loss.backward()
+    optimizer.step()
+    return optimizer
+
+
+@pytest.fixture
+def scheduler(optimizer):
+    config = VQBeTSchedulerConfig(num_warmup_steps=10, num_vqvae_training_steps=20, num_cycles=0.5)
+    return config.build(optimizer, num_training_steps=100)