Remove offline training, refactor train.py and logging/checkpointing

Makefile

@@ -39,8 +39,8 @@ test-act-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=4 \
-		--online.steps=0 \
+		--steps=4 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \
@@ -76,8 +76,8 @@ test-diffusion-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=2 \
-		--online.steps=0 \
+		--steps=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -106,8 +106,8 @@ test-tdmpc-ete-train:
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
-		--offline.steps=2 \
-		--online.steps=0 \
+		--steps=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -126,30 +126,3 @@ test-tdmpc-ete-eval:
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--device=$(DEVICE)
-
-# TODO(rcadene): fix online buffer to storing "task"
-# test-tdmpc-ete-train-with-online:
-# 	python lerobot/scripts/train.py \
-# 		--policy.type=tdmpc \
-# 		--env.type=pusht \
-# 		--env.obs_type=environment_state_agent_pos \
-# 		--env.episode_length=5 \
-# 		--dataset.repo_id=lerobot/pusht_keypoints \
-# 		--dataset.image_transforms.enable=true \
-# 		--dataset.episodes="[0]" \
-# 		--batch_size=2 \
-# 		--offline.steps=2 \
-# 		--online.steps=20 \
-# 		--online.rollout_n_episodes=2 \
-# 		--online.rollout_batch_size=2 \
-# 		--online.steps_between_rollouts=10 \
-# 		--online.buffer_capacity=1000 \
-# 		--online.env_seed=10000 \
-# 		--save_checkpoint=false \
-# 		--save_freq=10 \
-# 		--log_freq=1 \
-# 		--eval.use_async_envs=true \
-# 		--eval.n_episodes=1 \
-# 		--eval.batch_size=1 \
-# 		--device=$(DEVICE) \
-# 		--output_dir=tests/outputs/tdmpc_online/

examples/3_train_policy.py

@@ -86,8 +86,7 @@ def main():
     while not done:
         for batch in dataloader:
             batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-            output_dict = policy.forward(batch)
-            loss = output_dict["loss"]
+            loss, _ = policy.forward(batch)
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()

examples/4_train_policy_with_script.md

@@ -161,26 +161,30 @@ python lerobot/scripts/train.py \
 ```
 You should see from the logging that your training picks up from where it left off.
 
-Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--offline.steps`, which is 100 000 by default.
+Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default.
 You could double the number of steps of the previous run with:
 ```bash
 python lerobot/scripts/train.py \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true \
-    --offline.steps=200000
+    --steps=200000
 ```
 
 ## Outputs of a run
 In the output directory, there will be a folder called `checkpoints` with the following structure:
 ```bash
 outputs/train/run_resumption/checkpoints
 ├── 000100  # checkpoint_dir for training step 100
-│   ├── pretrained_model
-│   │   ├── config.json  # pretrained policy config
-│   │   ├── model.safetensors  # model weights
-│   │   ├── train_config.json  # train config
-│   │   └── README.md  # model card
-│   └── training_state.pth  # optimizer/scheduler/rng state and training step
+│   ├── pretrained_model/
+│   │   ├── config.json  # policy config
+│   │   ├── model.safetensors  # policy weights
+│   │   └── train_config.json  # train config
+│   └── training_state/
+│       ├── optimizer_param_groups.json  #  optimizer param groups
+│       ├── optimizer_state.safetensors  # optimizer state
+│       ├── rng_state.safetensors  # rng states
+│       ├── scheduler_state.json  # scheduler state
+│       └── training_step.json  # training step
 ├── 000200
 └── last -> 000200  # symlink to the last available checkpoint
 ```
@@ -250,7 +254,7 @@ python lerobot/scripts/train.py \
 python lerobot/scripts/train.py \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
-    --offline.steps=200000  # <- you can change some training parameters
+    --steps=200000  # <- you can change some training parameters
 ```
 
 #### Fine-tuning

examples/advanced/2_calculate_validation_loss.py

@@ -75,9 +75,9 @@ def main():
     n_examples_evaluated = 0
     for batch in val_dataloader:
         batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-        output_dict = policy.forward(batch)
+        loss, _ = policy.forward(batch)
 
-        loss_cumsum += output_dict["loss"].item()
+        loss_cumsum += loss.item()
         n_examples_evaluated += batch["index"].shape[0]
 
     # Calculate the average loss over the validation set.

lerobot/common/constants.py

@@ -4,3 +4,14 @@
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
+
+# files & directories
+CHECKPOINTS_DIR = "checkpoints"
+LAST_CHECKPOINT_LINK = "last"
+PRETRAINED_MODEL_DIR = "pretrained_model"
+TRAINING_STATE_DIR = "training_state"
+RNG_STATE = "rng_state.safetensors"
+TRAINING_STEP = "training_step.json"
+OPTIMIZER_STATE = "optimizer_state.safetensors"
+OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
+SCHEDULER_STATE = "scheduler_state.json"