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

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Co-authored-by: Remi <remi.cadene@huggingface.co>
This commit is contained in:
Simon Alibert
2025-02-11 10:36:06 +01:00
committed by GitHub
parent 334deb985d
commit 90e099b39f
40 changed files with 1515 additions and 935 deletions
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22
examples/4_train_policy_with_script.md
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@@ -161,13 +161,13 @@ 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
@@ -175,12 +175,16 @@ In the output directory, there will be a folder called `checkpoints` with the fo
```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