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Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
Co-authored-by: Remi <re.cadene@gmail.com>
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Zhuoheng Li
2024-08-23 18:00:35 +08:00
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examples/4_train_policy_with_script.md
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@@ -170,6 +170,36 @@ python lerobot/scripts/train.py --config-dir outputs/train/my_experiment/checkpo
Note that you may still use the regular syntax for config parameter overrides (eg: by adding `training.offline_steps=200000`).
## Typical logs and metrics
When you start the training process, you will first see your full configuration being printed in the terminal. You can check it to make sure that you config it correctly and your config is not overrided by other files. The final configuration will also be saved with the checkpoint.
After that, you will see training log like this one:
```
INFO 2024-08-14 13:35:12 ts/train.py:192 step:0 smpl:64 ep:1 epch:0.00 loss:1.112 grdn:15.387 lr:2.0e-07 updt_s:1.738 data_s:4.774
```
or evaluation log like:
```
INFO 2024-08-14 13:38:45 ts/train.py:226 step:100 smpl:6K ep:52 epch:0.25 ∑rwrd:20.693 success:0.0% eval_s:120.266
```
These logs will also be saved in wandb if `wandb.enable` is set to `true`. Here are the meaning of some abbreviations:
- `smpl`: number of samples seen during training.
- `ep`: number of episodes seen during training. An episode contains multiple samples in a complete manipulation task.
- `epch`: number of time all unique samples are seen (epoch).
- `grdn`: gradient norm.
- `∑rwrd`: compute the sum of rewards in every evaluation episode and then take an average of them.
- `success`: average success rate of eval episodes. Reward and success are usually different except for the sparsing reward setting, where reward=1 only when the task is completed successfully.
- `eval_s`: time to evaluate the policy in the environment, in second.
- `updt_s`: time to update the network parameters, in second.
- `data_s`: time to load a batch of data, in second.
Some metrics are useful for initial performance profiling. For example, if you find the current GPU utilization is low via the `nvidia-smi` command and `data_s` sometimes is too high, you may need to modify batch size or number of dataloading workers to accelerate dataloading. We also recommend [pytorch profiler](https://github.com/huggingface/lerobot?tab=readme-ov-file#improve-your-code-with-profiling) for detailed performance probing.
---
So far we've seen how to train Diffusion Policy for PushT and ACT for ALOHA. Now, what if we want to train ACT for PushT? Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):