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14
examples/4_train_policy_with_script.md
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@ -112,15 +112,15 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
We can then simply load the config values from this file using:
```bash
python lerobot/scripts/train.py \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/train_config.json \
--output_dir=outputs/train/act_aloha_transfer_2
```
`--config_path` is also a special argument which allows to initialize the config from a local config file. It can point to a directory that contains `train_config.json` or to the config file itself directly.
`--config_path` is also a special argument which allows to initialize the config from a local config file.
Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
```bash
python lerobot/scripts/train.py \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/train_config.json \
--output_dir=outputs/train/act_aloha_transfer_2
--policy.n_action_steps=80
```
@ -156,7 +156,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
```bash
python lerobot/scripts/train.py \
--config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
--config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/train_config.json \
--resume=true
```
You should see from the logging that your training picks up from where it left off.
@ -165,7 +165,7 @@ Another reason for which you might want to resume a run is simply to extend trai
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/ \
--config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/train_config.json \
--resume=true \
--steps=200000
```
@ -245,14 +245,14 @@ python lerobot/scripts/train.py \
#### Train a policy from scratch - config file + CLI
```bash
python lerobot/scripts/train.py \
--config_path=path/to/pretrained_model \ # <- can also be a repo_id
--config_path=path/to/pretrained_model/train_config.json \ # <- can also be a repo_id
--policy.n_action_steps=80 # <- you may still override values
```
#### Resume/continue a training run
```bash
python lerobot/scripts/train.py \
--config_path=checkpoint/pretrained_model/ \
--config_path=checkpoint/pretrained_model/train_config.json \
--resume=true \
--steps=200000 # <- you can change some training parameters
```