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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: We can then simply load the config values from this file using:
```bash ```bash
python lerobot/scripts/train.py \ 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 --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.: Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
```bash ```bash
python lerobot/scripts/train.py \ 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 --output_dir=outputs/train/act_aloha_transfer_2
--policy.n_action_steps=80 --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: 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 ```bash
python lerobot/scripts/train.py \ 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 --resume=true
``` ```
You should see from the logging that your training picks up from where it left off. 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: You could double the number of steps of the previous run with:
```bash ```bash
python lerobot/scripts/train.py \ 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 \ --resume=true \
--steps=200000 --steps=200000
``` ```
@ -245,14 +245,14 @@ python lerobot/scripts/train.py \
#### Train a policy from scratch - config file + CLI #### Train a policy from scratch - config file + CLI
```bash ```bash
python lerobot/scripts/train.py \ 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 --policy.n_action_steps=80 # <- you may still override values
``` ```
#### Resume/continue a training run #### Resume/continue a training run
```bash ```bash
python lerobot/scripts/train.py \ python lerobot/scripts/train.py \
--config_path=checkpoint/pretrained_model/ \ --config_path=checkpoint/pretrained_model/train_config.json \
--resume=true \ --resume=true \
--steps=200000 # <- you can change some training parameters --steps=200000 # <- you can change some training parameters
``` ```