Merge branch 'update_readme_on_wandb' into tutorial_act_pusht

README.md

@@ -92,6 +92,8 @@ To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tra
 wandb login
 ```
 
+(note: you will also need to enable WandB in the configuration. See below.)
+
 ## Walkthrough
 
 ```
@@ -158,13 +160,14 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
 
 Check out [example 3](./examples/3_train_policy.py) that illustrates how to start training a model.
 
-In general, you can use our training script to easily train any policy. To use wandb for logging training and evaluation curves, make sure you ran `wandb login`. Here is an example of training the ACT policy on trajectories collected by humans on the Aloha simulation environment for the insertion task:
+In general, you can use our training script to easily train any policy. To use wandb for logging training and evaluation curves, make sure you ran `wandb login`, and enable it in the configuration. Here is an example of training the ACT policy on trajectories collected by humans on the Aloha simulation environment for the insertion task:
 ```bash
 python lerobot/scripts/train.py \
     policy=act \
     env=aloha \
     env.task=AlohaInsertion-v0 \
-    dataset_repo_id=lerobot/aloha_sim_insertion_human
+    dataset_repo_id=lerobot/aloha_sim_insertion_human \
+    wandb.enable=true
 ```
 
 The experiment directory is automatically generated and will show up in yellow in your terminal. It looks like `outputs/train/2024-05-05/20-21-12_aloha_act_default`. You can manually specify an experiment directory by adding this argument to the `train.py` python command:
@@ -175,14 +178,6 @@ The experiment directory is automatically generated and will show up in yellow i
 A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of logs from wandb:
 ![](media/wandb.png)
 
-You can deactivate wandb by adding these arguments to the `train.py` python command:
-```bash
-    # this first one is not necessary to disable wandb, but you can set it with wandb enabled to avoid
-    # uploading model checkpoints
-    wandb.disable_artifact=true \
-    wandb.enable=false
-```
-
 Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
 
 

lerobot/configs/default.yaml

@@ -35,7 +35,7 @@ eval:
   use_async_envs: false
 
 wandb:
-  enable: true
+  enable: false
   # Set to true to disable saving an artifact despite save_model == True
   disable_artifact: false
   project: lerobot