Sure I am just trying to get the saved model weights. Logging scalers works fine. I am using stable baselines 3 and pytorch.

Thanks! I couldn't find it either, but better to ask and be sure. Trying the run with manual logging now

Hi AnxiousSeal95 , the models are saved both with a weights and biases call back and through stable baselines 3 model.save. Yes that makes sense to me that the files local to the docker container can't be downloaded. But yes when setting output_uri to true no models appear in the UI at all which seems strange

I don't explicitly call torch save

Sure I will try that. Does ClearML have a specific Stable Baselines 3 framework tag or should I try with just PyTorch?

But you can't download them.

Manual logging has the same behavior. When the output destination is not set the model artifacts are saved but can't be downloaded. They are saved to the docker in which they ran and not the fileserver. When the output uri is set the artifacts don't appear at all.

This run with no output_uri specified produces artifacts.

Sure, here is a snippet.
` run = wandb.init(project="rsTest",sync_tensorboard=True)

add tensorboard logging to the model

model = PPO('MlpPolicy', env, verbose=1, tensorboard_log=f"runs/{run.id}",
learning_rate=args.learning_rate,
batch_size=args.batch_size,
n_steps=args.n_steps,
n_epochs=args.n_epochs,
device='cpu')

create wandb callback

wandb_callback = WandbCallback(model_save_freq=1000,
model...