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_save_path=f"models/{run.id}",
verbose=2,
)

variable for how often to save the model

time_steps = 100000
for i in range(25):
# add the reset_num_timesteps=False argument to the learn function to prevent the model from resetting the timestep counter
# add the tb_log_name argument to the learn function to log the tensorboard data to the correct folder
model.learn(total_timesteps=time_steps, callback=wandb_callback, progress_bar=True, reset_num_timesteps=False,tb_log_name=f"runs/{run.id}")
# save the model to the models folder with the run id and the current timestep
model.save(f"models/{run.id}/{time_steps*(i+1)}") `The part I don't understand is that when output_uri is not set then model artifacts show up. But when it is they don't.