[HANDS-ON BUG] Unit 6 Package to hub

[JacquelineJR]

Package_to_hub is not working

When I try to push trained agent to hub using package_to_hub cell. An error occurs.
https://colab.research.google.com/drive/1i5qcc4OVOBSidYwmMbJx0FqzztX2EfMJ#scrollTo=J-cC-Feg9iMm

Material

• Did you use Google Colab? Yes

If not:

• Your Operating system (OS)
• Version of your OS

The error code is as follows:
ℹ This function will save, evaluate, generate a video of your agent,
create a model card and push everything to the hub. It might take up to 1min.
This is a work in progress: if you encounter a bug, please open an issue.

error Traceback (most recent call last)
/tmp/ipython-input-605551018.py in <cell line: 0>()
1 from huggingface_sb3 import package_to_hub
2
----> 3 package_to_hub(
4 model=model,
5 model_name=f"a2c-{env_id}",

12 frames
/usr/local/lib/python3.12/dist-packages/huggingface_sb3/push_to_hub.py in package_to_hub(model, model_name, model_architecture, env_id, eval_env, repo_id, commit_message, is_deterministic, n_eval_episodes, token, video_length, logs)
375
376 # Step 3: Evaluate the agent
--> 377 mean_reward, std_reward = _evaluate_agent(
378 model, eval_env, n_eval_episodes, is_deterministic, tmpdirname
379 )

/usr/local/lib/python3.12/dist-packages/huggingface_sb3/push_to_hub.py in _evaluate_agent(model, eval_env, n_eval_episodes, is_deterministic, local_path)
72 """
73 # Step 1: Evaluate the agent
---> 74 mean_reward, std_reward = evaluate_policy(
75 model, eval_env, n_eval_episodes, is_deterministic
76 )

/usr/local/lib/python3.12/dist-packages/stable_baselines3/common/evaluation.py in evaluate_policy(model, env, n_eval_episodes, deterministic, render, callback, reward_threshold, return_episode_rewards, warn)
85 current_rewards = np.zeros(n_envs)
86 current_lengths = np.zeros(n_envs, dtype="int")
---> 87 observations = env.reset()
88 states = None
89 episode_starts = np.ones((env.num_envs,), dtype=bool)

/usr/local/lib/python3.12/dist-packages/stable_baselines3/common/vec_env/vec_normalize.py in reset(self)
295 :return: first observation of the episode
296 """
--> 297 obs = self.venv.reset()
298 assert isinstance(obs, (np.ndarray, dict))
299 self.old_obs = obs

/usr/local/lib/python3.12/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py in reset(self)
76 for env_idx in range(self.num_envs):
77 maybe_options = {"options": self._options[env_idx]} if self._options[env_idx] else {}
---> 78 obs, self.reset_infos[env_idx] = self.envs[env_idx].reset(seed=self._seeds[env_idx], **maybe_options)
79 self._save_obs(env_idx, obs)
80 # Seeds and options are only used once

/usr/local/lib/python3.12/dist-packages/gymnasium/wrappers/common.py in reset(self, seed, options)
144 """
145 self._elapsed_steps = 0
--> 146 return super().reset(seed=seed, options=options)
147
148 @Property

/usr/local/lib/python3.12/dist-packages/gymnasium/core.py in reset(self, seed, options)
331 ) -> tuple[WrapperObsType, dict[str, Any]]:
332 """Uses the :meth:reset of the :attr:env that can be overwritten to change the returned data."""
--> 333 return self.env.reset(seed=seed, options=options)
334
335 def render(self) -> RenderFrame | list[RenderFrame] | None:

/usr/local/lib/python3.12/dist-packages/gymnasium/wrappers/common.py in reset(self, seed, options)
398 """Resets the environment with kwargs."""
399 self._has_reset = True
--> 400 return super().reset(seed=seed, options=options)
401
402 def render(self) -> RenderFrame | list[RenderFrame] | None:

/usr/local/lib/python3.12/dist-packages/gymnasium/core.py in reset(self, seed, options)
331 ) -> tuple[WrapperObsType, dict[str, Any]]:
332 """Uses the :meth:reset of the :attr:env that can be overwritten to change the returned data."""
--> 333 return self.env.reset(seed=seed, options=options)
334
335 def render(self) -> RenderFrame | list[RenderFrame] | None:

/usr/local/lib/python3.12/dist-packages/gymnasium/wrappers/common.py in reset(self, seed, options)
293 return env_reset_passive_checker(self.env, seed=seed, options=options)
294 else:
--> 295 return self.env.reset(seed=seed, options=options)
296
297 def render(self) -> RenderFrame | list[RenderFrame] | None:

/usr/local/lib/python3.12/dist-packages/panda_gym/envs/core.py in reset(self, seed, options)
279 super().reset(seed=seed, options=options)
280 self.task.np_random, seed = seeding.np_random(seed)
--> 281 with self.sim.no_rendering():
282 self.robot.reset()
283 self.task.reset()

/usr/lib/python3.12/contextlib.py in enter(self)
135 del self.args, self.kwds, self.func
136 try:
--> 137 return next(self.gen)
138 except StopIteration:
139 raise RuntimeError("generator didn't yield") from None

/usr/local/lib/python3.12/dist-packages/panda_gym/pybullet.py in no_rendering(self)
387 def no_rendering(self) -> Iterator[None]:
388 """Disable rendering within this context."""
--> 389 self.physics_client.configureDebugVisualizer(self.physics_client.COV_ENABLE_RENDERING, 0)
390 yield
391 self.physics_client.configureDebugVisualizer(self.physics_client.COV_ENABLE_RENDERING, 1)

error: Not connected to physics server.