Update hands-on.mdx

units/en/unit4/hands-on.mdx

@@ -642,6 +642,8 @@ import datetime
 import json
 import imageio
 
+import tempfile
+
 import os
 ```
 
@@ -669,67 +671,77 @@ def record_video(env, policy, out_directory, fps=30):
 ```
 
 ```python
-def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30, local_repo_path="hub"):
-    """
-    Evaluate, Generate a video and Upload a model to Hugging Face Hub.
-    This method does the complete pipeline:
-    - It evaluates the model
-    - It generates the model card
-    - It generates a replay video of the agent
-    - It pushes everything to the Hub
+def push_to_hub(repo_id, 
+                model,
+                hyperparameters,
+                eval_env,
+                video_fps=30
+                ):
+  """
+  Evaluate, Generate a video and Upload a model to Hugging Face Hub.
+  This method does the complete pipeline:
+  - It evaluates the model
+  - It generates the model card
+  - It generates a replay video of the agent
+  - It pushes everything to the Hub
 
-    :param repo_id: repo_id: id of the model repository from the Hugging Face Hub
-    :param model: the pytorch model we want to save
-    :param hyperparameters: training hyperparameters
-    :param eval_env: evaluation environment
-    :param video_fps: how many frame per seconds to record our video replay
-    :param local_repo_path: where the local repository is
-    """
+  :param repo_id: repo_id: id of the model repository from the Hugging Face Hub
+  :param model: the pytorch model we want to save
+  :param hyperparameters: training hyperparameters
+  :param eval_env: evaluation environment
+  :param video_fps: how many frame per seconds to record our video replay 
+  """
 
-    _, repo_name = repo_id.split("/")
-    api = HfApi()
-
-    # Step 1: Create the repo
-    repo_url = api.create_repo(
+  _, repo_name = repo_id.split("/")
+  api = HfApi()
+  
+  # Step 1: Create the repo
+  repo_url = api.create_repo(
         repo_id=repo_id,
         exist_ok=True,
-    )
+  )
 
-    # Step 2: Download files
-    repo_local_path = Path(snapshot_download(repo_id=repo_id))
+  with tempfile.TemporaryDirectory() as tmpdirname:
+    local_directory = Path(tmpdirname)
+  
+    # Step 2: Save the model
+    torch.save(model, local_directory / "model.pt")
 
-    # Step 3: Save the model
-    torch.save(model, os.path.join(repo_local_path, "model.pt"))
-
-    # Step 4: Save the hyperparameters to JSON
-    with open(Path(repo_local_path) / "hyperparameters.json", "w") as outfile:
-        json.dump(hyperparameters, outfile)
-
-    # Step 5: Evaluate the model and build JSON
-    mean_reward, std_reward = evaluate_agent(
-        eval_env, hyperparameters["max_t"], hyperparameters["n_evaluation_episodes"], model
-    )
+    # Step 3: Save the hyperparameters to JSON
+    with open(local_directory / "hyperparameters.json", "w") as outfile:
+      json.dump(hyperparameters, outfile)
+    
+    # Step 4: Evaluate the model and build JSON
+    mean_reward, std_reward = evaluate_agent(eval_env, 
+                                            hyperparameters["max_t"],
+                                            hyperparameters["n_evaluation_episodes"], 
+                                            model)
     # Get datetime
     eval_datetime = datetime.datetime.now()
     eval_form_datetime = eval_datetime.isoformat()
 
     evaluate_data = {
-        "env_id": hyperparameters["env_id"],
-        "mean_reward": mean_reward,
-        "n_evaluation_episodes": hyperparameters["n_evaluation_episodes"],
-        "eval_datetime": eval_form_datetime,
+          "env_id": hyperparameters["env_id"], 
+          "mean_reward": mean_reward,
+          "n_evaluation_episodes": hyperparameters["n_evaluation_episodes"],
+          "eval_datetime": eval_form_datetime,
     }
 
     # Write a JSON file
-    with open(Path(repo_local_path) / "results.json", "w") as outfile:
+    with open(local_directory / "results.json", "w") as outfile:
         json.dump(evaluate_data, outfile)
 
-    # Step 6: Create the model card
-    # Env id
+    # Step 5: Create the model card
     env_name = hyperparameters["env_id"]
-
+    
     metadata = {}
-    metadata["tags"] = [env_name, "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"]
+    metadata["tags"] = [
+          env_name,
+          "reinforce",
+          "reinforcement-learning",
+          "custom-implementation",
+          "deep-rl-class"
+      ]
 
     # Add metrics
     eval = metadata_eval_result(
@@ -741,7 +753,7 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30, local_r
         metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
         dataset_pretty_name=env_name,
         dataset_id=env_name,
-    )
+      )
 
     # Merges both dictionaries
     metadata = {**metadata, **eval}
@@ -752,32 +764,32 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30, local_r
   To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
   """
 
-    readme_path = repo_local_path / "README.md"
+    readme_path = local_directory / "README.md"
     readme = ""
     if readme_path.exists():
         with readme_path.open("r", encoding="utf8") as f:
-            readme = f.read()
+          readme = f.read()
     else:
-        readme = model_card
+      readme = model_card
 
     with readme_path.open("w", encoding="utf-8") as f:
-        f.write(readme)
+      f.write(readme)
 
     # Save our metrics to Readme metadata
     metadata_save(readme_path, metadata)
 
-    # Step 7: Record a video
-    video_path = repo_local_path / "replay.mp4"
+    # Step 6: Record a video
+    video_path =  local_directory / "replay.mp4"
     record_video(env, model, video_path, video_fps)
 
     # Step 7. Push everything to the Hub
     api.upload_folder(
-        repo_id=repo_id,
-        folder_path=repo_local_path,
-        path_in_repo=".",
+          repo_id=repo_id,
+          folder_path=local_directory,
+          path_in_repo=".",
     )
 
-    print(f"Your model is pushed to the hub. You can view your model here: {repo_url}")
+    print(f"Your model is pushed to the Hub. You can view your model here: {repo_url}")
 ```
 
 By using `push_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the Hub**.
@@ -815,8 +827,7 @@ push_to_hub(
     cartpole_policy,  # The model we want to save
     cartpole_hyperparameters,  # Hyperparameters
     eval_env,  # Evaluation environment
-    video_fps=30,
-    local_repo_path="hub",
+    video_fps=30
 )
 ```
 
@@ -964,8 +975,7 @@ push_to_hub(
     pixelcopter_policy,  # The model we want to save
     pixelcopter_hyperparameters,  # Hyperparameters
     eval_env,  # Evaluation environment
-    video_fps=30,
-    local_repo_path="hub",
+    video_fps=30
 )
 ```