qgallouedec HF staff commited on
Commit
1b0277d
1 Parent(s): 0233854

fix model count

Browse files
Files changed (3) hide show
  1. app.py +5 -4
  2. src/backend.py +5 -3
  3. src/evaluation.py +1 -0
app.py CHANGED
@@ -136,19 +136,21 @@ def get_leaderboard_df():
136
  try:
137
  with open(filename) as fp:
138
  report = json.load(fp)
139
- user_id, model_id = report["config"]["model_id"].split("/")
140
- row = {"user_id": user_id, "model_id": model_id, "model_sha": report["config"]["model_sha"]}
141
  if report["status"] == "DONE" and len(report["results"]) > 0:
 
 
142
  env_ids = list(report["results"].keys())
143
  assert len(env_ids) == 1, "Only one environment supported for the moment"
144
  row["env_id"] = env_ids[0]
145
  row["iqm_episodic_return"] = iqm(report["results"][env_ids[0]]["episodic_returns"])
146
- data.append(row)
147
  except Exception as e:
148
  logger.error(f"Error while processing {filename}: {e}")
149
 
150
  df = pd.DataFrame(data) # create DataFrame
151
  df = df.fillna("") # replace NaN values with empty strings
 
 
152
  return df
153
 
154
 
@@ -295,7 +297,6 @@ with gr.Blocks(css=css) as demo:
295
  # Load the first video of the first environment
296
  demo.load(refresh_one_video(df, env_ids[0]), outputs=[all_gr_videos[env_ids[0]]])
297
 
298
-
299
  with gr.TabItem("🚀 Getting my agent evaluated"):
300
  with open("texts/getting_my_agent_evaluated.md") as fp:
301
  gr.Markdown(fp.read())
 
136
  try:
137
  with open(filename) as fp:
138
  report = json.load(fp)
 
 
139
  if report["status"] == "DONE" and len(report["results"]) > 0:
140
+ user_id, model_id = report["config"]["model_id"].split("/")
141
+ row = {"user_id": user_id, "model_id": model_id, "model_sha": report["config"]["model_sha"]}
142
  env_ids = list(report["results"].keys())
143
  assert len(env_ids) == 1, "Only one environment supported for the moment"
144
  row["env_id"] = env_ids[0]
145
  row["iqm_episodic_return"] = iqm(report["results"][env_ids[0]]["episodic_returns"])
146
+ data.append(row)
147
  except Exception as e:
148
  logger.error(f"Error while processing {filename}: {e}")
149
 
150
  df = pd.DataFrame(data) # create DataFrame
151
  df = df.fillna("") # replace NaN values with empty strings
152
+ # Save to csv
153
+ df.to_csv("leaderboard.csv", index=False)
154
  return df
155
 
156
 
 
297
  # Load the first video of the first environment
298
  demo.load(refresh_one_video(df, env_ids[0]), outputs=[all_gr_videos[env_ids[0]]])
299
 
 
300
  with gr.TabItem("🚀 Getting my agent evaluated"):
301
  with open("texts/getting_my_agent_evaluated.md") as fp:
302
  gr.Markdown(fp.read())
src/backend.py CHANGED
@@ -1,5 +1,6 @@
1
  import json
2
  import os
 
3
  import re
4
  import tempfile
5
 
@@ -48,7 +49,7 @@ def _backend_routine():
48
  # Run an evaluation on the models
49
  with tempfile.TemporaryDirectory() as tmp_dir:
50
  commits = []
51
- model_id, sha = pending_models[0]
52
  logger.info(f"Running evaluation on {model_id}")
53
  report = {"config": {"model_id": model_id, "model_sha": sha}}
54
  try:
@@ -84,5 +85,6 @@ def backend_routine():
84
  except Exception as e:
85
  logger.error(f"{e.__class__.__name__}: {str(e)}")
86
 
87
- if __name__=="__main__":
88
- backend_routine()
 
 
1
  import json
2
  import os
3
+ import random
4
  import re
5
  import tempfile
6
 
 
49
  # Run an evaluation on the models
50
  with tempfile.TemporaryDirectory() as tmp_dir:
51
  commits = []
52
+ model_id, sha = random.choice(pending_models)
53
  logger.info(f"Running evaluation on {model_id}")
54
  report = {"config": {"model_id": model_id, "model_sha": sha}}
55
  try:
 
85
  except Exception as e:
86
  logger.error(f"{e.__class__.__name__}: {str(e)}")
87
 
88
+
89
+ if __name__ == "__main__":
90
+ backend_routine()
src/evaluation.py CHANGED
@@ -112,6 +112,7 @@ ALL_ENV_IDS = [
112
 
113
  NUM_EPISODES = 50
114
 
 
115
  class NoopResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
116
  """
117
  Sample initial states by taking random number of no-ops on reset.
 
112
 
113
  NUM_EPISODES = 50
114
 
115
+
116
  class NoopResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
117
  """
118
  Sample initial states by taking random number of no-ops on reset.