pkalkman commited on
Commit
4370c42
·
1 Parent(s): 8f281c1

Added more info in log and created file with the time the process was run

Browse files
Files changed (1) hide show
  1. app.py +18 -2
app.py CHANGED
@@ -1,6 +1,7 @@
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  import os
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  import json
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  import requests
 
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  import pandas as pd
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  import gradio as gr
@@ -76,12 +77,26 @@ def get_model_ids(rl_env):
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  return model_ids
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  # Parralelized version
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  def update_leaderboard_dataset_parallel(rl_env, path):
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  # Get model ids associated with rl_env
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  model_ids = get_model_ids(rl_env)
 
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- def process_model(model_id):
 
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  meta = get_metadata(model_id)
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  if meta is None:
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  return None
@@ -98,7 +113,8 @@ def update_leaderboard_dataset_parallel(rl_env, path):
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  row["Std Reward"] = std_reward
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  return row
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- data = list(thread_map(process_model, model_ids, desc="Processing models"))
 
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  # Filter out None results (models with no metadata)
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  data = [row for row in data if row is not None]
 
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  import os
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  import json
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  import requests
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+ import datetime
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  import pandas as pd
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  import gradio as gr
 
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  return model_ids
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+ def store_last_update_time(path):
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+ # Get the current time
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+ current_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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+
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+ # Create a file to store the last update time
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+ last_update_file = os.path.join(path, "last_update.txt")
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+ with open(last_update_file, 'w') as f:
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+ f.write(f"Last update time: {current_time}")
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+
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+ print(f"Stored last update time: {current_time}")
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+
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+
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  # Parralelized version
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  def update_leaderboard_dataset_parallel(rl_env, path):
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  # Get model ids associated with rl_env
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  model_ids = get_model_ids(rl_env)
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+ total_models = len(model_ids)
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+ def process_model(index, model_id):
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+ print(f"Processing model {index + 1} of {total_models}: {model_id}")
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  meta = get_metadata(model_id)
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  if meta is None:
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  return None
 
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  row["Std Reward"] = std_reward
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  return row
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+ # Process models with index tracking
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+ data = list(thread_map(lambda idx_model: process_model(*idx_model), enumerate(model_ids), desc="Processing models"))
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  # Filter out None results (models with no metadata)
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  data = [row for row in data if row is not None]