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| import time, asyncio | |
| from openai import AsyncOpenAI | |
| import uuid | |
| import traceback | |
| litellm_client = AsyncOpenAI(api_key="test", base_url="http://0.0.0.0:8000") | |
| async def litellm_completion(): | |
| # Your existing code for litellm_completion goes here | |
| try: | |
| print("starting embedding calls") | |
| response = await litellm_client.embeddings.create( | |
| model="text-embedding-ada-002", | |
| input=[ | |
| "hello who are you" * 2000, | |
| "hello who are you tomorrow 1234" * 1000, | |
| "hello who are you tomorrow 1234" * 1000, | |
| ], | |
| ) | |
| print(response) | |
| return response | |
| except Exception as e: | |
| # If there's an exception, log the error message | |
| with open("error_log.txt", "a") as error_log: | |
| error_log.write(f"Error during completion: {str(e)}\n") | |
| pass | |
| async def main(): | |
| start = time.time() | |
| n = 100 # Number of concurrent tasks | |
| tasks = [litellm_completion() for _ in range(n)] | |
| chat_completions = await asyncio.gather(*tasks) | |
| successful_completions = [c for c in chat_completions if c is not None] | |
| # Write errors to error_log.txt | |
| with open("error_log.txt", "a") as error_log: | |
| for completion in chat_completions: | |
| if isinstance(completion, str): | |
| error_log.write(completion + "\n") | |
| print(n, time.time() - start, len(successful_completions)) | |
| if __name__ == "__main__": | |
| # Blank out contents of error_log.txt | |
| open("error_log.txt", "w").close() | |
| asyncio.run(main()) | |