openai-manager 0.0.2

Speed up your OpenAI requests by balancing prompts to multiple API keys.

OpenAI-Manager

Speed up your OpenAI requests by balancing prompts to multiple API keys. Quite useful if you are playing with code-davinci-002 endpoint.

If you seldomly trigger rate limit errors, it is unnecessary to use this package.

Disclaimer

Before using this tool, you are required to read the EULA and ToS of OpenAI L.P. carefully. Actions that violate the OpenAI user agreement may result in the API Key and associated account being suspended. The author shall not be held liable for any consequential damages.

Design

TL;DR: this package helps you manage rate limit (both request-level and token-level) for each api_key for maximum number of requests to OpenAI API.

This is extremely helpful if you use CODEX endpoint or you have a handful of free-trial accounts due to limited budget. Free-trial accounts apply strict rate limit.

Quickstart

1. Install openai-manager on PyPI.

   pip install openai-manager
   

2. Prepare your OpenAI credentials in

   1. Environmental Varibles: any envvars beginning with OPENAI_API_KEY will be used to initialized the manager. Best practice to load your api keys is to prepare a .env file like:

   OPENAI_API_KEY_1=sk-Nxo******
   OPENAI_API_KEY_2=sk-TG2******
   OPENAI_API_KEY_3=sk-Kpt******
   # You can set a global proxy for all api_keys
   OPENAI_API_PROXY=http://127.0.0.1:7890
   # You can also append proxy to each api_key. 
   # Make sure the indices match.
   OPENAI_API_PROXY_1=http://127.0.0.1:7890
   OPENAI_API_PROXY_2=http://127.0.0.1:7890
   OPENAI_API_PROXY_3=http://127.0.0.1:7890
   

   Then load your environmental varibles before running any scripts:

   export $(grep -v '^#' .env | xargs)
   

   2. YAML config file: you can add more fine-grained restrictions on each API key if you know the ratelimit for each key in advance. See example_config.yml for details.

   import openai_manager
   openai_manager.append_auth_from_config(config_path='example_config.yml')
   

3. Run this minimal running example to see how to boost your OpenAI completions. (more interfaces coming!)

   import openai as official_openai
   import openai_manager
   
   @timeit
   def test_official_separate():
       for i in range(10):
           prompt = "Once upon a time, "
           response = official_openai.Completion.create(
               model="code-davinci-002",
               prompt=prompt,
               max_tokens=20,
           )
           print("Answer {}: {}".format(i, response["choices"][0]["text"]))
   
   @timeit
   def test_manager():
       prompt = "Once upon a time, "
       prompts = [prompt] * 10
       responses = openai_manager.Completion.create(
           model="code-davinci-002",
           prompt=prompts,
           max_tokens=20,
       )
       assert len(responses) == 10
       for i, response in enumerate(responses):
           print("Answer {}: {}".format(i, response["choices"][0]["text"]))
   

Configuration

Most configurations are manupulated by environmental variables.

GLOBAL_NUM_REQUEST_LIMIT = os.getenv("OPENAI_GLOBAL_NUM_REQUEST_LIMIT", 500)  # aiohttp connection limit
REQUESTS_PER_MIN_LIMIT = os.getenv("OPENAI_REQUESTS_PER_MIN_LIMIT", 10)  # number of requests per minute, config file will overwrite this
TOKENS_PER_MIN_LIMIT = os.getenv("TOKENS_PER_MIN_LIMIT", 40_000)  # number of tokens per minute, config file will overwrite this
COROTINE_PER_AUTH = int(os.getenv("COROTINE_PER_AUTH", 3))  # number of corotine per api_key, decrease it to 1 if ratelimit errors are triggered too often
ATTEMPTS_PER_PROMPT = int(os.getenv("ATTEMPTS_PER_PROMPT", 5))  # number of attempts per prompt
RATELIMIT_AFTER_SUBMISSION = str2bool(os.getenv("RATELIMIT_AFTER_SUBMISSION", "True"))  # whether to track ratelimit after submission, keep it enabled if response takes a long time
OPENAI_LOG_LEVEL = int(os.getenv("OPENAI_LOG_LEVEL", logging.WARNING))  # default log level is WARNING, 10-DEBUG, 20-INFO, 30-WARNING, 40-ERROR, 50-CRITICAL; set to 10 if getting stuck

Rate limit triggers will be visible under logging.WARNING. Run OPENAI_LOG_LEVEL=40 to ignore rate limit warnings if you believe current setting is stable enought.

Performance Assessment

After ChatCompletion release, the code-davinci-002 endpoint becomes slow. Using 10 API keys, running 100 completions with max_tokens=20 and other hyperparameters left default took 90 seconds on average. Using official API, it took 10 seconds per completion, thus 1000 in total.

Theroticallly, the throughput increases linearly with the number of API keys.

Frequently Asked Questions

1. Q: Why don't we just use official batching function?

    prompt = "Once upon a time, "
    prompts = [prompt] * 10
    response = openai.Completion.create(
        model="code-davinci-002",
        prompt=prompts,  # official batching allows multiple prompts in one request
        max_tokens=20,
    )
    assert len(response["choices"]) == 10
    for i, answer in enumerate(response["choices"]):
        print("Answer {}: {}".format(i, answer["text"]))
   

   A: code-davinci-002 or other similar OpenAI endpoints apply strict token-level rate limit, even if you upgrade to pay-as-you-go user. Simple batching would not solve this.

Acknowledgement

openai-cookbook

openai-python

TODO

• Support all functions in OpenAI Python API.
  • Completions
  • Embeddings
  • Generations
  • ChatCompletions
• Better back-off strategy for maximum throughput.
• Properly handling exceptions raised by OpenAI API.
• Automatic batching prompts to reduce the number of requests.
• Automatic rotation of tons of OpenAI API Keys. (Removing invaild, adding new, etc.)

Donation

If this package helps your research, consider making a donation via GitHub!