Skip to content

ria-com/aqueduct

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

148 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Aqueduct

Framework to make youre prediction performance-efficient and scalable.

Key Benefits

  • Increases the throughput of your machine learning-based service
  • Uses shared memory for instantaneous transfer of large amounts of data between processes
  • All optimizations in one library
  • Supports multiple frameworks

Documentation

Examples

Installation

Install using pip:

pip install aqueduct

Moreover, aqueduct has "optional extras"

  • numpy - support types from numpy in shared memory
  • aiohttp - extension for aiohttp support(see more in examples)
pip install aqueduct[numpy,aiohttp]

Contact Us

Feel free to ask questions in Telegram: t.me/avito-ml

About

Framework for create performance-efficient prediction

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Python 99.3%
  • Other 0.7%