mlq | Asynchronous queue for machine learning jobs | Machine Learning library

mlq is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. mlq has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install mlq' or download it from GitHub, PyPI.

MLQ is a job queueing system, and framework for workers to process queued jobs, providing an easy way to offload long running jobs to other computers. You've got an ML model and want to deploy it. Meaning that, you have a web app and want users to be able to re-train the model, and that takes a long time. Or perhaps even inference takes a long time. Long, relative to the responsiveness users expect from webapps, meaning, not immediate. You can't do this stuff direct from your Flask app, because it would lock up the app and not scale beyond a couple of users. The solution is to enqueue the user's request, and until then, show the user some loading screen, or tell them to check back in a few minutes. The ML stuff happens in the background, in a separate process, or perhaps on a different machine. When it's done, the user is notified (maybe via websockets; maybe their browser is polling at intervals). Or perhaps your company has a limited resource, such as GPUs, and you need a solution for employees to access them from Jupyter one-by-one. MLQ is designed to provide a performant, reliable, and most of all easy to use, queue and workers to solve the above common problems. It's in Python 3.6+, is built on asyncio, and uses Redis as a queue backend.