I’ve been working on Flux, a platform to deploy and host deep learning models in production.
Instead of renting a GPU instance and setting up a Flask web server, you use git to push your trained model to Flux with some configuration and get back an http endpoint.
For example, you set that your input is the url to an image, and that your output should be the top classification and its likelihood, and that your model is in pytorch.
For example if you have a classifier for dog breeds you:
Make a POST to fluxdeploy.com/username/dog-classifier
with json { “url”: “...” }
And get back { “klass”: “Great Dane”, “probability”: 0.937373 }
No need to do your own devops, Flux will scale for you. And it’s priced per-request and cheaper than hosting your own web server. Flux also deals with versioning and dependencies.
It was originally called Astra, which ended up having even more conflicts.
I use f.lux and love it. Hoping mine is different enough to not confuse. Funny enough, I actually thought it was “f dot lux” and was conceptual thinking of it like “function dot light”
Instead of renting a GPU instance and setting up a Flask web server, you use git to push your trained model to Flux with some configuration and get back an http endpoint.
For example, you set that your input is the url to an image, and that your output should be the top classification and its likelihood, and that your model is in pytorch.
For example if you have a classifier for dog breeds you:
Make a POST to fluxdeploy.com/username/dog-classifier with json { “url”: “...” } And get back { “klass”: “Great Dane”, “probability”: 0.937373 }
No need to do your own devops, Flux will scale for you. And it’s priced per-request and cheaper than hosting your own web server. Flux also deals with versioning and dependencies.
Still working on streaming inputs like video.