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For the inference part, you can dockerise your model and use https://banana.dev for serverless GPU. They have examples on github on how to deploy and I’ve done it last year and was pretty straightforward. - Source: Hacker News / 16 days ago
I want to first check the user's ID and only if the user has an active subscription then the request will be forwarded to my API on banana.dev else the request will be blocked at the middleware itself. Should I use Express JS for the middleware i.e. Authentication and forwarding requests? Is there any other better way to improve my project structure? Currently it looks like:. Source: 6 months ago
Hey! Would love to have you try https://banana.dev (bias: I'm one of the founders). We run A100s for you and scale 0->1->n->0 on demand, so you only pay for what you use. I'm at erik@banana.dev if you want any help with it :). - Source: Hacker News / about 1 year ago
CAN you do this in AWS? Of course, do they have a service that does exactly what this banana.dev does? Probably not. Source: about 1 year ago
I've been using banana.dev for easily running my ML models on GPU in a serverless manner, and interacting with them as an API. Although the principle of the service is sound, it is currently too buggy to take into production (very long cold boots, errorring requests, always hitting capacity). Source: about 1 year ago
Projects are definitely the best way to learn models. Build things for fun that do things in topics/fields that you care about or think is cool. a few years ago when I was getting into ML stuff I build fantasy football things that weren't even useful but provided an actual use case. Then I did more complicated stuff with photography and lighting because I did real estate photography. As far as ML libraries go,... Source: almost 3 years ago
So far I’ve seen AWS Sagemaker kind of allows for a situation like this, but would rather not deal with all that config. Algorithmia and Nuclio are too enterprise focused. Neuro is new and looks great, but from my understanding I would still need to create a lambda instance myself that then calls neuro’s servers - too indirect. Is there a total solution out there for this? Source: almost 3 years ago
A couple of weeks ago I put out a post on DeepSpeech running on the serverless setup at Neuro (https://getneuro.ai), and I've now got Silero running there as well. I've found this model is a lot faster than DS and way more accurate. Seeing around 300ms per request at the moment, hopefully will be closer to 100ms soon but this is a pretty decent speed in this application already. Source: about 3 years ago
I just made a streaming script connecting Deepspeech to serverless GPUs at Neuro (https://getneuro.ai). Was a fun piece of work, and cool to play around with. You can find the source here: https://github.com/neuro-ai-dev/npu_examples/tree/main/deepspeech. Source: about 3 years ago
GPU.LAND - Cloud GPUs for Deep Learning — for ⅓ the price!
Lobe - Visual tool for building custom deep learning models
Clever Grid - Easy to use and fairly priced GPUs for Machine Learning
Opta - Opta is a new kind of Infrastructure-As-Code framework designed for fast moving startups.
mlblocks - A no-code Machine Learning solution. Made by teenagers.
TensorFlow Lite - Low-latency inference of on-device ML models
