GPU.LAND
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Based on our record, GPU.LAND should be more popular than Neuro. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I'm just going to mention here the experience of someone who ran gpu.land (doesn't exist any more). He did something similar, monetized it (very cheap) and then had to shut down because people were running crypto miners on it. I hope you have a plan to avoid that type of abuse. Source: about 2 years ago
RIP to gpu.land... I was hoping they would take off because they seemed to have a cool product with great pricing. Source: almost 3 years ago
There's also https://gpu.land (which has their own comparison page). Source: almost 3 years ago
Heya, I'm also so just keeping in touch. After liek 1 month of non redditing, someone replied who claimed to be the developer of gpu.land Apparently it is cloud computing for full Linux rather than the Jupyter notebook like what we tried before. Can I ask what is the update on the cloud computing site? I messaged the gpu.land person to see if we can get some free trial ($1 per hour on cheapest one but I don't know... Source: about 3 years ago
There are also more affordable GPU-for-DL-lending options like gpu.land, although I have never used them so I can't vouch for them -- just something I saw on PH. Source: about 3 years 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
Banana.dev - Banana provides inference hosting for ML models in three easy steps and a single line of code.
Lobe - Visual tool for building custom deep learning models
Apple Core ML - Integrate a broad variety of ML model types into your app
Opta - Opta is a new kind of Infrastructure-As-Code framework designed for fast moving startups.
TensorFlow Lite - Low-latency inference of on-device ML models
mlblocks - A no-code Machine Learning solution. Made by teenagers.
