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Show HN: TensorFire

tenso.rs

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Re: Show HN: TensorFire

#2

Post by jstsch »

Seriously cool. Great work. I did get a glitch every now and then in the rendered output (say 1 out of 5 times) using Safari 10.1.2, MBP touchbar 2016 15", Radeon Pro 460 4096 MB.

Re: Show HN: TensorFire

#5

Post by bijection »

cs702 wrote: post #4

Where is the repo?

We're still finishing up a few things (documentation etc) and planning on releasing more stuff tomorrow.

You can also sign up for the mailing list if you'd like us to email you when the repo goes live!

Re: Show HN: TensorFire

#6

Post by bijection »

zitterbewegung wrote: post #3

Really cool demo. How does this compare to https://github.com/transcranial/keras-js ? Do the authors have a licence in mind?

TensorFire is up to an order of magnitude faster than keras-js because it doesn't have to shuffle data back and forth between the gpu and cpu. Also TensorFire can run on browsers and devices that don't support OES_TEXTURE_FLOAT.

We will probably release it under an MIT license.

Re: Show HN: TensorFire

#7

Post by antimatter15 »

Hey HN!

We're really excited to finally share this with you all! This is the first of a series of demos that we're working to release this week, and we're hoping you'll keep us to that promise :)

Sorry if it doesn't work on your computer! There's still a few glitches and browser compatibility problems that we need to iron out, and we're collecting some telemetry data with LogRocket (https://logrocket.com/) to help us do so (so you all know what kind of data is being collected).

We'll open source the library under an MIT license once we finish writing up the API docs, and fixing these bugs.

Re: Show HN: TensorFire

#9

Post by rahimnathwani »

antimatter15 wrote: post #7

Hey HN! We're really excited to finally share this with you all! This is the first of a series of demos that we're working to release this week, and we're hoping you'll keep us to that promise :) Sorry if it doesn't work on your computer! There's still a few glitches and browser compatibility problems that we need to iron out, and we're collecting some telemetry data with LogRocket ( https://logrocket.com/ ) to help…

This looks awesome!

It looks like it (like keras-js) is only for inference (running already-trained models) and not for training. Is this correct?

Are the operations or memory required for training very different?

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