INSTA
tiny-cuda-nn
| INSTA | tiny-cuda-nn | |
|---|---|---|
| 3 | 9 | |
| 405 | 3,432 | |
| - | 2.8% | |
| 5.2 | 5.9 | |
| 4 months ago | about 1 month ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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INSTA
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I am very happy to share our recent CVPR2023 work on instant volumetric head avatars (INSTA) which allows you to reconstruct an animatable NeRF of a human head within a few minutes.
https://github.com/Zielon/INSTA https://zielon.github.io/insta/
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[R] I am very happy to share our recent CVPR2023 work on instant volumetric head avatars (INSTA) which allows you to reconstruct an animatable NeRF of a human head within a few minutes. GitHub and the paper in the comments.
Arixv GitHub Webpage
tiny-cuda-nn
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[D] Have their been any attempts to create a programming language specifically for machine learning?
In the opposite direction from your question is a very interesting project, TinyNN all implemented as close to the metal as possible and very fast: https://github.com/NVlabs/tiny-cuda-nn
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A CUDA-free instant NGP renderer written entirely in Python: Support real-time rendering and camera interaction and consume less than 1GB of VRAM
This repo only implemented the rendering part of the NGP but is more simple and has a lesser amount of code compared to the original (Instant-NGP and tiny-cuda-nn).
- Tiny CUDA Neural Networks: fast C++/CUDA neural network framework
- Making 3D holograms this weekend with the very “Instant” Neural Graphics Primitives by nvidia — made this volume from 100 photos taken with an old iPhone 7 Plus
- NVlabs/tiny-CUDA-nn: fast C++/CUDA neural network framework
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Small Neural networks in Julia 5x faster than PyTorch
...a C++ library with a CUDA backend. But these high-performance building blocks might only be saturating the GPU fully if the data is large enough.
I haven't looked at implementing these things, but I imagine uf you have smaller networks and thus less data, the large building blocks may not be optimal. You may for example want to fuse some operations to reduce memory latency from repeated memory access.
In PyTorch world, there are approaches for small networks as well, there is https://github.com/NVlabs/tiny-cuda-nn - as far as I understand from the first link in the README, it makes clever use of the CUDA shared memory, which can hold all the weights of a tiny network (but not larger ones).
- [R] Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Training a NeRF takes 5 seconds!)
- Tiny CUDA Neural Networks
- Real-Time Neural Radiance Caching for Path Tracing
What are some alternatives?
Stuxnet-Source - stuxnet Source & Binaries. (+PLC ROOTKIT) ONLY FOR ACADEMICAL RESEARCH AND EDUCATIONAL PURPOSES! Includes: Source files, Binaries, PLC Samples,Fanny Added in another repo.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
MICA - MICA - Towards Metrical Reconstruction of Human Faces [ECCV2022]
blis - BLAS-like Library Instantiation Software Framework
hashcat - World's fastest and most advanced password recovery utility
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
juliaup - Julia installer and version multiplexer
RecursiveFactorization
RecursiveFactorization.jl
vectorflow
LeNetTorch - PyTorch implementation of LeNet for fitting MNIST for benchmarking.