3DGS.cpp
tiny-cuda-nn
| 3DGS.cpp | tiny-cuda-nn | |
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| 1 | 9 | |
| 333 | 3,432 | |
| - | 2.8% | |
| 8.4 | 5.9 | |
| about 1 month ago | about 1 month ago | |
| C++ | C++ | |
| GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
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3DGS.cpp
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?
awesome-3D-gaussian-splatting - Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
blis - BLAS-like Library Instantiation Software Framework
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.
DREAMPlace - Deep learning toolkit-enabled VLSI placement
MindsDB - The platform for customizing AI from enterprise data
jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/