tensor_annotations
tiny-cuda-nn
tensor_annotations | tiny-cuda-nn | |
---|---|---|
2 | 9 | |
158 | 3,397 | |
-0.6% | 1.8% | |
5.8 | 5.9 | |
10 months ago | about 1 month ago | |
Python | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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tensor_annotations
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrick-kidger/torchtyping or https://github.com/deepmind/tensor_annotations
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Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
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?
dex-lang - Research language for array processing in the Haskell/ML family
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
blis - BLAS-like Library Instantiation Software Framework
miniF2F - Formal to Formal Mathematics Benchmark
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
TablaM - The practical relational programing language for data-oriented applications
juliaup - Julia installer and version multiplexer
MindsDB - The platform for customizing AI from enterprise data
RecursiveFactorization
FL - FL language specification and reference implementations
RecursiveFactorization.jl