fastaugment
jittor
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| fastaugment | jittor | |
|---|---|---|
| 2 | 4 | |
| 14 | 2,995 | |
| - | - | |
| 5.3 | 7.6 | |
| about 1 month ago | 4 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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fastaugment
jittor
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Jittor: High-performance deep learning framework based on JIT and meta-operators
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Tinygrad: A simple and powerful neural network framework
Very similar idea as Jittor, convolution definitely can be break down: https://github.com/Jittor/jittor/blob/master/python/jittor/n...
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How do I deal with ML models taking soooo long to train, when I have to optimize results?
-I've found JIT quite useful: https://github.com/Jittor/jittor
What are some alternatives?
cupy - NumPy & SciPy for GPU
Res2Net-PretrainedModels - (ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
shumai - Fast Differentiable Tensor Library in JavaScript and TypeScript with Bun + Flashlight
vsl - V library to develop Artificial Intelligence and High-Performance Scientific Computations
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
StylizedNeRF - [CVPR 2022] Code for StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D mutual learning
nnabla - Neural Network Libraries
loop_tool - A thin, highly portable toolkit for efficiently compiling dense loop-based computation.
xla - A machine learning compiler for GPUs, CPUs, and ML accelerators
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
black - The uncompromising Python code formatter