taco
MegEngine
| taco | MegEngine | |
|---|---|---|
| 2 | 5 | |
| 1,208 | 4,719 | |
| 1.1% | 0.2% | |
| 0.0 | 8.9 | |
| 18 days ago | 7 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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taco
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The Distributed Tensor Algebra Compiler (2022)
I agree! Much of this work was done as part of the overarching TACO project (https://github.com/tensor-compiler/taco), in an attempt to distribute sparse tensor computations (https://rohany.github.io/publications/sc2022-spdistal.pdf). MLIR recently (~mid 2022) began implementing the ideas from TACO into a "sparse tensor" dialect, so perhaps some of these ideas could make it into there. I'm working with MLIR these days, and if I could re-do the project now I would probably utilize and targetb the MLIR linalg infrastructure!
- Qué tire la primer piedra, aquien no le ha pasado así....?
MegEngine
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How to speedup 31*31 conv 10 times
The Real Performance in MegEngine
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[P] Train Model 3x as large with Dynamic Tensor Rematerialization
In Deep Learning you can trade space for compute by recomputing activation in backpropagation phase, known as gradient checkpointing. Classical gradient checkpointing algorithm is great but they dont work for eager execution. Dynamic Tensor Rematerialization(DTR) is a gradient checkpointing algorithm that work with eager execution, and is implemented at Megenine, a deep learning framework. Read this blogpost to learn more!
- Training 3x larger model on the same GPU cards
What are some alternatives?
blitz - Blitz++ Multi-Dimensional Array Library for C++
DALI - A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
Grassmann.jl - ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
executorch - On-device AI across mobile, embedded and edge for PyTorch
CuTeLib - CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
theme-ui - Build consistent, themeable React apps based on constraint-based design principles
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ntfstool - Forensics tool for NTFS (parser, mft, bitlocker, deleted files)
mtensor - a c++/cuda template library for tensor lazy evaluation