MegEngine
taco
| MegEngine | taco | |
|---|---|---|
| 5 | 2 | |
| 4,718 | 1,211 | |
| 0.1% | 1.3% | |
| 8.9 | 0.0 | |
| 8 days ago | 24 days ago | |
| C++ | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
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....?
What are some alternatives?
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.
blitz - Blitz++ Multi-Dimensional Array Library for C++
executorch - On-device AI across mobile, embedded and edge for PyTorch
Grassmann.jl - ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
CuTeLib - CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
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/
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
theme-ui - Build consistent, themeable React apps based on constraint-based design principles
mtensor - a c++/cuda template library for tensor lazy evaluation
ntfstool - Forensics tool for NTFS (parser, mft, bitlocker, deleted files)