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. (by NVIDIA)
MegEngine
MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架 (by MegEngine)
| DALI | MegEngine | |
|---|---|---|
| 5 | 5 | |
| 4,917 | 4,718 | |
| 1.1% | 0.2% | |
| 9.6 | 8.9 | |
| 3 days ago | 4 days ago | |
| C++ | C++ | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
DALI
Posts with mentions or reviews of DALI.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-15.
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[D] Will data augmentations work faster on TPUs?
Another option is DALI https://github.com/NVIDIA/DALI For my project while training EfficientNet2, it was a game changer. But it a way harder to implement in code than TorchVision or Kornia.
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DirectStorage - Loading data to GPU *directly* from the SSD drive, almost without using CPU
Check out https://github.com/nvidia/DALI
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mmap_ninja: Speedup your training dramatically by using memory-mapped files for your dataset
Small question if you are using GPU: How to this compare to GPUDirect Storage from Nvidia? can you have even more speedup by using both? I never toy with it, but the DALI project from Nvidia seem to tackle the same data loading problem.
- [D] Efficiently loading videos in PyTorch without extracting frames
MegEngine
Posts with mentions or reviews of MegEngine.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-25.
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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?
When comparing DALI and MegEngine you can also consider the following projects:
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
executorch - On-device AI across mobile, embedded and edge for PyTorch
Blurry - Blurry is an easy blur library for Android
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
vision - Datasets, Transforms and Models specific to Computer Vision
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
DREAMPlace - Deep learning toolkit-enabled VLSI placement
taco - The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs
ocaml-torch - OCaml bindings for PyTorch
mtensor - a c++/cuda template library for tensor lazy evaluation