GeometricFlux.jl
DataLoaders.jl
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GeometricFlux.jl | DataLoaders.jl | |
---|---|---|
3 | 1 | |
348 | 73 | |
1.4% | - | |
5.1 | 0.0 | |
6 months ago | over 1 year ago | |
Julia | Julia | |
MIT License | MIT License |
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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.
GeometricFlux.jl
- TensorFlow Graph Neural Networks
- Fastai.jl: Fastai for Julia
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OO in Python is mostly pointless
@ddragon described it rather well. Some significant examples include JuMP (https://jump.dev) for mathematical optimization and how FluxML (Julia's "Keras"), Zygote (whole program automatic differentiation) and ODE solvers can work together easily to build Neural ODE solvers in relatively few lines of code. It is also why one person can write - https://github.com/yuehhua/GeometricFlux.jl - and benefit from the CUDA, FluxML, Zygote packages to get high performance.
DataLoaders.jl
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Fastai.jl: Fastai for Julia
> When should I choose FastAI.jl vs fastai?
This depends on your use cases and how comfortable you are with a) Julia b) having to roll some of your own code. For the first, I'd recommend poking around with the language before as well as using the linked dev channel in TFA to get an informed opinion.
FastAI.jl itself is composed of multiple constituent packages that can and are used independently, so there's also the option of mixing and matching. For example, https://github.com/lorenzoh/DataLoaders.jl is completely library agnostic.
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
Transformers.jl - Julia Implementation of Transformer models
FluxTraining.jl - A flexible neural net training library inspired by fast.ai
swiftai - Swift for TensorFlow's high-level API, modeled after fastai