Oceananigans.jl
XLA.jl
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Oceananigans.jl | XLA.jl | |
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4 | 2 | |
875 | 46 | |
1.6% | - | |
9.5 | 10.0 | |
5 days ago | almost 4 years ago | |
Julia | Julia | |
MIT License | MIT License |
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.
Oceananigans.jl
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Julia 1.10 Released
I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those.
The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone.
For instance a known Julia project that simulates diff equations seems to have implemented their own solver
https://github.com/CliMA/Oceananigans.jl
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GPU vendor-agnostic fluid dynamics solver in Julia
I‘m currently playing around with Oceananigans.jl (https://github.com/CliMA/Oceananigans.jl). Do you know how both are similar or different?
Oceananigans.jl has really intuitive step-by-step examples and a great discussion page on GitHub.
- Supercharged high-resolution ocean simulation with Jax
XLA.jl
- PyTorch vs. TensorFlow in 2022
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Supercharged high-resolution ocean simulation with Jax
https://github.com/FluxML/XLA.jl
When in doubt, piggybacking on (or at least interoperating with) what the large technology companies are investing in is probably savvy, sort of what the OP did.
What are some alternatives?
MATDaemon.jl
flax - Flax is a neural network library for JAX that is designed for flexibility.
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support
MITgcm - M.I.T General Circulation Model master code and documentation repository
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
Metal.jl - Metal programming in Julia
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
opendylan - Open Dylan compiler and IDE
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
julia-ml-from-scratch - Machine learning from scratch in Julia
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets