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equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
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diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
See CONTRIBUTING.md for details on getting started, and how to build the documentation locally.
Okay, that's easy-to-use. What about generality? The evidence here is Diffrax, which is a library of differential equation solvers... built around Equinox. How so? It's because Equinox is something more general than a "neural network library". It's a parameterised function library. So whilst neural networks are functions parameterised by their weights/biases/etc., Equinox is general enough to handle far more complicated use-cases; in this case all of the Diffrax solvers are Equinox modules. So are all of the adaptive step size controllers. And the adjoint methods. And so on.
On another note, what did you dislike in Sphinx ? I found it pretty easy to work with until now and there are quite nice themes, like Furo (https://github.com/pradyunsg/furo), which is actually pretty similar to your current docs theme. I used it recently for one of my projects (see https://francois-rozet.github.io/piqa/piqa.psnr.html).
(I already have a couple of these. See e.g. [pytkdocs_tweaks](github.com/patrick-kidger/pytkdocs_tweaks) and include_exclude_files.)
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