Plotly.NET
equinox
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Plotly.NET | equinox | |
---|---|---|
4 | 31 | |
584 | 1,809 | |
8.7% | - | |
8.3 | 9.2 | |
about 1 month ago | 7 days ago | |
F# | Python | |
MIT License | Apache License 2.0 |
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.
Plotly.NET
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Exploratory Data Analysis with F#, Plotly.NET, and ML.NET DataFrames
There are many charting options for .NET in a Polyglot Notebook, including ScottPlot, the older XPlot Library, and Plotly.NET. I'm a big fan of Plotly for data visualization in Python, so I choose it when I can in other languages too. However, Plotly.NET is also becoming the defacto standard for data visualization in .NET notebooks.
- Issue with F# Plotly.NET chart descriptions - would love some advice!
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Best libraries for scientific charts?
You can look at the csharp tests for examples https://github.com/plotly/Plotly.NET/tree/dev/tests/Plotly.NET.Tests.CSharp
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F# + Plotly.NET + AngouriMath + Interactive: symbolic algebra for research!
Plotly.NET: awesome package for plotting in F# (in that article, it's there).
equinox
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Ask HN: What side projects landed you a job?
I wrote a JAX-based neural network library (Equinox [1]) and numerical differential equation solving library (Diffrax [2]).
At the time I was just exploring some new research ideas in numerics -- and frankly, procrastinating from writing up my PhD thesis!
But then one of the teams at Google starting using them, so they offered me a job to keep developing them for their needs. Plus I'd get to work in biotech, which was a big interest of mine. This was a clear dream job offer, so I accepted.
Since then both have grown steadily in popularity (~2.6k GitHub stars) and now see pretty widespread use! I've since started writing several other JAX libraries and we now have a bit of an ecosystem going.
[1] https://github.com/patrick-kidger/equinox
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[P] Optimistix, nonlinear optimisation in JAX+Equinox!
The elevator pitch is Optimistix is really fast, especially to compile. It plays nicely with Optax for first-order gradient-based methods, and takes a lot of design inspiration from Equinox, representing the state of all the solvers as standard JAX PyTrees.
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
If you like PyTorch then you might like Equinox, by the way. (https://github.com/patrick-kidger/equinox ; 1.4k GitHub stars now!)
- Equinox: Elegant easy-to-use neural networks in Jax
- Show HN: Equinox (1.3k stars), a JAX library for neural networks and sciML
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Pytrees
You're thinking of `jax.closure_convert`. :)
(Although technically that works by tracing and extracting all constants from the jaxpr, rather than introspecting the function's closure cells -- it sounds like your trick is the latter.)
When you discuss dynamic allocation, I'm guessing you're mainly referring to not being able to backprop through `jax.lax.while_loop`. If so, you might find `equinox.internal.while_loop` interesting, which is an unbounded while loop that you can backprop through! The secret sauce is to use a treeverse-style checkpointing scheme.
https://github.com/patrick-kidger/equinox/blob/f95a8ba13fb35...
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Writing Python like it’s Rust
I'm a big fan of using ABCs to declare interfaces -- so much so that I have an improved abc.ABCMeta that also handles abstract instance variables and abstract class variables: https://github.com/patrick-kidger/equinox/blob/main/equinox/_better_abstract.py
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[D] JAX vs PyTorch in 2023
For the daily research, I use Equinox (https://github.com/patrick-kidger/equinox) as a DL librarry in JAX.
- [Machinelearning] [D] État actuel de JAX vs Pytorch?
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Training Deep Networks with Data Parallelism in Jax
It sounds like you're concerned about how downstream libraries tend to wrap JAX transformations to handle their own thing? (E.g. `haiku.grad`.)
If so, then allow me to make my usual advert here for Equinox:
https://github.com/patrick-kidger/equinox
This actually works with JAX's native transformations. (There's no `equinox.vmap` for example.)
On higher-order functions more generally, Equinox offers a way to control these quite carefully, by making ubiquitous use of callables that are also pytrees. E.g. a neural network is both a callable in that it has a forward pass, and a pytree in that it records its parameters in its tree structure.
What are some alternatives?
interactive - .NET Interactive combines the power of .NET with many other languages to create notebooks, REPLs, and embedded coding experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before.
flax - Flax is a neural network library for JAX that is designed for flexibility.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
dm-haiku - JAX-based neural network library
GnuplotCSharp - Make gnuplot graphs with C#, including by passing arrays (data) and using "hold on" to have several layers of graphs
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
F-a-maze-ing - Create aesthetic mazes of different shapes, tiles, sizes and more using a CLI tool. A website is also available at https://mazes.apixelinspace.com
treex - A Pytree Module system for Deep Learning in JAX
equinox - .NET event sourcing library with CosmosDB, DynamoDB, EventStoreDB, message-db, SqlStreamStore and integration test backends. Focused at stream level; see https://github.com/jet/propulsion for cross-stream projections/subscriptions/reactions
extending-jax - Extending JAX with custom C++ and CUDA code
MiniScaffold - F# Template for creating and publishing libraries targeting .NET 6.0 `net6.0` or console apps .NET 6.0 `net6.0`.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/