the-ray-tracer-challenge-fsharp
equinox
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the-ray-tracer-challenge-fsharp | equinox | |
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10 | 31 | |
19 | 1,789 | |
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4.0 | 9.3 | |
7 months ago | 6 days ago | |
F# | Python | |
MIT License | Apache License 2.0 |
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the-ray-tracer-challenge-fsharp
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Good book to learn F#?
Take a book that is project-based, such as The Ray Tracer Challenge, no matter what language it uses in the book, and start going through the book in F#. I have done so, for example, here is my in-work F# implementation for the ray tracer book.
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OCaml at First Glance
Yes, please do! Warning: F# will ruin other languages for you. I find it rather painful to work in basically anything else after using F#, with gradients of pain for different languages. Haha.
And that's a good question. I have basically every book written on F#, but I can't say I have ever used them for anything more than reference.
The official docs/guide/reference are actually really good, and I refer to them a lot when using some feature I'm not familiar with: https://docs.microsoft.com/en-us/dotnet/fsharp/what-is-fshar...
F# For Fun and Profit is well-known, but I can't say I use it a lot: https://fsharpforfunandprofit.com/
The same author's (Scott Wlaschin) book is very good: https://pragprog.com/titles/swdddf/domain-modeling-made-func...
As for books, I have always liked:
* Functional Programming Using F# by Hansen and Rischel (might be too simple if you are already comfortable with functional programming and is out of date every now and then with changes to F# that's happened)
* Expert F# 4.0 by Don Syme and others (contains a lot of nice things by the designer of F#
One of the latest books is Stylish F# 6: Crafting Elegant Functional Code for .NET 6 by Kit Eason. I have the first edition but haven't read it.
My personal recommendation is to take the approach of type/domain driven design. That is, I start off every F# module the same:
1. Define my types with discriminated unions, records, type aliases (such as for tuples) or single case discriminated unions. Use classes when necessary but try to prefer the more functional types.
2. Start writing functions against these.
And that's basically it. One thing to recognize with F# is that it mixes OOP rather nicely. Even discriminated unions and records, which are immutable, can have members defined on them, including operator overloading (something F# is pretty good about). They can even implement interfaces and be defined with generic types, which is also nice and powerful.
I have some projects that might of interest, since they're simple enough and illustrate the above process.
https://github.com/bmitc/the-ray-tracer-challenge-fsharp
https://github.com/bmitc/nand2tetris
Lastly, I'd suggest just starting up some projects. You could also take the Programming Languages course on Coursera by Dan Grossman. Part A uses SML, and you could port the examples and homework solutions to F# (I did so when I took the course). I also take books written for other languages and port the code to F#, usually taking a more idiomatic functional style. .NET Interactive notebooks (https://github.com/dotnet/interactive) are a great way to get started. You just need to install the .NET 6 SDK (which gets you F#) and then install the .NET Interactive Notebook extension in VS Code. That's it. There is also the book The Little MLer which gets people comfortable with discriminated unions (sum types), and I used the book and ported the examples to F#. I need to go back and finish that annotation project (https://github.com/bmitc/the-little-fsharper). I'll probably convert the script files to .NET Interactive notebooks if I do.
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What are you working on? (2022-07)
Here's mine, which is only about halfway through: https://github.com/bmitc/the-ray-tracer-challenge-fsharp
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Das.Test - an opinionated unit testing library written in F# for F#
I used FsUnit here: https://github.com/bmitc/the-ray-tracer-challenge-fsharp/tree/main/RayTracerChallenge/XUnitTests
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The joy of building a ray tracer, for fun, in Rust
Yes, the book has both implementations of the required functions (for all the complicated ones you need) and tests all written in pseudocode.
The book is really good. I have a half-finished implementation in F#, and what I find striking is just how close the F# code is to the pseudocode. I have also started an idiomatic port to Racket but have only done the tuples, vector, and point implementations so far. I need to pick these up again.
https://github.com/bmitc/the-ray-tracer-challenge-fsharp
https://github.com/bmitc/the-ray-tracer-challenge-racket
I mean, check this out: https://github.com/bmitc/the-ray-tracer-challenge-fsharp/blo...
I have also worked through pieces of Ray Tracing in One Weekend (what was referenced in this post). They get you going much faster, but the code is written in C++. I found the translation to a functional style was harder (was just using Racket and F#'s mutability features), whereas the way The Ray Tracer Challenge is laid out and specified, I found it much easier to translate to an idiomatic functional style.
- Really great example projects?
- Is F# Tough to Learn?
- What are F#'s advantages?
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Can anyone suggest any interesting F# projects?
Another project of mine is going through the book The Ray Tracer Challenge with F#. The book is language agnostic and represents the code with pseudocode and presents a test driven approach. My repository is here: https://github.com/bmitc/the-ray-tracer-challenge-fsharp
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Super simple ray tracer guide after first math semester
I highly recommend the two books:
Ray Tracing in One Weekend: https://www.amazon.com/dp/B01B5AODD8/
The Ray Tracer Challenge: https://www.amazon.com/dp/1680502719/
For the latter, I have made it a pretty good way through the book, implementing a functional (in the programming paradigm sense) version of the ray tracer in F#. It’s actually rather mind boggling how close the F# code and tests are to the psuedocode found in the book.
https://github.com/bmitc/the-ray-tracer-challenge-fsharp
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?
nand2tetris - Original course HDL solutions, F# implementations for the software stack, and VHDL implementations for the hardware stack for the nand2tetris course and The Elements of Computing Systems book.
flax - Flax is a neural network library for JAX that is designed for flexibility.
Bolero - Bolero brings Blazor to F# developers with an easy to use Model-View-Update architecture, HTML combinators, hot reloaded templates, type-safe endpoints, advanced routing and remoting capabilities, and more.
dm-haiku - JAX-based neural network library
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
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
the-ray-tracer-challenge-racket - Racket implementations of the ray tracer found in The Ray Tracer Challenge book by Jamis Buck.
treex - A Pytree Module system for Deep Learning in JAX
WebApiTest - Sustainable Web API written in F# that showcases the language's strengths via a functional style.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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extending-jax - Extending JAX with custom C++ and CUDA code