swift | swift | |
---|---|---|
16 | 215 | |
6,052 | 66,003 | |
- | 0.5% | |
0.0 | 10.0 | |
over 2 years ago | 1 day ago | |
Jupyter Notebook | C++ | |
Apache License 2.0 | 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.
swift
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Show HN: Designing Bridges with PyTorch
I remember several years ago when differentiable programming was an object of interest to the programming community and Lattner was trying to make Swift for Tensorflow happen[1].
I'm of the opinion that it was ahead of its time: Swift hadn't (and still hasn't) made enough progress on Linux support for it to be taken seriously as a language for writing anything that isn't associated with Apple. However, as a result, Swift now has language-level differentiability in its compiler. I'd love to see Swift get used for projects like this, but I suppose the reality of the matter is that there are so many performant runtimes for 2D/3D physics that there just isn't much of a need for automatic differentiation (and its overhead) to solve these problems. The tooling nerd in me thinks this stuff is fascinating.
https://github.com/tensorflow/swift
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Can Swift be used for Data Science?
there was a time when google attempted to integrate swift with tensorflow, but the project was abandoned, and the repo is archived now. I believe the swift community picked up some of the features, and they are still working on it.
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Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia - Stochastic Lifestyle
Apple really is focusing on CoreML rather than differentiable swift, that was more of the vision of Swift4TF, which really was driven mostly by Google, until it was cancelled (I assume because of Chris Latner leaving google for SiFive): https://github.com/tensorflow/swift
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Swift on the Server in 2020
to be fair, Swift for Tensorflow was dropped (Feb 21) way after this article was written (Aug 20) https://github.com/tensorflow/swift
- Flashlight: Fast and flexible machine learning in C++
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Swift for TensorFlow Shuts Down
Neat! This may have not been well known when they kicked off the project and wrote their reasoning. Here is what they had to say about Scala at the time of the document linked up-thread[0]:
"Java / C# / Scala (and other OOP languages with pervasive dynamic dispatch): These languages share most of the static analysis problems as Python: their primary abstraction features (classes and interfaces) are built on highly dynamic constructs, which means that static analysis of Tensor operations depends on "best effort" techniques like alias analysis and class hierarchy analysis. Further, because they are pervasively reference-based, it is difficult to reliably disambiguate pointer aliases."
If they were wrong about that, or if the state of the art has progressed in the meantime, that's great! You may well be right that Scala would be a good / the best choice if they started the project today.
[0]: https://github.com/tensorflow/swift/blob/main/docs/WhySwiftF...
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Swift for TensorFlow in Archive Mode
It was not in the README
swift
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Evolving the Go Standard Library with math/rand/v2
This algorithm produces biased result with probability 1/2^(32-bitwidth(N)). Using 64 or 128 random bits can make the bias practically undetectable. Comprehensive overview of the approach can be found here: https://github.com/apple/swift/pull/39143
- Swift: Differentiable Programming Manifesto
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Embedded Swift on the Raspberry Pi Pico
Because of C/C++ interop, and integration with CMake, you can just add Swift to a Zephyr project and it pretty much Just Works. [The docs](https://github.com/apple/swift/blob/main/docs/EmbeddedSwift/...) should mostly apply to the Zephyr SDK as well.
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A Deep Dive Into Observation: A New Way to Boost SwiftUI Performance
Fortunately, the Observation framework is part of the Swift 5.9 standard library. We can learn more information by examining its source code.
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Swift was always going to be part of the OS
They do! See https://github.com/apple/swift/blob/main/docs/LibraryEvoluti...
You can also see an example of what a different high level language integration with Swift ABI looks like here: https://github.com/dotnet/designs/blob/main/proposed/swift-i...
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Differentiable Swift
So is differentiable Swift a package for Swift or is it part of the Swift standard library? The video says go to swift.org but I can't find any info about differentiable Swift on that site.
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.
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Kotlin Multiplatform for Android and iOS Apps
You can do the same thing the other way around - https://github.com/apple/swift/blob/main/docs/Android.md.
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This isn’t the way to speed up Rust compile times
Codable (along with other derived conformances like Equatable, Hashable, and RawRepresentable) is indeed built in to the compiler[0], but unlike Serde, it operates during type-checking on a fully-constructed AST (with access to type information), manipulating the AST to insert code. Because it operates at a later stage of compilation and at a much higher level (with access to type information), the work necessary is significantly less.
With ongoing work for Swift macros, it may eventually be possible to rip this code out of the compiler and rewrite it as a macro, though it would need to be a semantic macro[1] rather a syntactic one, which isn't currently possible in Swift[2].
[0] https://github.com/apple/swift/blob/main/lib/Sema/DerivedCon...
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How does Swift implement primitive types in its standard library?
`Int` is a regular struct with a single stored property of type `Builtin.Word` . But the latter is a magical compiler built-in. Source for integer types is generated from this template - https://github.com/apple/swift/blob/9da65ca0a15fdf341649c994b0a77ec3b71f2687/stdlib/public/core/IntegerTypes.swift.gyb
What are some alternatives?
julia - The Julia Programming Language
solidity - Solidity, the Smart Contract Programming Language
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
cpp-lazy - C++11/14/17/20 library for lazy evaluation
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
dataenforce - Python package to enforce column names & data types of pandas DataFrames
tree-sitter - An incremental parsing system for programming tools
Vapor - 💧 A server-side Swift HTTP web framework.
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
smoke-framework - A light-weight server-side service framework written in the Swift programming language.
lobster - The Lobster Programming Language