coremltools VS swift

Instead of trying to integrate the whole stack of, say, pytorch, Apple's primary approach has been converting models to work with Apple's stack.

https://github.com/apple/coremltools

Clearly no one is going to be doing training or even fine tuning on Apple hardware at any scale (it competes at the low end, but at scale you invariably will be using nvidia hardware), but once you have a decent model it's a robust way of using it on Apple devices.

There is one guy who was able to run it on iOS. See this thread for more information. Basically, the idea is to convert torch models to CoreMl. Only the CLIP tokenizer's implementation is currently missing. I guess this guy will keep modifications private, but he is trying to optimize model for lower RAM requirements.

Afaik (correct me if I’m wrong) both PyTorch and tensorflow only use the gpu when training and not the neural engine. I think the neural engines can be used for inference if the model is in the CoreML format (https://github.com/apple/coremltools)

CoreML Tools

And Python.

There's literally an Apple provided tool, called [coremltools[(https://github.com/apple/coremltools) to convert many common PyTorch and TensorFlow models to CoreML.

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

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.

Fortunately, the Observation framework is part of the Swift 5.9 standard library. We can learn more information by examining its source code.

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...

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.

Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.

You can do the same thing the other way around - https://github.com/apple/swift/blob/main/docs/Android.md.

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...

`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