coremltools
swift-evolution
| coremltools | swift-evolution | |
|---|---|---|
| 11 | 124 | |
| 4,063 | 15,014 | |
| 1.3% | 0.4% | |
| 8.7 | 9.7 | |
| 11 days ago | 7 days ago | |
| Python | Markdown | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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.
coremltools
- CoreML commit from Apple mentions iOS17 exclusive features
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Lisa Su Saved AMD. Now She Wants Nvidia's AI Crown
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.
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Stable Diffusion for M1 iPad
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.
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MacBook Pro 14” M1 Pro (worth buying for programming)
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)
- Is it possible to convert a yolov5 model to a CoreML/.mlmodel to work in an IOS app?
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ML model conversion
CoreML Tools
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Supreme Court, in a 6–2 ruling in Google v. Oracle, concludes that Google’s use of Java API was a fair use of that material
And Python.
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Apple’s New M1 Chip is a Machine Learning Beast
There's literally an Apple provided tool, called [coremltools[(https://github.com/apple/coremltools) to convert many common PyTorch and TensorFlow models to CoreML.
swift-evolution
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Byte-Sized Swift: Building Tiny Games for the Playdate
[A Vision for Embedded Swift](https://github.com/apple/swift-evolution/blob/main/visions/e...) has the details on this new build mode and is quite interesting.
> Effectively, there will be two bottom layers of Swift, and the lower one, “non-allocating” Embedded Swift, will necessarily be a more restricted compilation mode (e.g. classes will be disallowed as they fundamentally require heap allocations) and likely to be used only in very specialized use cases. “Allocating” Embedded Swift should allow classes and other language facilities that rely on the heap (e.g. indirect enums).
Also, this seems to maybe hint at the Swift runtime eventually being reimplemented in non-allocating Embedded Swift rather than the C++ (?) that it uses now:
> The Swift runtime APIs will be provided as an implementation that’s optimized for small codesize and will be available as a static library in the toolchain for common CPU architectures. Interestingly, it’s possible to write that implementation in “non-allocating” Baremetal Swift.
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Borrow Checking Without Lifetimes
I may be out of my depth here as I've only casually used Rust, but this seems similar to Swift's proposed lifetime dependencies[1]. They're not in the type system formally so maybe they're closer to poloneius work
[1]: https://github.com/apple/swift-evolution/blob/3055becc53a3c3...
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Functional Ownership Through Fractional Uniqueness
Swift recently adopted a region-based approach for safe concurrency that builds on Milano et al’s ideas: https://github.com/apple/swift-evolution/blob/main/proposals...
- Swift-evolution/proposals/0373-vars-without-limits-in-result-builders.md
- The Swift proposal that removed the ++ and –- operators (2017)
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Crafting Self-Evident Code with D
No, it's not. Refcounting CAN be a garbage collection algorithm, but in Swift it's deterministic and done at compile time. Not to mention recently added support for non-copyable types that enforces unique ownership: https://github.com/apple/swift-evolution/blob/main/proposals...
- Statically link Swift runtime libraries by default on supported platforms
- (5.9) What is the point of a SerialExecutor that can silently re-order jobs?
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Mac shipments grow 10%, as all major PC brands see downturns.
You can stackallocate buffers with unsafe Swift but it's not exactly fun to use. https://github.com/apple/swift-evolution/blob/main/proposals/0322-temporary-buffers.md
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Can someone explain how Task really works in terms of threads (I couldnt ask all the questions with the swift team today)?
If the docs do not suffice, read the concurrency proposals of Swift Evolution. The authors describe the semantics in a very detailed way there.
What are some alternatives?
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
compose-multiplatform - Compose Multiplatform, a modern UI framework for Kotlin that makes building performant and beautiful user interfaces easy and enjoyable.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
foundationdb - FoundationDB - the open source, distributed, transactional key-value store
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
kotlinx-datetime - KotlinX multiplatform date/time library
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
okio - A modern I/O library for Android, Java, and Kotlin Multiplatform.
MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
PeopleInSpace - Kotlin Multiplatform project with SwiftUI, Jetpack Compose, Compose for Wear, Compose for Desktop, Compose for Web and Kotlin/JS + React clients along with Ktor backend.
password-manager-resources - A place for creators and users of password managers to collaborate on resources to make password management better.
swift-algorithms - Commonly used sequence and collection algorithms for Swift