coremltools
Pytorch
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coremltools | Pytorch | |
---|---|---|
11 | 338 | |
4,063 | 77,783 | |
2.9% | 2.4% | |
8.7 | 10.0 | |
7 days ago | 6 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 1-Clause License |
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.
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.
Pytorch
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
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Building a GPT Model from the Ground Up!
import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
What are some alternatives?
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
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.
flax - Flax is a neural network library for JAX that is designed for flexibility.
password-manager-resources - A place for creators and users of password managers to collaborate on resources to make password management better.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more