TorchML__MD_000000000000_000
tinygrad
TorchML__MD_000000000000_000 | tinygrad | |
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1 | 24 | |
4 | 24,539 | |
- | 2.1% | |
10.0 | 10.0 | |
5 months ago | 1 day ago | |
C++ | Python | |
- | MIT License |
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TorchML__MD_000000000000_000
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
We wanted to use ONNX runtime for a "model driver" for MD simulations, where any ML model can be used for molecular dynamics simulations. Problem was it was way too immature. Like ceiling function will only work with single precision in ONNX. But the biggest issue was that we could not take derivatives in ONNX runtime, so any complicated model that uses derivatives inside was a nogo, is that limitation still exist? Do you know if it can take derivatives in training mode now?
Eventually we went with pytorch only support for the time being, with still exploring OpenXLA in place of ONNX, as a universal adapter: https://github.com/ipcamit/colabfit-model-driver
tinygrad
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AMD Unveils Ryzen 8000G Series Processors: Zen 4 APUs for Desktop with Ryzen AI
Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.
There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)
0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...
1. https://gpuopen.com/learn/wmma_on_rdna3/
2. https://github.com/ROCm/HIPCC
3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...
- Tinygrad 0.8.0 Release
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations
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AMD MI300 Performance – Faster Than H100, but How Much?
The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.
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💻 7 Open-Source DevTools That Save Time You Didn't Know to Exist ⌛🚀
🌟 Support on GitHub Website: https://tinygrad.org/
- Tinygrad
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How to train an Iris dataset classifier with Tinygrad
Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.
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Decomposing Language Models into Understandable Components
Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.
You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.
Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.
1: https://github.com/tinygrad/tinygrad
2: https://github.com/ggerganov/llama.cpp
- Tinygrad 0.7.0
What are some alternatives?
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
onnx - Open standard for machine learning interoperability
llama.cpp - LLM inference in C/C++
llama - Inference code for Llama models
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
stable-diffusion.cpp - Stable Diffusion in pure C/C++
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
hlb-CIFAR10 - Train CIFAR-10 in <7 seconds on an A100, the current world record.