tensorflow_macos
tinygrad
tensorflow_macos | tinygrad | |
---|---|---|
33 | 17 | |
2,887 | 24,018 | |
- | 3.3% | |
3.4 | 10.0 | |
almost 3 years ago | 4 days ago | |
Shell | Python | |
GNU General Public License v3.0 or later | MIT 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.
tensorflow_macos
-
Updated Apple Silicon Guide for M2 Pro and M2 Max Chips
https://github.com/apple/tensorflow_macos is no longer needed
-
The hunt for the M1βs neural engine
Tensorflow has a CoreML enabled version which run on ANE.
https://github.com/apple/tensorflow_macos
-
M1 Mac users
Apple released a guide on how to use the M1's integrated Neural Chip in TensorFlow. Have a look at this Apple documentation page (and maybe also this GitHub that talks about TensorFlow together with Apple's own ML Compute platform).
-
MacBook Air or Wait for new potential MacBook Air with M2
Tensorflow does work on Apple Silicon
- Kernels dying when using tensorflow in Jupyter Notebooks.
-
Main PyTorch maintainer confirms that work is being done to support Apple Silicon GPU acceleration for the popular machine learning framework.
Apple did some work to optimize tensorflow for M1, can be found here https://github.com/apple/tensorflow_macos It's alpha, but works fine, I tried it
-
The M1 Max is the fastest GPU we have ever measured in Affinity Photo benchmark
https://github.com/apple/tensorflow_macos/issues/25
https://forums.macrumors.com/threads/apple-silicon-deep-lear...
It is expected that the M1 Max should have similar performance to a RTX-2080 or Titan X.
-
MacBook Pro M1 Pro benchmark
In case anyone is interested, in ran a fairly simple MNIST benchmark (proposed here : https://github.com/apple/tensorflow_macos/issues/25) on my recently acquired M1 Pro MBP (16-core GPU, 16GB RAM).
-
Error while installing tensorflow on Mac M1
The only method I know of to download tensorflow on M1 macs is the one documented here: https://github.com/apple/tensorflow_macos
- How exactly does the Neural Engine benefit the consumer?
tinygrad
-
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
-
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.
-
[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
-
AMD MI300 Performance β 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.
-
π» 7 Open-Source DevTools That Save Time You Didn't Know to Exist βπ
π Support on GitHub Website: https://tinygrad.org/
- Tinygrad
-
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.
-
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?
miniforge - A conda-forge distribution.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
tinygrad - You like pytorch? You like micrograd? You love tinygrad! β€οΈ [Moved to: https://github.com/tinygrad/tinygrad]
llama.cpp - LLM inference in C/C++
ROCm - AMD ROCmβ’ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
llama - Inference code for Llama models
flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
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.
Python-docker - Docker Official Image packaging for Python
stable-diffusion.cpp - Stable Diffusion in pure C/C++