best-of-ml-python
tinygrad
| best-of-ml-python | tinygrad | |
|---|---|---|
| 16 | 58 | |
| 15,335 | 17,800 | |
| 0.7% | - | |
| 7.8 | 9.7 | |
| 8 days ago | 10 months ago | |
| Python | Python | |
| Creative Commons Attribution Share Alike 4.0 | 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.
best-of-ml-python
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Ask HN: How to get back into AI?
For Python, here's a nice compilation: https://github.com/ml-tooling/best-of-ml-python/blob/main/RE...
- Best-Of Machine Learning with Python
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Questions regarding Job Requirements for data analyst to data science transition?
You will need numpy, scipy, pandas, scikit-learn, Keras/tensorflow/pytorch, xgboost and many many many others. See this list for example.
- Awesome list of ML
- Are there any speech recognition modules so I can write one and do not have to rely on google and the likes?
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Learning opencv
Take a look at this list on github. It has a pretty comprehensive list of python image libraries.
- Best-of Machine Learning with Python
- 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
tinygrad
- tinygrad: extreme simplicity, easiest framework to add new accelerators to
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GGML – AI at the Edge
Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?
[0] https://github.com/geohot/tinygrad
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Render neural network into CUDA/HIP code
at first glance i thought may its like tinygrad. but looks has many ops than that tiny grad but most maps to underlying hardware provided ops?
i wonder how well tinygrad's apporach will work out, ops fusion sounds easy, just a walk a graph, pattern match it and lower to hardware provided ops?
Anyway if anyone wants to understand the philosophy behind tinygrad, this file is great start https://github.com/geohot/tinygrad/blob/master/docs/abstract...
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
- George Hotz building an AMD competitor to Nvidia.
-
George Hotz ROCm adventures
Hopefully we will see now full support with AMD hardware on https://github.com/geohot/tinygrad. You can read more about it on https://tinygrad.org/
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The Coming of Local LLMs
tinygrad
https://github.com/geohot/tinygrad/tree/master/accel/ane
But I have not tested it on Linux since Asahi has not yet added support.
llama.cpp runs at 18ms per token (7B) and 200ms per token (65B) without quantization.
- Everything we know about Apple's Neural Engine
- Everything we know about the Apple Neural Engine (ANE)
- How 'Open' Is OpenAI, Really?
What are some alternatives?
Awesome-WAF - 🔥 Web-application firewalls (WAFs) from security standpoint.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
llama.cpp - LLM inference in C/C++
dtale - Visualizer for pandas data structures
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.
ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)
llama - Inference code for Llama models
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ