SmallPebble
mercury-ad
| SmallPebble | mercury-ad | |
|---|---|---|
| 6 | 2 | |
| 112 | 6 | |
| - | - | |
| 0.0 | 10.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Mercury | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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SmallPebble
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Fastest Autograd in the West
You can implement autograd as a library. Just take a look at this
https://github.com/sradc/SmallPebble
The first line of the description is:
> SmallPebble is a minimal automatic differentiation and deep learning library written from scratch in Python, using NumPy/CuPy. -
Compiling ML models to C for fun
Thanks for this. My approach to speeding up an autodiff system like this was to write it in terms of nd-arrays rather than scalars, using numpy/cupy [1]. But it's still slower than deep learning frameworks that compile / fuse operations. Wondering how it compares to the approach in this post. (Might try to benchmark at some point.)
[1] https://github.com/sradc/SmallPebble
- Understanding Automatic Differentiation in 30 lines of Python
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[P] SmallPebble - minimal(/toy) deep learning framework written from scratch in Python, using NumPy/CuPy. <700 loc.
Located here: https://github.com/sradc/SmallPebble
- Show HN: I wrote a minimal(/toy) deep learning library from scratch in Python
- SmallPebble – Minimal automatic differentiation implementation in Python, NumPy
mercury-ad
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Understanding Automatic Differentiation in 30 lines of Python
I wrote a purely functional AD library in Mercury [0], which adapts a general approach from [1]. I believe that Owl provides a similar approach [2].
[0] https://github.com/mclements/mercury-ad
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Autodidax: Jax Core from Scratch (In Python)
I find the solutions from https://github.com/qobi/AD-Rosetta-Stone/ to be very helpful, particularly for representing forward and backward mode automatic differentiation using a functional approach.
I used this code as inspiration for a functional-only (without references/pointers) in Mercury: https://github.com/mclements/mercury-ad
What are some alternatives?
MyGrad - Drop-in autodiff for NumPy.
GPU-Puzzles - Solve puzzles. Learn CUDA.
chainer - A flexible framework of neural networks for deep learning
AD-Rosetta-Stone - Examples of Automatic Differentiation (AD) in many different languages and systems
memoized_coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers
autodidact - A pedagogical implementation of Autograd
Tensor-Puzzles - Solve puzzles. Improve your pytorch.
autograd - Efficiently computes derivatives of numpy code.
owl - Owl - OCaml Scientific Computing @ https://ocaml.xyz