SmallPebble
A minimalist deep learning library written from scratch in Python (by sradc)
chainer
A flexible framework of neural networks for deep learning (by chainer)
| SmallPebble | chainer | |
|---|---|---|
| 7 | 2 | |
| 133 | 5,917 | |
| 0.0% | -0.2% | |
| 8.5 | 0.0 | |
| 5 months ago | almost 3 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
SmallPebble
Posts with mentions or reviews of SmallPebble.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-24.
- Show HN: SmallPebble – minimalist deep learning library in <1000 lines of Python
-
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
-
[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
chainer
Posts with mentions or reviews of chainer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-19.
-
ChaiNNer – Node/Graph based image processing and AI upscaling GUI
There is already an AI framework named Chainer: https://github.com/chainer/chainer
-
Protip: the upscaler matters a lot
Sorry maybe someone could chime in and help but I use chainer to upscale. https://github.com/chainer/chainer
What are some alternatives?
When comparing SmallPebble and chainer you can also consider the following projects:
GPU-Puzzles - Solve puzzles. Learn CUDA.
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
MyGrad - Drop-in autodiff for NumPy.
leptonai - A Pythonic framework to simplify AI service building
Tensor-Puzzles - Solve puzzles. Improve your pytorch.
pytortto - deep learning from scratch. uses numpy/cupy, trains in GPU, follows pytorch API