https://github.com/jafagervik/zybel
AI/ML library in the making, all written in zig
https://github.com/jafagervik/zybel
ai dl dnn ml zig-library zig-package
Last synced: 6 months ago
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AI/ML library in the making, all written in zig
- Host: GitHub
- URL: https://github.com/jafagervik/zybel
- Owner: Jafagervik
- License: mit
- Created: 2025-01-24T15:49:19.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T01:08:10.000Z (8 months ago)
- Last Synced: 2025-02-06T12:19:16.292Z (8 months ago)
- Topics: ai, dl, dnn, ml, zig-library, zig-package
- Language: Zig
- Homepage:
- Size: 40 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
> [!WARNING]
> **zybel** is still WIP. **Breaking changes** may occur
# Zinyn - ML and AI made possible for fun
## Goal
Create a framework for training DNNs in Zig (using hardware accelerators)
and compare results
## Why?
To quote a wise man: "For the joy of programming!"
## Features
- [x] Generic tensor using comptime
- [x] Binops such as add, sub, mul, div
- [x] General tensor ops (clamp, reshape, sum, min, max...)
- [x] SGD optimizer
- [x] Common loss functions such as mse and mae + 3 more
- [ ] Activation functions
- [ ] Simple layers
- [ ] Autograd
- [ ] Computational graph
- [ ] Hardware acceleration support
## Install (Per 0.14 beta)
Run this command in the parent directory of your project
```sh
zig fetch --save git+https://github.com/Jafagervik/zybel.git
```
Then add these lines to build.zig before b.installArtifact(exe)
```zig
const zybel = b.dependency("zybel", .{});
exe.root_module.addImport("zybel", zybel.module("zybel"));
```
## Example
```zig
const std = @import("std");
const zb = @import("zybel");
const Tensor = zb.Tensor;
const TF32 = Tensor(f32);
pub fn main() !void {
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
const allocator = gpa.allocator();
defer _ = gpa.deinit();
// Creates a F32 Tensor of shape (1, 3, 3)
var t: TF32 = try TF32.ones(allocator, &[_]u32{ 1, 3, 3 });
defer t.deinit();
std.debug.print("First value is {d:.2}\n", .{t.getFirst()});
bon.setVal(0, 2.0);
std.debug.print("First value is now {d:.2}\n", .{t.getFirst()});
// Prints input about the tensor
t.print();
std.debug.print("Sum is {d:.2}\n", .{t.sum()});
}
```
### Contribute
Hell yea! Feel free to add suggestions/corrections. I am a mere mortal, not a god