candlelighter
| Crates.io | candlelighter |
| lib.rs | candlelighter |
| version | 0.4.0 |
| created_at | 2024-01-05 15:08:18.971085+00 |
| updated_at | 2024-04-14 15:01:28.69957+00 |
| description | A Keras like abstraction layer on top of the Rust ML framework candle |
| homepage | https://github.com/BDUG/Lighter |
| repository | https://github.com/BDUG/Lighter |
| max_upload_size | |
| id | 1089809 |
| size | 8,408,886 |
(BDUG)
documentation
README
Rust Lighter
This project was started as my RUST exercise to abstract the Rust minimalist ML framework Candle (https://github.com/huggingface/candle) and introduce a more convenient way of programming neural network machine learning models.
The behaviour is inspired by Python KERAS (https://keras.io) and the initial step based on the Rust-Keras-like code (https://github.com/AhmedBoin/Rust-Keras-Like).
So let's call the project Candle Lighter 🕯, because it helps to turn on the candle light and is even easier to implement.
Examples can be found below the lib/examples/ directory.
To use it as library just call 'cargo add candlelighter'
CONTRIBUTORS ARE HIGHLY WELCOME
Note: It is by far not production ready and is only used for own training purposes. No warranty and liability is given. I am a private person and not targeting any commercial benefits.
Supported Layer types
| Meta Layer | Type | State | Example |
|---|---|---|---|
| Sequential model | - | ✅ | |
| - | Feature scaling | 🏃 | DNN and TNN |
| - | Dense | ✅ | DNN |
| - | Convolution | ✅ | CNN |
| - | Pooling | ✅ | - |
| - | Normalization | ✅ | - |
| - | Flatten | ✅ | - |
| - | Recurrent | ✅ | RNN 1st throw |
| - | Regulation | ✅ | - |
| - | Feature embedding | ✅ | S2S 1st throw |
| - | Attention | 🏃 | TNN 1st throw |
| - | Mixture of Experts | 🏃 | ENN 1st throw |
| - | Feature masking and -quantization | 🏃 | - |
| Parallel model (in sense of split) | - | 🏃 | PNN 1st throw |
| Parallel model | Merging | 🏃 | PNN 1st throw |
| - | Model fine tuning | 🏃 | - |
License
Tripple-licensed to be compatible with the Rust project and the source roots.
Licensed under the MPL 2.0, MIT license or the Apache license, Version 2.0 at your option.