collenchyma-NN provides Neural Network related algorithms for Collenchyma. Run NN operations on servers, desktops or mobiles, GPUs, FPGAs or CPUS, without carrying about OpenCL or CUDA support on the machine.
collenchyma-NN was started at Autumn to support the Machine Intelligence Framework Leaf with backend-agnostic, state-of-the-art performance.
For more information,
This Plugins provides the following operations to the Collenchyma Backend.
Every Operation includes forward + backward. A - means not yet implemented.
More information can be found in the Documentation.
| Operation | CUDA | OpenCL | Native | |--- |--- |--- |--- | | Sigmoid | cuDNN v3 | - | Rust | | ReLU | cuDNN v3 | - | Rust | | Tanh | cudNN v3 | - | Rust | | | | | | | Normalization (LRN) | cudNN v3 | - | - | | | | | | | Convolution | cudNN v3 | - | - | | | | | | | Softmax | cudNN v3 | - | Rust | | LogSoftmax | cudNN v3 | - | Rust | | | | | | | Pooling Max | cudNN v3 | - | - | | Pooling Avg | cudNN v3 | - | - |
Kudos to ehiggs, for implementing the native Rust operations.
If you're using Cargo, just add collenchyma-NN to your Cargo.toml:
[dependencies]
collenchyma = "0.0.8"
collenchyma-nn = "0.3.0"
If you're using Cargo Edit, you can call:
$ cargo add collenchyma-nn
Bring the Plugin trait and the other important Collenchyma traits/structs in scope and you will be able to execute the here provided operations on your Collenchyma Backend.
rust
extern crate collenchyma as co;
extern crate collenchyma_nn as nn;
use co::backend::{Backend, BackendConfig};
use co::framework::IFramework;
use co::frameworks::Cuda;
use co::tensor::SharedTensor;
use nn::*;
fn main() {
// Initialize a CUDA Backend.
// Usually you would not use CUDA but let it pick what is available on the machine.
let framework = Cuda::new();
let hardwares = framework.hardwares();
let backend_config = BackendConfig::new(framework, hardwares);
let backend = Backend::new(backend_config).unwrap();
// Initialize two SharedTensors.
// Usually you would want also fill them with data.
let mut x = SharedTensor::<f32>::new(backend.device(), &(1, 1, 3)).unwrap();
let mut result = SharedTensor::<f32>::new(backend.device(), &(1, 1, 3)).unwrap();
// Use the operation provided by this Plugin.
backend.sigmoid(&mut x, &mut result);
}
Want to contribute? Awesome! We have instructions to help you get started contributing code or documentation. And high priority issues, that we could need your help with.
We have a mostly real-time collaboration culture and happens here on Github and on the Collenchyma Gitter Channel. You can also reach out to the Maintainers {@MJ, @hobofan}.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as below, without any additional terms or conditions.
You can find the release history at the CHANGELOG file.
A changelog is a log or record of all the changes made to a project, such as a website or software project, usually including such records as bug fixes, new features, etc. - Wikipedia
We are using Clog, the Rust tool for auto generating CHANGELOG files.
Licensed under either of
at your option.