Crate coaster_nn [−] [src]
Provides a Coaster Plugin, to extend Coaster with Neural Network related operations such as convolutions, pooling, ReLU, etc. A full list of operations provided by this Plugin, can be found at the provided Operations section.
Overview
This Coaster Plugin extends Coaster's Backend with NN related methods/operations. This allows you to run, these operations (and therefore your application) on your local machine as well as on servers, mobiles or any other machine (as if they were written for common CPU execution), while receiving the significant performance increases (usually one-to-two orders of magnitutde), by executing the operations on special purpose hardware such as GPUs - if they are available. Usage examples can be found in the next section.
The architecture of a Plugin is quite easy. It defines one Plugin Trait, in this case the NN
trait, which implements basic functionality for initialization and multiple Plugin Operation Traits which define the
methods which are going to be available on the Backed, as the Plugin Trait as well as the Plugin Operations Traits
are implemented for the Coaster Backends (CUDA, OpenCL, Native). The operations take as arguments one or many
SharedTensors, holding the data over which the operation should happen, and none or one Operation Configuration.
Usage
An example on how to write some data into a SharedTensor and compute the result of the sigmoid function for each value:
extern crate coaster as co; extern crate coaster_nn as nn; use co::prelude::*; use co::frameworks::native::flatbox::FlatBox; use nn::*; fn write_to_memory<T: Copy>(mem: &mut FlatBox, data: &[T]) { let mut mem_buffer = mem.as_mut_slice::<T>(); for (index, datum) in data.iter().enumerate() { mem_buffer[index] = *datum; } } pub fn main() { // Initialize a CUDA Backend. // Usually you would not use CUDA but let Coaster pick what is available on the machine. let backend = Backend::<Cuda>::default().unwrap(); // Initialize two SharedTensors. let mut x = SharedTensor::<f32>::new(&(1, 1, 3)); let mut result = SharedTensor::<f32>::new(&(1, 1, 3)); // Fill `x` with some data. let payload: &[f32] = &::std::iter::repeat(1f32).take(x.capacity()).collect::<Vec<f32>>(); let native = Native::new(); let cpu = native.new_device(native.hardwares()).unwrap(); write_to_memory(x.write_only(&cpu).unwrap(), payload); // Write to native host memory. // Run the sigmoid operation, provided by the NN Plugin, on your CUDA enabled GPU. backend.sigmoid(&mut x, &mut result).unwrap(); // See the result. println!("{:?}", result.read(&cpu).unwrap().as_slice::<f64>()); }
Provided Operations
This Plugins provides the following operations. If not denoted otherwise, this means forward and backward
A -
means not yet implemented.
Operation | CUDA | OpenCL | Native |
---|---|---|---|
Sigmoid | cuDNN v5 or later | - | Rust |
SigmoidPointwise | cuDNN v5 or later | - | Rust |
ReLU | cuDNN v5 or later | - | Rust |
ReLUPointwise | cuDNN v5 or later | - | Rust |
Tanh | cuDNN v5 or later | - | Rust |
TanhPointwise | cuDNN v5 or later | - | Rust |
Normalization (LRN) | cuDNN v5 or later | - | - |
Convolution | cuDNN v5 or later | - | Rust(fwd) |
Softmax | cuDNN v5 or later | - | Rust |
LogSoftmax | cuDNN v5 or later | - | Rust |
Pooling Max | cuDNN v5 or later | - | Rust(fwd) |
Pooling Avg | cuDNN v5 or later | - | - |
Modules
frameworks |
Provides the specific Framework implementations for the Library Operations. |
Macros
impl_ops_log_softmax_for | |
impl_ops_lrn_for | |
impl_ops_relu_for | |
impl_ops_sigmoid_for | |
impl_ops_softmax_for | |
impl_ops_tanh_for |
Enums
ConvBackwardDataAlgo |
Different algorithms to compute the gradient with respect to the filter. |
ConvBackwardFilterAlgo |
Different algorithms to compute the gradient with respect to the filter. |
ConvForwardAlgo |
Different algorithms to compute the convolution forward algorithm. |
Traits
Convolution |
Provides the functionality for a Backend to support Convolution operations. |
ConvolutionConfig |
Provides Convolution Config functionality. |
LRN |
Provides the functionality for a Backend to support Local Response Normalization operations. |
LogSoftmax |
Provides the functionality for a Backend to support LogSoftmax operations. |
NN |
Provides the functionality for a backend to support Neural Network related operations. |
NNOperationConfig |
Provides generic NN Operation Config functionality. |
Pooling |
Provides the functionality for a Backend to support Pooling operations. |
Relu |
Provides the functionality for a Backend to support ReLU operations. |
ReluPointwise |
Provides the functionality for pointwise ReLU operations (overwrites the input with the result of the operation). |
Sigmoid |
Provides the functionality for a Backend to support Sigmoid operations. |
SigmoidPointwise |
Provides the functionality for pointwise Sigmoid operations (overwrites the input with the result of the operation). |
Softmax |
Provides the functionality for a Backend to support Softmax operations. |
Tanh |
Provides the functionality for a Backend to support TanH operations. |
TanhPointwise |
Provides the functionality for pointwise ReLU operations (overwrites the input with the result of the operation). |