gopala-kr / nnabla

NNabla - Neural Network Libraries NNabla is a deep learning framework that is intended to be used for research, development and production. We aim it running everywhere like desktop PCs, HPC clusters, embedded devices and production servers.

NNabla - Neural Network Libraries

NNabla is a deep learning framework that is intended to be used for research, development and production. We aim it running everywhere like desktop PCs, HPC clusters, embedded devices and production servers.

Installation

Installing NNabla is easy:

pip install nnabla

This installs the CPU version of NNabla. GPU-acceleration can be added by installing the CUDA extension with pip install nnabla-ext-cuda.

Features

Easy, flexible and expressive

The Python API built on the NNabla C++11 core gives you flexibility and productivity. For example, a two layer neural network with classification loss can be defined in the following 5 lines of codes (hyper parameters are enclosed by <>).

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

x = nn.Variable(<input_shape>)
t = nn.Variable(<target_shape>)
h = F.tanh(PF.affine(x, <hidden_size>, name='affine1'))
y = PF.affine(h, <target_size>, name='affine2')
loss = F.mean(F.softmax_cross_entropy(y, t))

Training can be done by:

import nnabla.solvers as S

# Create a solver (parameter updater)
solver = S.Adam(<solver_params>)
solver.set_parameters(nn.get_parameters())

# Training iteration
for n in range(<num_training_iterations>):
    # Setting data from any data source
    x.d = <set data>
    t.d = <set label>
    # Initialize gradients
    solver.zero_grad()
    # Forward and backward execution
    loss.forward()
    loss.backward()
    # Update parameters by computed gradients
    solver.update()

The dynamic computation graph enables flexible runtime network construction. NNabla can use both paradigms of static and dynamic graphs, both using the same API.

x.d = <set data>
t.d = <set label>
drop_depth = np.random.rand(<num_stochastic_layers>) < <layer_drop_ratio>
with nn.auto_forward():
    h = F.relu(PF.convolution(x, <hidden_size>, (3, 3), pad=(1, 1), name='conv0'))
    for i in range(<num_stochastic_layers>):
        if drop_depth[i]:
            continue  # Stochastically drop a layer
        h2 = F.relu(PF.convolution(x, <hidden_size>, (3, 3), pad=(1, 1), 
                                   name='conv%d' % (i + 1)))
        h = F.add2(h, h2)
    y = PF.affine(h, <target_size>, name='classification')
    loss = F.mean(F.softmax_cross_entropy(y, t))
# Backward computation (can also be done in dynamically executed graph)
loss.backward()

Portable and multi-platform

• Python API can be used on Linux and Windows
• Most of the library code is written in C++11, deployable to embedded devices

Extensible

• Easy to add new modules like neural network operators and optimizers
• The library allows developers to add specialized implementations (e.g., for FPGA, ...). For example, we provides CUDA backend as an extension, which gives speed-up by GPU accelerated computation.

Efficient

• High speed on a single CUDA GPU
• Memory optimization engine
• Multiple GPU support (Available soon)

Documentation

https://nnabla.readthedocs.org

Setup

https://nnabla.readthedocs.io/en/latest/python/installation.html

Getting started

• A number of Jupyter notebook tutorials can be found in the tutorial folder. We recommend starting from by_examples.ipynb for a first working example in NNabla and python_api.ipynb for an introduction into the NNabla API.

• We also provide some more sophisticated examples in examples.