Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sony/nnabla

Neural Network Libraries
https://github.com/sony/nnabla

Last synced: 10 days ago
JSON representation

Neural Network Libraries

Awesome Lists containing this project

README 

        
# Neural Network Libraries



[Neural Network Libraries](https://arxiv.org/abs/2102.06725) is a deep learning framework that is intended to be used for research,

development and production. We aim to have it running everywhere: desktop PCs, HPC

clusters, embedded devices and production servers.



* [Neural Network Libraries - CUDA extension](https://github.com/sony/nnabla-ext-cuda): An extension library of Neural Network Libraries that allows users to speed-up the computation on CUDA-capable GPUs.

* [Neural Network Libraries - Examples](https://github.com/sony/nnabla-examples): Working examples of Neural Network Libraries from basic to state-of-the-art.

* [Neural Network Libraries - C Runtime](https://github.com/sony/nnabla-c-runtime):  Runtime library for inference Neural Network created by Neural Network Libraries.

* [Neural Network Libraries - NAS](https://github.com/sony/nnabla-nas):  Hardware-aware Neural Architecture Search (NAS) for Neural Network Libraries.

* [Neural Network Libraries - RL](https://github.com/sony/nnabla-rl):  Deep Reinforcement Learning (RL) library built on top of Neural Network Libraries.

* [Neural Network Console](https://dl.sony.com/): A Windows GUI app for neural network development.



## Installation



Installing Neural Network Libraries is easy:



```

pip install nnabla

```



This installs the CPU version of Neural Network Libraries. GPU-acceleration can be added by installing the CUDA extension with following command.

```

pip install nnabla-ext-cuda116

```

Above command is for version 11.6 CUDA Toolkit.



The other supported CUDA packages are listed [here](https://nnabla.readthedocs.io/en/latest/python/pip_installation_cuda.html#cuda-vs-cudnn-compatibility).



CUDA ver.10.x, ver.9.x, ver.8.x are not supported now.



For more details, see the [installation section](http://nnabla.readthedocs.io/en/latest/python/installation.html) of the documentation.



### Building from Source



See [Build Manuals](doc/build/README.md).



### Running on Docker

For details on running on Docker, see the [installation section](http://nnabla.readthedocs.io/en/latest/python/installation.html) of the documentation.



## Features



### Easy, flexible and expressive



The Python API built on the Neural Network Libraries 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 `<>`).



```python

import nnabla as nn

import nnabla.functions as F

import nnabla.parametric_functions as PF



x = nn.Variable()

t = nn.Variable()

h = F.tanh(PF.affine(x, , name='affine1'))

y = PF.affine(h, , name='affine2')

loss = F.mean(F.softmax_cross_entropy(y, t))

```



Training can be done by:



```python

import nnabla.solvers as S



# Create a solver (parameter updater)

solver = S.Adam()

solver.set_parameters(nn.get_parameters())



# Training iteration

for n in range():

    # Setting data from any data source

    x.d = 

    t.d = 

    # 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.

Neural Network Libraries can use both paradigms of static and dynamic graphs,

both using the same API.



```python

x.d = 

t.d = 

drop_depth = np.random.rand() < 

with nn.auto_forward():

    h = F.relu(PF.convolution(x, , (3, 3), pad=(1, 1), name='conv0'))

    for i in range():

        if drop_depth[i]:

            continue  # Stochastically drop a layer

        h2 = F.relu(PF.convolution(x, , (3, 3), pad=(1, 1), 

                                   name='conv%d' % (i + 1)))

        h = F.add2(h, h2)

    y = PF.affine(h, , name='classification')

    loss = F.mean(F.softmax_cross_entropy(y, t))

# Backward computation (can also be done in dynamically executed graph)

loss.backward()

```



You can differentiate to any order with nn.grad.



```python

import nnabla as nn

import nnabla.functions as F

import numpy as np



x = nn.Variable.from_numpy_array(np.random.randn(2, 2)).apply(need_grad=True)

x.grad.zero()

y = F.sin(x)

def grad(y, x, n=1):

    dx = [y]

    for _ in range(n):

        dx = nn.grad([dx[0]], [x])

    return dx[0]

dnx = grad(y, x, n=10)

dnx.forward()

print(np.allclose(-np.sin(x.d), dnx.d))

dnx.backward()

print(np.allclose(-np.cos(x.d), x.g))



# Show the registry status

from nnabla.backward_functions import show_registry

show_registry()

```



### Command line utility



Neural Network Libraries provides a command line utility `nnabla_cli` for easier use of NNL.



`nnabla_cli` provides following functionality.



- Training, Evaluation or Inference with NNP file.

- Dataset and Parameter manipulation.

- File format converter

  - From ONNX to NNP and NNP to ONNX.

  - From TensorFlow to NNP and NNP to TensorFlow.

  - From NNP to TFLite.

  - From ONNX or NNP to NNB or C source code.



For more details see [Documentation](doc/python/command_line_interface.rst)



### Portable and multi-platform



* Python API can be used on Linux and Windows

* Most of the library code is written in C++14, 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 provide 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



## Documentation



### Getting started



* A number of Jupyter notebook tutorials can be found in the [tutorial](https://github.com/sony/nnabla/tree/master/tutorial) folder.

  We recommend starting from `by_examples.ipynb` for a first

  working example in Neural Network Libraries and `python_api.ipynb` for an introduction into the

  Neural Network Libraries API.



* We also provide some more sophisticated examples at [`nnabla-examples`](https://github.com/sony/nnabla-examples) repository.



* C++ API examples are available in [`examples/cpp`](https://github.com/sony/nnabla/tree/master/examples/cpp).



## Contribution guide



The technology is rapidly progressing, and researchers and developers often want to add their custom features to a deep learning framework.

NNabla is really nice in this point. The architecture of Neural Network Libraries is clean and quite simple.

Also, you can add new features very easy by the help of our code template generating system.

See the following link for details.



* [Contribution guide](CONTRIBUTING.md)



## License & Notice



Neural Network Libraries is provided under the [Apache License Version 2.0](LICENSE) license.



It also depends on some open source software packages. For more information, see [LICENSES](third_party/LICENSES.md).



## Citation



```

@misc{hayakawa2021neural,

      title={Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives}, 

      author={Takuya Narihira and Javier Alonsogarcia and Fabien Cardinaux and Akio Hayakawa

              and Masato Ishii and Kazunori Iwaki and Thomas Kemp and Yoshiyuki Kobayashi

              and Lukas Mauch and Akira Nakamura and Yukio Obuchi and Andrew Shin and Kenji Suzuki

              and Stephen Tiedmann and Stefan Uhlich and Takuya Yashima and Kazuki Yoshiyama},

      year={2021},

      eprint={2102.06725},

      archivePrefix={arXiv},

      primaryClass={cs.LG}

}

```