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.
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.
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
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
- 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.
- High speed on a single CUDA GPU
- Memory optimization engine
- Multiple GPU support (Available soon)
A number of Jupyter notebook tutorials can be found in the
tutorialfolder. We recommend starting from
by_examples.ipynbfor a first working example in NNabla and
python_api.ipynbfor an introduction into the NNabla API.
We also provide some more sophisticated examples in