What is it¶

nnio is a light-weight python package for easily running neural networks.

It supports running models on CPU as well as some of the edge devices:

For each device there exists an own library and a model format. We wrap all those in a single well-defined python package.

Look at this simple example:

import nnio

# Create model and put it on a Google Coral Edge TPU device
model = nnio.EdgeTPUModel(
    model_path='path/to/model_quant_edgetpu.tflite',
    device='TPU',
)
# Create preprocessor
preproc = nnio.Preprocessing(
    resize=(224, 224),
    batch_dimension=True,
)

# Preprocess your numpy image
image = preproc(image_rgb)

# Make prediction
class_scores = model(image)

nnio was developed for the Fast Sense X microcomputer. It has six neural accelerators, which are all supported by nnio:

3 x Google Coral Edge TPU
2 x Intel Myriad VPU
an Intel integrated GPU

Installation¶

nnio is simply installed with pip, but it requires some additional libraries. See Installation.

Usage¶

There are 3 ways one can use nnio:

Loading your saved models for inference - Basic Usage
Using already prepared models from our model zoo: Model Zoo
Using our API to wrap around your own custom models. Extending nnio

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