Neural network inference on accelerators simplified
Project description
nnio
Please refer to the project's documentation.
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:
- Google USB Accelerator
- Intel Compute Stock
- Intel integrated GPUs
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 VPU
- an integrated Intel GPU
More usage examples can be found in the documentation.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
nnio-0.2.4.3.tar.gz
(13.5 kB
view hashes)