An introduction to ONNX! Convert your ML models to an agnostic format and take advantage of speedier inference ⚡️.
See:
✨
pip install -r requirements.txt
Serve the Notebook as slides using:
jupyter nbconvert onnx-demo.ipynb \
--to slides \
--post serve \
--SlidesExporter.reveal_scroll=True \
--TagRemovePreprocessor.remove_input_tags={\"remove-input\"}
- The reference ONNX.js demo comes from: dunnkers/neural-network-backdoors
- Notebook partially from sklearn-onnx repo.
Slides built by Jeroen Overschie.