Examples Gallery¶
A couple of examples to illustrate different implementation of dot product (see also sphinx-gallery).
Getting started¶
pytorch nightly build should be installed, see Start Locally.
git clone https://github.com/sdpython/experimental-experiment.git
pip install onnxruntime-gpu nvidia-ml-py
pip install -r requirements-dev.txt
export PYTHONPATH=$PYTHONPATH:<this folder>
Compare torch exporters¶
The script evaluates the memory peak, the computation time of the exporters. It also compares the exported models when run through onnxruntime. The full script takes around 20 minutes to complete. It stores on disk all the graphs, the data used to draw them, and the models.
python _doc/examples/plot_torch_export.py -s large
See l-pytorch-onnx-examples for an organized version of this page.
101: Onnx Model Optimization based on Pattern Rewriting
101: Onnx Model Optimization based on Pattern Rewriting
201: Evaluate different ways to export a torch model to ONNX
201: Evaluate different ways to export a torch model to ONNX
201: Use torch to export a scikit-learn model into ONNX
201: Use torch to export a scikit-learn model into ONNX
