.. _l-tutorials: Tutorials ========= .. contents:: :local: ONNX ecosystem ++++++++++++++ Following tutorials introduce the :epkg:`ONNX` ecosystem. It walk the user through the ONNX specficiations, how to execute an ONNX graph, how to create an ONNX graph, how to convert a model from :epkg:`scikit-learn`, and how to train them with :epkg:`onnxruntime-training`. .. toctree:: :maxdepth: 2 tutorial_onnx/index tutorial_onnxruntime/index tutorial_skl/index tutorial_training/index tutorial_bench/index tutorial_parallel/index Readings ++++++++ * `Add AI to mobile applications with Xamarin and ONNX Runtime `_ * `Announcing ONNX Runtime Availability in the NVIDIA Jetson Zoo for High Performance Inferencing `_ (8/2021) * `Speeding Up Deep Learning Inference Using TensorFlow, ONNX, and NVIDIA TensorRT `_ (7/2021) * `Journey to optimize large scale transformer model inference with ONNX Runtime `_ (6/2021) * `Accelerating Model Training with the ONNX Runtime `_ (5/2020) * `Accelerate and simplify Scikit-learn model inference with ONNX Runtime `_ (12/2020) * `Model Persistence scikit-learn and ONNX `_, short talk at `scikit-learn foundation `_ (2019) Current documention of ONNX and onnxruntime +++++++++++++++++++++++++++++++++++++++++++ Most of the documentation related on :epkg:`onnx` and :epkg:`onnxruntime` is written on :epkg:`markdown`. The following section is an attempt to render it and make it searchable. .. toctree:: :maxdepth: 2 onnxmd/index Build +++++ Some useful pages. * :ref:`Build onnxruntime on WSL (Windows Linux Subsystem) (2021) `.