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Optimizing Deep Learning models: theory, tools & best-practices (AI Day 2022)
In this repository you can find slides and demos for the Optimizing Deep Learning models: theory, tools & best-practices session, presented (in Italian) at AI Day 2022 Conference on November 18th, 2022. ...learn more
Project status: Published/In Market
Artificial Intelligence, Performance Tuning
Intel Technologies
Intel Arc,
Intel Integrated Graphics,
Intel CPU
Overview / Usage
The notebook shows how to take an ONNX model, convert/optimize it to OpenVINO IR format, and run it in the OpenVINO runtime. The optimized model is compared against the original ONNX model, for output compatibility and performance evaluation.
Requirements: Python 3.9.x, OpenVINO 2022.2, ONNX Runtime 1.13.1 (on Windows)
NVIDIA GPU (with CUDA 11.6 and cuDNN)
Setup environment following the official installation guide and the steps below to configure a Python Virtual Environment.
For additional details and other examples, please refer to the OpenVINO Notebooks repository. We used the 102-pytorch-onnx-to-openvino notebook as a starting point for this demo.
python -m venv .venv
..venv\scripts\activate
python -m pip install -U pip
pip install wheel
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install openvino-dev[onnx]==2022.2.0
pip install fastseg
pip install ipywidgets
pip install matplotlib
Methodology / Approach
Check slides on https://github.com/deltatrelabs/deltatre-aiday-2022-demo/tree/main/docs
Technologies Used
Intel OpenVino,
ONNX Runtime,
Nebuly,
etc etc
Documents and Presentations
Repository
https://github.com/deltatrelabs/deltatre-aiday-2022-demo
Collaborators