renovate-bot / 10imaging-_-onnx

Open Neural Network Exchange

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Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation).

Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.

Use ONNX

Start experimenting today:

• Getting Started Guide
• Supported Frameworks & Tools
• Tutorials on using ONNX converters.

Learn about ONNX spec

Check ONNX design choices and internals:

• Overview
• ONNX intermediate representation spec
• Versioning principles of the spec
• Operators documentation
• Python API Overview

Tools

• Netron: a viewer for ONNX models
• Net Drawer ONNX vizualizer

Programming utilities for working with ONNX Graphs

• Shape and Type Inference
• Graph Optimization
• Opset Version Conversion

Contribute

ONNX is a community project. We encourage you to join the effort and contribute feedback, ideas, and code. You can join one of the working groups and help shape the future of ONNX.

Check out our contribution guide and call for contributions to get started.

Discuss

We encourage you to open Issues, or use Gitter for more real-time discussion:

Follow Us

Stay up to date with the latest ONNX news. [Facebook] [Twitter]

Installation

Binaries

A binary build of ONNX is available from Conda, in conda-forge:

conda install -c conda-forge onnx

Source

You will need an install of protobuf and numpy to build ONNX. One easy way to get these dependencies is via Anaconda:

# Use conda-forge protobuf, as default doesn't come with protoc
conda install -c conda-forge protobuf numpy

You can then install ONNX from PyPi (Note: Set environment variable ONNX_ML=1 for onnx-ml):

pip install onnx

You can also build and install ONNX locally from source code:

git clone https://github.com/onnx/onnx.git
cd onnx
git submodule update --init --recursive
python setup.py install

Note: When installing in a non-Anaconda environment, make sure to install the Protobuf compiler before running the pip installation of onnx. For example, on Ubuntu:

sudo apt-get install protobuf-compiler libprotoc-dev
pip install onnx

After installation, run

python -c "import onnx"

to verify it works. Note that this command does not work from a source checkout directory; in this case you'll see:

ModuleNotFoundError: No module named 'onnx.onnx_cpp2py_export'

Change into another directory to fix this error.

Testing

ONNX uses pytest as test driver. In order to run tests, first you need to install pytest:

pip install pytest-cov nbval

After installing pytest, do

pytest

to run tests.

Development

Check out contributor guide for instructions.

License

MIT License

Code of Conduct

ONNX Open Source Code of Conduct