To help you get started, we've collected the most common ways that onnx is being used within popular public projects.

pytorch / pytorch / test / onnx / export_onnx_tests_filter.py View on Github

for d in os.listdir(root_dir):
        dir_name = os.path.join(root_dir, d)
        if os.path.isdir(dir_name):
            failed = False
            try:
                model_file = os.path.join(dir_name, "model.onnx")
                data_dir_pattern = os.path.join(dir_name, "test_data_set_*")
                for data_dir in glob.glob(data_dir_pattern):
                    for device in torch.testing.get_all_device_types():
                        run_generated_test(model_file, data_dir, device)
                if expect:
                    expect_file = os.path.join(_expect_dir,
                                               "PyTorch-generated-{}.expect".format(d))
                    with open(expect_file, "w") as text_file:
                        model = onnx.load(model_file)
                        onnx.checker.check_model(model)
                        onnx.helper.strip_doc_string(model)
                        text_file.write(google.protobuf.text_format.MessageToString(model))
                total_pass += 1
            except Exception as e:
                if verbose:
                    print("The test case in {} failed!".format(dir_name))
                    traceback.print_exc()
                if fail_dir is None:
                    shutil.rmtree(dir_name)
                else:
                    target_dir = os.path.join(fail_dir, d)
                    if os.path.exists(target_dir):
                        shutil.rmtree(target_dir)
                    shutil.move(dir_name, target_dir)
                total_fail += 1
    print("Successfully generated/updated {} test cases from PyTorch.".format(total_pass))

View more ways to use onnx

Open Neural Network Exchange (ONNX) is 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, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).

ONNX is widely supported and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. We invite the community to join us and further evolve ONNX.

Use ONNX

• Documentation of ONNX Python Package
• Tutorials for creating ONNX models
• Pre-trained ONNX models

Learn about the ONNX spec

• Overview
• ONNX intermediate representation spec
• Versioning principles of the spec
• Operators documentation
• Operators documentation (latest release)
• Python API Overview

Programming utilities for working with ONNX Graphs

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

Contribute

ONNX is a community project and the open governance model is described here. We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the Special Interest Groups and Working Groups to shape the future of ONNX.

Check out our contribution guide to get started.

If you think some operator should be added to ONNX specification, please read this document.

Community meetings

The schedules of the regular meetings of the Steering Committee, the working groups and the SIGs can be found here

Community Meetups are held at least once a year. Content from previous community meetups are at:

• 2020.04.09
• 2020.10.14
• 2021.03.24
• 2021.10.21
• 2022.06.24
• 2023.06.28

Discuss

We encourage you to open Issues, or use Slack (If you have not joined yet, please use this link to join the group) for more real-time discussion.

Follow Us

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

Roadmap

A roadmap process takes place every year. More details can be found here

Installation

Official Python packages

ONNX released packages are published in PyPi.

pip install onnx  # or pip install onnx[reference] for optional reference implementation dependencies

ONNX weekly packages are published in PyPI to enable experimentation and early testing.

vcpkg packages

onnx is in the maintenance list of vcpkg, you can easily use vcpkg to build and install it.

git clone https://github.com/microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.bat # For powershell
./bootstrap-vcpkg.sh # For bash
./vcpkg install onnx

Conda packages

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

conda install -c conda-forge onnx

Build ONNX from Source

Before building from source uninstall any existing versions of onnx pip uninstall onnx.

c++17 or higher C++ compiler version is required to build ONNX from source. Still, users can specify their own CMAKE_CXX_STANDARD version for building ONNX.

If you don't have protobuf installed, ONNX will internally download and build protobuf for ONNX build.

Or, you can manually install protobuf C/C++ libraries and tools with specified version before proceeding forward. Then depending on how you installed protobuf, you need to set environment variable CMAKE_ARGS to "-DONNX_USE_PROTOBUF_SHARED_LIBS=ON" or "-DONNX_USE_PROTOBUF_SHARED_LIBS=OFF". For example, you may need to run the following command:

Linux:

export CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"

Windows:

set CMAKE_ARGS="-DONNX_USE_PROTOBUF_SHARED_LIBS=ON"

The ON/OFF depends on what kind of protobuf library you have. Shared libraries are files ending with *.dll/*.so/*.dylib. Static libraries are files ending with *.a/*.lib. This option depends on how you get your protobuf library and how it was built. And it is default OFF. You don't need to run the commands above if you'd prefer to use a static protobuf library.

Windows

If you are building ONNX from source, it is recommended that you also build Protobuf locally as a static library. The version distributed with conda-forge is a DLL, but ONNX expects it to be a static library. Building protobuf locally also lets you control the version of protobuf. The tested and recommended version is 3.21.12.

The instructions in this README assume you are using Visual Studio. It is recommended that you run all the commands from a shell started from "x64 Native Tools Command Prompt for VS 2019" and keep the build system generator for cmake (e.g., cmake -G "Visual Studio 16 2019") consistent while building protobuf as well as ONNX.

You can get protobuf by running the following commands:

git clone https://github.com/protocolbuffers/protobuf.git
cd protobuf
git checkout v21.12
cd cmake
cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX= -Dprotobuf_MSVC_STATIC_RUNTIME=OFF -Dprotobuf_BUILD_SHARED_LIBS=OFF -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_EXAMPLES=OFF .
msbuild protobuf.sln /m /p:Configuration=Release
msbuild INSTALL.vcxproj /p:Configuration=Release

Then it will be built as a static library and installed to . Please add the bin directory(which contains protoc.exe) to your PATH.

set PATH=/bin;%PATH%

Please note: if your protobuf_install_dir contains spaces, do not add quotation marks around it.

Alternative: if you don't want to change your PATH, you can set ONNX_PROTOC_EXECUTABLE instead.

set CMAKE_ARGS=-DONNX_PROTOC_EXECUTABLE=

Then you can build ONNX as:

git clone https://github.com/onnx/onnx.git
cd onnx
git submodule update --init --recursive
# prefer lite proto
set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
pip install -e .

Linux

First, you need to install protobuf. The minimum Protobuf compiler (protoc) version required by ONNX is 3.6.1. Please note that old protoc versions might not work with CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON.

Ubuntu 20.04 (and newer) users may choose to install protobuf via

apt-get install python3-pip python3-dev libprotobuf-dev protobuf-compiler

In this case, it is required to add -DONNX_USE_PROTOBUF_SHARED_LIBS=ON to CMAKE_ARGS in the ONNX build step.

A more general way is to build and install it from source. See the instructions below for more details.

  git clone https://github.com/protocolbuffers/protobuf.git
  cd protobuf
  git checkout v21.12
  git submodule update --init --recursive
  mkdir build_source && cd build_source
  cmake ../cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=/usr -DCMAKE_INSTALL_SYSCONFDIR=/etc -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
  make -j$(nproc)
  make install

  git clone https://github.com/protocolbuffers/protobuf.git
  cd protobuf
  git checkout v21.12
  git submodule update --init --recursive
  mkdir build_source && cd build_source
  cmake ../cmake  -DCMAKE_INSTALL_LIBDIR=lib64 -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=/usr -DCMAKE_INSTALL_SYSCONFDIR=/etc -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
  make -j$(nproc)
  make install

Then you can build ONNX as:

git clone https://github.com/onnx/onnx.git
cd onnx
git submodule update --init --recursive
# Optional: prefer lite proto
export CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
pip install -e .

Mac

export NUM_CORES=`sysctl -n hw.ncpu`
brew update
brew install autoconf && brew install automake
wget https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protobuf-cpp-3.21.12.tar.gz
tar -xvf protobuf-cpp-3.21.12.tar.gz
cd protobuf-3.21.12
mkdir build_source && cd build_source
cmake ../cmake -Dprotobuf_BUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -Dprotobuf_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release
make -j${NUM_CORES}
make install

Once build is successful, update PATH to include protobuf paths.

Then you can build ONNX as:

git clone --recursive https://github.com/onnx/onnx.git
cd onnx
# Optional: prefer lite proto
set CMAKE_ARGS=-DONNX_USE_LITE_PROTO=ON
pip install -e .

Verify Installation

After installation, run

python -c "import onnx"

to verify it works.

Common Build Options

For full list refer to CMakeLists.txt

Environment variables

• USE_MSVC_STATIC_RUNTIME should be 1 or 0, not ON or OFF. When set to 1 onnx links statically to runtime library. Default: USE_MSVC_STATIC_RUNTIME=0

• DEBUG should be 0 or 1. When set to 1 onnx is built in debug mode. or debug versions of the dependencies, you need to open the CMakeLists file and append a letter d at the end of the package name lines. For example, NAMES protobuf-lite would become NAMES protobuf-lited. Default: Debug=0

CMake variables

• ONNX_USE_PROTOBUF_SHARED_LIBS should be ON or OFF. Default: ONNX_USE_PROTOBUF_SHARED_LIBS=OFF USE_MSVC_STATIC_RUNTIME=0 ONNX_USE_PROTOBUF_SHARED_LIBS determines how onnx links to protobuf libraries.

  • When set to ON - onnx will dynamically link to protobuf shared libs, PROTOBUF_USE_DLLS will be defined as described here, Protobuf_USE_STATIC_LIBS will be set to OFF and USE_MSVC_STATIC_RUNTIME must be 0.
  • When set to OFF - onnx will link statically to protobuf, and Protobuf_USE_STATIC_LIBS will be set to ON (to force the use of the static libraries) and USE_MSVC_STATIC_RUNTIME can be 0 or 1.

• ONNX_USE_LITE_PROTO should be ON or OFF. When set to ON onnx uses lite protobuf instead of full protobuf. Default: ONNX_USE_LITE_PROTO=OFF

• ONNX_WERROR should be ON or OFF. When set to ON warnings are treated as errors. Default: ONNX_WERROR=OFF in local builds, ON in CI and release pipelines.

Common Errors

• Note: the import onnx command does not work from the 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.

• If you run into any issues while building Protobuf as a static library, please ensure that shared Protobuf libraries, like libprotobuf, are not installed on your device or in the conda environment. If these shared libraries exist, either remove them to build Protobuf from source as a static library, or skip the Protobuf build from source to use the shared version directly.

• If you run into any issues while building ONNX from source, and your error message reads, Could not find pythonXX.lib, ensure that you have consistent Python versions for common commands, such as python and pip. Clean all existing build files and rebuild ONNX again.

Testing

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

pip install pytest nbval

After installing pytest, use the following command to run tests.

pytest

Development

Check out the contributor guide for instructions.

License

Apache License v2.0

Code of Conduct

ONNX Open Source Code of Conduct