# Development

You will need to install protobuf and numpy to build ONNX. An easy
way to get these dependencies is via [Anaconda](https://www.anaconda.com/download/):

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

During development, it's convenient to install ONNX in development mode (to disable ONNX-ML, set environment variable `ONNX_ML=0`):

```
git clone --recursive https://github.com/onnx/onnx.git
pip install -e onnx/
```
Then, after you have made changes to Python and C++ files:

- `Python files`: the changes are effective immediately in your installation. You don't need to install these again.
- `C++ files`: you need to install these again to trigger the native extension build.

## Folder structure

- `onnx/`: the main folder that all code lies under
  - `onnx.proto`: the protobuf (v2.6.1) that contains all the structures
  - `checker.py`: a utility to check whether a serialized ONNX proto is legal
  - `helper.py`: tools for graph operation
  - `defs/`: a subfolder that defines the ONNX operators
  - `test/`: test files

## Generated operator documentation

[Operator docs in Operators.md](Operators.md) are automatically generated based on C++ operator definitions. To refresh these docs, remember to re-install (see above) and then run the following command from the repo root and commit the results:

```
python onnx/defs/gen_doc.py
```

## Adding a new operator

ONNX is an open standard, and we encourage developers to contribute high
quality operators to ONNX specification.
Before proposing a new operator, please read [the tutorial](AddNewOp.md).

# Testing

ONNX uses [pytest](https://docs.pytest.org) as a test driver. To run tests, you'll first need to install pytest:

```
pip install pytest nbval
```

After installing pytest, run

```
pytest
```

to begin the tests.

# Static typing (mypy)

We use [mypy](http://mypy-lang.org/) to run static type checks on the onnx code base. To check that your code passes, you'll first need to install the mypy type checker. If you're using python 3, call from your onnx source folder:

```
pip install -e .[mypy]
```

The type checker cannot run in a python 2 environment (but it will check python 2 code).
If you're using python 2, you need to install mypy into your system packages instead:

```
pip3 install mypy==[version]
```
*Note: You'll find the version we're currently using in `setup.py`.*

After having installed mypy, you can run the type checks:

```
python setup.py typecheck
```


# Other developer documentation

* [How to implement ONNX backend (ONNX to something converter)](ImplementingAnOnnxBackend.md)
* [Backend test infrastructure and how to add tests](OnnxBackendTest.md)

# License

[MIT License](LICENSE)

# Code of Conduct

[ONNX Open Source Code of Conduct](http://onnx.ai/codeofconduct.html)
