readme

CONTRIBUTING.md

@@ -54,4 +54,62 @@ pip install pre-commit
 go install github.com/bazelbuild/buildtools/buildifier@latest
 ```
 
-Thanks in advance for your patience as we review your contributions; we do appreciate them!
+## Testing using Python backend
+
+Torch-TensorRT supports testing in Python using [nox](https://nox.thea.codes/en/stable)
+
+To install the nox using python-pip
+
+```
+python3 -m pip install --upgrade nox
+```
+
+To list supported nox sessions:
+
+```
+nox --session -l
+```
+
+Environment variables supported by nox
+
+```
+PYT_PATH          - To use different PYTHONPATH than system installed Python packages
+TOP_DIR           - To set the root directory of the noxfile
+USE_CXX11         - To use cxx11_abi (Defaults to 0)
+USE_HOST_DEPS     - To use host dependencies for tests (Defaults to 0)
+```
+
+Usage example
+
+```
+nox --session l0_api_tests
+```
+
+Supported Python versions:
+```
+["3.7", "3.8", "3.9", "3.10"]
+```
+
+## How do I add support for a new op...
+
+### In Torch-TensorRT?
+
+Thanks for wanting to contribute! There are two main ways to handle supporting a new op. Either you can write a converter for the op from scratch and register it in the NodeConverterRegistry or if you can map the op to a set of ops that already have converters you can write a graph rewrite pass which will replace your new op with an equivalent subgraph of supported ops. Its preferred to use graph rewriting because then we do not need to maintain a large library of op converters. Also do look at the various op support trackers in the [issues](https://github.com/pytorch/TensorRT/issues) for information on the support status of various operators.
+
+### In my application?
+
+> The Node Converter Registry is not exposed in the top level API but in the internal headers shipped with the tarball.
+
+You can register a converter for your op using the `NodeConverterRegistry` inside your application.
+
+## Structure of the repo
+
+| Component                | Description                                                  |
+| ------------------------ | ------------------------------------------------------------ |
+| [**core**](core)         | Main JIT ingest, lowering, conversion and runtime implementations |
+| [**cpp**](cpp)           | C++ API and CLI source                                       |
+| [**examples**](examples) | Example applications to show different features of Torch-TensorRT |
+| [**py**](py)             | Python API for Torch-TensorRT                                |
+| [**tests**](tests)       | Unit tests for Torch-TensorRT                                |
+
+Thanks in advance for your patience as we review your contributions; we do appreciate them!