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fix: Update broken repo hyperlink #1124

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6b86ca8
fix: Fixed torchtrt_runtime example link
lamhoangtung Jun 16, 2022
cd84cac
fix: Update new repo link under pytorch org
lamhoangtung Jun 17, 2022
ef6a88d
docs: [Automated] Regenerating documenation for cd84cac
Jun 17, 2022
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2 changes: 1 addition & 1 deletion CONTRIBUTING.md
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### Developing Torch-TensorRT

Do try to fill an issue with your feature or bug before filling a PR (op support is generally an exception as long as you provide tests to prove functionality). There is also a backlog (https://github.com/NVIDIA/Torch-TensorRT/issues) of issues which are tagged with the area of focus, a coarse priority level and whether the issue may be accessible to new contributors. Let us know if you are interested in working on a issue. We are happy to provide guidance and mentorship for new contributors. Though note, there is no claiming of issues, we prefer getting working code quickly vs. addressing concerns about "wasted work".
Do try to fill an issue with your feature or bug before filling a PR (op support is generally an exception as long as you provide tests to prove functionality). There is also a backlog (https://github.com/pytorch/TensorRT/issues) of issues which are tagged with the area of focus, a coarse priority level and whether the issue may be accessible to new contributors. Let us know if you are interested in working on a issue. We are happy to provide guidance and mentorship for new contributors. Though note, there is no claiming of issues, we prefer getting working code quickly vs. addressing concerns about "wasted work".

#### Communication

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Expand Up @@ -118,7 +118,7 @@ These are the following dependencies used to verify the testcases. Torch-TensorR

## Prebuilt Binaries and Wheel files

Releases: https://github.com/NVIDIA/Torch-TensorRT/releases
Releases: https://github.com/pytorch/TensorRT/releases

## Compiling Torch-TensorRT

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### 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/NVIDIA/Torch-TensorRT/issues) for information on the support status of various operators.
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?

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it's still a phase in our partitioning process.
- `Stitching`. Stitch all TensorRT engines with PyTorch nodes altogether.

Test cases for each of these components could be found [here](https://github.com/NVIDIA/Torch-TensorRT/tree/master/tests/core/partitioning).
Test cases for each of these components could be found [here](https://github.com/pytorch/TensorRT/tree/master/tests/core/partitioning).

Here is the brief description of functionalities of each file:
- `PartitionInfo.h/cpp`: The automatic fallback APIs that is used for partitioning.
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Expand Up @@ -37,4 +37,4 @@ If you'd like to compile your plugin with Torch-TensorRT,

Once you've completed the above steps, upon successful compilation of Torch-TensorRT library, your plugin should be available in `libtorchtrt_plugins.so`.

A sample runtime application on how to run a network with plugins can be found <a href="https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/torchtrt_runtime_example" >here</a>
A sample runtime application on how to run a network with plugins can be found <a href="https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example" >here</a>
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