If you are interested in contributing to TorchServe you'll often need to install it from source and follow some of our guidelines to merge your PRs easily.
Your contributions will fall into two categories:
- You want to propose a new feature and implement it.
- Post about your intended feature as an issue, and we will discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
- You want to implement a feature or bug-fix for an outstanding issue.
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Search for your issue here: https://github.com/pytorch/serve/issues (look for the "good first issue" tag if you're a first time contributor)
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Pick an issue and comment on the task that you want to work on this feature.
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To ensure your changes doesn't break any of the existing features run the sanity suite as follows from serve directory:
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Install dependencies (if not already installed) For CPU
python ts_scripts/install_dependencies.py --environment=dev
For GPU
bash python ts_scripts/install_dependencies.py --environment=dev --cuda=cu102> Supported cuda versions as cu116, cu113, cu111, cu102, cu101, cu92- Install
pre-committo your Git flow:pre-commit install
- Run sanity suite
python torchserve_sanity.py
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Run Regression test
python test/regression_tests.py -
For running individual test suites refer code_coverage documentation
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If you are updating an existing model make sure that performance hasn't degraded by typing running benchmarks on the master branch and your branch and verify there is no performance regression
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Run
ts_scripts/spellcheck.shto fix any typos in your documentation -
For large changes make sure to run the automated benchmark suite which will run the apache bench tests on several configurations of CUDA and EC2 instances
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If you need more context on a particular issue, please create raise a ticket on
TorchServeGH repo or connect to PyTorch's slack channel
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Once you finish implementing a feature or bug-fix, please send a Pull Request to https://github.com/pytorch/serve.
For more non-technical guidance about how to contribute to PyTorch, see the Contributing Guide.
If you plan to develop with TorchServe and change some source code, you must install it from source code.
Ensure that you have python3 installed, and the user has access to the site-packages or ~/.local/bin is added to the PATH environment variable.
Run the following script from the top of the source directory.
NOTE: This script force reinstalls torchserve, torch-model-archiver and torch-workflow-archiver if existing installations are found
python ./ts_scripts/install_dependencies.py --environment=dev
python ./ts_scripts/install_from_src.py --environment=dev
Use --cuda flag with install_dependencies.py for installing cuda version specific dependencies. Possible values are cu111, cu102, cu101, cu92
Refer to the documentation here.
For information about the model archiver, see detailed documentation.
If you've never contributed to TorchServe or OSS before then a great place to start is issues labeled as good first issue. Bonus points if you personally care about this issue or if it's an issue you filed.
If you've used TorchServe in an interesting way, we'd love to feature it in https://github.com/pytorch/serve#-news
The easiest area to contribute a new change to TorchServe is https://github.com/pytorch/serve/tree/master/examples by creating a new handler. Handlers are a simple but powerful paradigm that let you execute arbitrary Python code while serving a model.
For example
The core team developed a single handler to deal with question answering, token classification and sequence classification for HuggingFace models https://github.com/pytorch/serve/blob/master/examples/Huggingface_Transformers/Transformer_handler_generalized.py
If you have another use case we'd love to merge it!
As simple as they seem handlers let you do complex stuff like exporting to various runtimes and various people in open source have managed to support ORT, TensorRT and IPEX without much of the core team's involvement.
class CustomHandler(BaseHandler):
def initialize(self, ctx):
def preprocess(self, requests) -> List[Any]:
def inference(self, input_batch : List[torch.Tensor]) -> List[torch.Tensor]:
def postprocess(self, inference_output : List[torch.Tensor]) -> List[Any]:The key thing to observe here is that CustomHandler is a class so it can hold state that you can save from any of the relevant handler functions. For example you can setup a runtime configuration in initialize() and then use it in an inference() function. postprocess() can return a list of outputs alongside some debug information stored in arbitrary data-structure. requests can be anything, video, text or sound data and we've it to this effect in our multi modal MMF example
Handlers are incredibly powerful and will let you build meaningful contributions to TorchServe without having to dive deep into the Java internals.
If you are interested in contributing to the internals, we suggest you start here Internals Guide
To have your custom configurations available in config.properties this is a good educational PR to follow as an example.
All available configurations are set in ConfigManager.java and then can be accessed from handler using properties = context.system_properties. You can also access environment variables directly using os.environ.get() and gate behavior based on what that environment variable is.
All of our model optimization work ranging from IPEX to TensorRT work in a similar manner.
For something more complicated please open an issue and discuss it with the core team, you can see what our general priorities are here https://github.com/pytorch/serve/projects but if you need a feature urgently we are happy to guide you so you can get it done.