guill 2b653e8c18
Support for async node functions (#8830)
* Support for async execution functions

This commit adds support for node execution functions defined as async. When
a node's execution function is defined as async, we can continue
executing other nodes while it is processing.

Standard uses of `await` should "just work", but people will still have
to be careful if they spawn actual threads. Because torch doesn't really
have async/await versions of functions, this won't particularly help
with most locally-executing nodes, but it does work for e.g. web
requests to other machines.

In addition to the execute function, the `VALIDATE_INPUTS` and
`check_lazy_status` functions can also be defined as async, though we'll
only resolve one node at a time right now for those.

* Add the execution model tests to CI

* Add a missing file

It looks like this got caught by .gitignore? There's probably a better
place to put it, but I'm not sure what that is.

* Add the websocket library for automated tests

* Add additional tests for async error cases

Also fixes one bug that was found when an async function throws an error
after being scheduled on a task.

* Add a feature flags message to reduce bandwidth

We now only send 1 preview message of the latest type the client can
support.

We'll add a console warning when the client fails to send a feature
flags message at some point in the future.

* Add async tests to CI

* Don't actually add new tests in this PR

Will do it in a separate PR

* Resolve unit test in GPU-less runner

* Just remove the tests that GHA can't handle

* Change line endings to UNIX-style

* Avoid loading model_management.py so early

Because model_management.py has a top-level `logging.info`, we have to
be careful not to import that file before we call `setup_logging`. If we
do, we end up having the default logging handler registered in addition
to our custom one.
2025-07-10 14:46:19 -04:00
..
2023-09-18 23:18:06 -04:00
2023-09-18 23:18:06 -04:00

Automated Testing

Running tests locally

Additional requirements for running tests:

pip install pytest
pip install websocket-client==1.6.1
opencv-python==4.6.0.66
scikit-image==0.21.0

Run inference tests:

pytest tests/inference

Quality regression test

Compares images in 2 directories to ensure they are the same

  1. Run an inference test to save a directory of "ground truth" images
    pytest tests/inference --output_dir tests/inference/baseline
  1. Make code edits

  2. Run inference and quality comparison tests

pytest