Demonstration of logging to Rerun from multiple threads.

Multithreading example screenshot

Used Rerun types used-rerun-types


Logging and visualizing with Rerun logging-and-visualizing-with-rerun

This example showcases logging from multiple threads, starting with the definition of the function for logging, the rect_logger, followed by typical usage of Python's threading module in the main function.

def rect_logger(path: str, color: npt.NDArray[np.float32]) -> None:
   for _ in range(1000):
       rects_xy = np.random.rand(5, 2) * 1024
       rects_wh = np.random.rand(5, 2) * (1024 - rects_xy + 1)
       rects = np.hstack((rects_xy, rects_wh))
       rr.log(path, rr.Boxes2D(array=rects, array_format=rr.Box2DFormat.XYWH, colors=color)) # Log the rectangles using Rerun

The main function manages the multiple threads for logging data to the Rerun viewer.

def main() -> None:
   # … existing code …

   threads = []

   for i in range(10): # Create 10 threads to run the rect_logger function with different paths and colors.
       t = threading.Thread(target=rect_logger, args=(f"thread/{i}", [random.randrange(255) for _ in range(3)]))

   for t in threads: # Wait for all threads to complete before proceeding.

   # … existing code …

Run the code run-the-code

To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:

pip install --upgrade rerun-sdk  # install the latest Rerun SDK
git clone  # Clone the repository
cd rerun
git checkout latest  # Check out the commit matching the latest SDK release

Install the necessary libraries specified in the requirements file:

pip install -e examples/python/multithreading

To experiment with the provided example, simply execute the main Python script:

python -m multithreading # run the example

If you wish to customize it, explore additional features, or save it use the CLI with the --help option for guidance:

python -m multithreading --help