Demonstration of logging to Rerun from multiple threads.

Used Rerun types

Boxes2D

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)]))
       t.start()
       threads.append(t)

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

   # … existing code …

Run the code

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

# Setup
pip install --upgrade rerun-sdk  # install the latest Rerun SDK
git clone git@github.com:rerun-io/rerun.git  # 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