README.md

Ray Memory Leak Repro

Development Environment

This project uses VS Code Dev Containers to provide a reporoducible development environment.

When the project is opened in VS Code, it will prompt you to re-open the project in a container. Be sure to allow this.

When editing Python files, make sure that VS Code is using the poetry venv (./.venv/bin/python3). Otherwise it will fail to resolve third-party imports.

Running the application from the dev container

Open a terminal in VS Code and run the following:

poetry run ray start --head --disable-usage-stats
poetry run python src/main.py

This will continuously invoke the do_work remote method on the MinimalActor actor, which simply increments the value of its _counter state.

To collect memory stats for relevant processes, open another terminal in VS Code and run the following:

./log-mem.sh

This will use ps to get the RSS of processes containing 'ray' or 'python' in its process name. It will write the output to memory_usage.log every 5 seconds. Let it run for a while before plotting the data in order to get a meaningful trend.

To plot the data in memory_usage.log, use the src/plot_mem.py script as follows:

poetry run python src/plot_mem.py

This will create output/plot.html, which you can view in a web browser. Hovering the mouse over a line will reveal details, including the PID. Double-click the matching value in the Process ID legend to focus the chart on just that process. Single-click individual Process IDs to toggle their visibility.

Observations

We ran this for about 112 hours and observed a continuous overall increase in the peak memory usage of the ray/core/src/ray/gcs/gcs_server process. Values of the peaks increased from an initial value of ~595 MB to ~662 MB over that period. That works out to roughly 598 KB per hour.

The data and plot can be downloaded from Releases.