# How to know the detailed memory usage of an Actor? **URL:** \ **Category:** Ray Core\ **Created:** [September 10, 2025, 9:01am UTC](https://discuss.ray.io/t/how-to-know-the-detailed-memory-usage-of-an-actor/23090 "2025-09-10T09:01:15Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![INBreeze](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/inbreeze/32/8225_2.png) [@INBreeze](https://discuss.ray.io/u/INBreeze)\ **Post date:** [September 10, 2025, 9:01am UTC](https://discuss.ray.io/t/how-to-know-the-detailed-memory-usage-of-an-actor/23090/1 "2025-09-10T09:01:15Z")
**1. Severity of the issue: (select one)** Medium: Significantly affects my productivity but can find a workaround. **2. Environment:** - Ray version: 2.47.1 - Python version: 3.11 - OS: Ubuntu 22.04 - Cloud/Infrastructure: 5070Ti + 64G + AMD64 - Other libs/tools (if relevant): None **3. What happened vs. what you expected:** - Expected: I’m new to Ray and would like to know how to freeze a Ray local cluster before killing all actors and tasks due to out-of-memory, and how to drill down to a specific actor for memory analysis and get detailed object information in Plasma/Heap. - Actual: The job exits directly, leaving only the Actor-level memory overflow log report First of all, I would like to express my gratitude to friends who are willing to share their experience ```python Traceback (most recent call last): File "/home/inbreeze/PycharmProjects/DataPlatformOnRay/python/raypipe/runner.py", line 65, in logger.info(f"[DataPlatformOnRay] final return: {ray.get(mergeActor.run.remote())}") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/miniconda3/envs/ray-dev/lib/python3.11/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/opt/miniconda3/envs/ray-dev/lib/python3.11/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/miniconda3/envs/ray-dev/lib/python3.11/site-packages/ray/_private/worker.py", line 2849, in get values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/miniconda3/envs/ray-dev/lib/python3.11/site-packages/ray/_private/worker.py", line 939, in get_objects raise value ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory. Memory on the node (IP: 10.2.97.17, ID: 89f4f4b4c16742cd9053e68d1513923883441cf068bfc2667e81c669) where the task (actor ID: 0f28f36dc1a20a125cb8a86f05000000, name=ResultMergeActor. __init__ , pid=268816, memory used=0.06GB) was running was 61.76GB / 62.62GB (0.98628), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 5d4a0cc4958f36a555306298c10f9043ec52bfd14067810dbc5f4a9d) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 10.2.97.17`. To see the logs of the worker, use `ray logs worker-5d4a0cc4958f36a555306298c10f9043ec52bfd14067810dbc5f4a9d*out -ip 10.2.97.17. Top 10 memory users: PID MEM(GB) COMMAND 263393 14.02 ray::SceneCutRouter.submit 268660 11.42 ray::AesActor.process_with_queue 268785 11.22 ray::OptFlowActor.process_with_queue 263388 6.15 ray::SceneCutMemActor 268686 4.95 ray::OCRActor 54419 3.07 /home/inbreeze/.cache/JetBrains/RemoteDev/dist/461d91da9a280_pycharm-2025.2.0.1/bin/remote-dev-serve... 263297 0.47 /opt/miniconda3/envs/ray-dev/bin/python /home/inbreeze/PycharmProjects/DataPlatformOnRay/python/rayp... 268778 0.41 ray::LaplacianActor.process_with_queue 263394 0.29 ray::SceneCutMemActor 70074 0.28 /home/inbreeze/.cache/JetBrains/PyCharm2025.2/full-line/models/7b957b12-0866-32e2-985d-1542c7c2aeee/... ``` ---
**Author:** ![eoakes](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/eoakes/32/7825_2.png) [@eoakes](https://discuss.ray.io/u/eoakes)\ **Post date:** [September 24, 2025, 4:54pm UTC](https://discuss.ray.io/t/how-to-know-the-detailed-memory-usage-of-an-actor/23090/2 "2025-09-24T16:54:00Z")
@INBreeze I suggest using a memory profiling tool like memray for this. Relevant docs are here: [Debugging Memory Issues — Ray 2.49.2](https://docs.ray.io/en/latest/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors) ---
**Author:** ![RunLLM](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/runllm/32/8249_2.png) [@RunLLM](https://discuss.ray.io/u/RunLLM)\ **Post date:** [September 24, 2025, 4:54pm UTC](https://discuss.ray.io/t/how-to-know-the-detailed-memory-usage-of-an-actor/23090/3 "2025-09-24T16:54:31Z")
To memory profile Ray tasks or actors, use the memray library. Install memray (`pip install memray`), then wrap your Ray task or actor code with memray’s Tracker context manager, saving the output to `/tmp/ray/session_latest/logs` so you can download the profiling files from the Ray dashboard. After running, use `memray flamegraph ` to visualize the results. Example code and detailed steps are provided in the official documentation. Would you like a step-by-step code example for profiling a Ray task or actor? Sources: - [Memory Profiling Ray tasks and actors](https://docs.ray.io/en/latest/ray-observability/user-guides/debug-apps/debug-memory.html#memory-profiling-ray-tasks-and-actors) Hint: Mention @RunLLM in the post for followups.