Redis Memory Management (Experimental)¶
Ray stores metadata associated with tasks and objects in one or more Redis servers, as described in An Overview of the Internals. Applications that are long-running or have high task/object generation rate could risk high memory pressure, potentially leading to out-of-memory (OOM) errors.
Here, we describe an experimental feature that transparently flushes metadata entries out of Redis memory.
As of early July 2018, the automatic memory management feature requires building Ray from source. We are planning on eliminating this step in the near future by releasing official wheels.
First, follow instructions to build Ray from source to install prerequisites. After
the prerequisites are installed, instead of doing the regular
pip install as
referenced in that document, pass an additional special flag,
git clone https://github.com/ray-project/ray.git cd ray/python RAY_USE_NEW_GCS=on pip install -e . --verbose # Add --user if you see a permission denied error.
Running Ray applications¶
At run time the environment variables
RAY_USE_XRAY=1 are required.
export RAY_USE_NEW_GCS=on export RAY_USE_XRAY=1 python my_ray_script.py # Or launch python/ipython.
Activate memory flushing¶
After building Ray using the method above, simply add these two lines after
ray.init() to activate automatic memory flushing:
ray.init(...) policy = ray.experimental.SimpleGcsFlushPolicy() ray.experimental.set_flushing_policy(policy) # My awesome Ray application logic follows.
Paramaters of the flushing policy¶
There are three user-configurable parameters
flush_when_at_least_bytes: Wait until this many bytes of memory usage accumulated in the redis server before flushing kicks in.
flush_period_secs: Issue a flush to the Redis server every this many seconds.
flush_num_entries_each_time: A hint to the system on the number of entries to flush on each request.
The default values should serve to be non-invasive for lightweight Ray
flush_when_at_least_bytes is set to
(1<<31) or 2GB,
flush_period_secs to 10, and
flush_num_entries_each_time to 10000:
# Default parameters. ray.experimental.SimpleGcsFlushPolicy( flush_when_at_least_bytes=(1 << 31), flush_period_secs=10, flush_num_entries_each_time=10000)
In particular, these default values imply that
1. the Redis server would accumulate memory usage up to 2GB without any entries being flushed, then the flushing would kick in; and
2. generally, “older” metadata entries would be flushed first, and the Redis server would always keep the most recent window of metadata of 2GB in size.
For advanced users. Advanced users can tune the above parameters to their applications’ needs; note that the desired flush rate is equal to (flush period) * (num entries each flush).