Memory Management#
This page describes how memory management works in Ray.
Also view Debugging Out of Memory to learn how to troubleshoot out-of-memory issues.
Concepts#
There are several ways that Ray applications use memory:
- Ray system memory: this is memory used internally by Ray
GCS: memory used for storing the list of nodes and actors present in the cluster. The amount of memory used for these purposes is typically quite small.
Raylet: memory used by the C++ raylet process running on each node. This cannot be controlled, but is typically quite small.
- Application memory: this is memory used by your application
Worker heap: memory used by your application (e.g., in Python code or TensorFlow), best measured as the resident set size (RSS) of your application minus its shared memory usage (SHR) in commands such as
top. The reason you need to subtract SHR is that object store shared memory is reported by the OS as shared with each worker. Not subtracting SHR will result in double counting memory usage.Object store memory: memory used when your application creates objects in the object store via
ray.putand when it returns values from remote functions. Objects are reference counted and evicted when they fall out of scope. An object store server runs on each node. By default, when starting an instance, Ray reserves 30% of available memory. The size of the object store can be controlled by –object-store-memory. The memory is by default allocated to/dev/shm(shared memory) for Linux. For MacOS, Ray uses/tmp(disk), which can impact the performance compared to Linux. In Ray 1.3+, objects are spilled to disk if the object store fills up.Object store shared memory: memory used when your application reads objects via
ray.get. Note that if an object is already present on the node, this does not cause additional allocations. This allows large objects to be efficiently shared among many actors and tasks.
ObjectRef Reference Counting#
Ray implements distributed reference counting so that any ObjectRef in scope in the cluster is pinned in the object store. This includes local python references, arguments to pending tasks, and IDs serialized inside of other objects.
Debugging using ‘ray memory’#
The ray memory command can be used to help track down what ObjectRef references are in scope and may be causing an ObjectStoreFullError.
Running ray memory from the command line while a Ray application is running will give you a dump of all of the ObjectRef references that are currently held by the driver, actors, and tasks in the cluster.
======== Object references status: 2021-02-23 22:02:22.072221 ========
Grouping by node address... Sorting by object size...
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
287 MiB 4 0 0 1 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 6465 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 15 MiB LOCAL_REFERENCE (put object)
| test.py:
<module>:17
192.168.0.15 6465 Driver a67dc375e60ddd1affffffffffffffffffffffff0100000001000000 15 MiB LOCAL_REFERENCE (task call)
| test.py:
:<module>:18
192.168.0.15 6465 Driver ffffffffffffffffffffffffffffffffffffffff0100000002000000 18 MiB CAPTURED_IN_OBJECT (put object) |
test.py:
<module>:19
192.168.0.15 6465 Driver ffffffffffffffffffffffffffffffffffffffff0100000004000000 21 MiB LOCAL_REFERENCE (put object) |
test.py:
<module>:20
192.168.0.15 6465 Driver ffffffffffffffffffffffffffffffffffffffff0100000003000000 218 MiB LOCAL_REFERENCE (put object) |
test.py:
<module>:20
--- Aggregate object store stats across all nodes ---
Plasma memory usage 0 MiB, 4 objects, 0.0% full
Each entry in this output corresponds to an ObjectRef that’s currently pinning an object in the object store along with where the reference is (in the driver, in a worker, etc.), what type of reference it is (see below for details on the types of references), the size of the object in bytes, the process ID and IP address where the object was instantiated, and where in the application the reference was created.
ray memory comes with features to make the memory debugging experience more effective. For example, you can add arguments sort-by=OBJECT_SIZE and group-by=STACK_TRACE, which may be particularly helpful for tracking down the line of code where a memory leak occurs. You can see the full suite of options by running ray memory --help.
There are five types of references that can keep an object pinned:
1. Local ObjectRef references
import ray
@ray.remote
def f(arg):
return arg
a = ray.put(None)
b = f.remote(None)
In this example, we create references to two objects: one that is ray.put() in the object store and another that’s the return value from f.remote().
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
30 MiB 2 0 0 0 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 6867 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 15 MiB LOCAL_REFERENCE (put object) |
test.py:
<module>:12
192.168.0.15 6867 Driver a67dc375e60ddd1affffffffffffffffffffffff0100000001000000 15 MiB LOCAL_REFERENCE (task call)
| test.py:
:<module>:13
In the output from ray memory, we can see that each of these is marked as a LOCAL_REFERENCE in the driver process, but the annotation in the “Reference Creation Site” indicates that the first was created as a “put object” and the second from a “task call.”
2. Objects pinned in memory
import numpy as np
a = ray.put(np.zeros(1))
b = ray.get(a)
del a
In this example, we create a numpy array and then store it in the object store. Then, we fetch the same numpy array from the object store and delete its ObjectRef. In this case, the object is still pinned in the object store because the deserialized copy (stored in b) points directly to the memory in the object store.
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
243 MiB 0 1 0 0 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 7066 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 243 MiB PINNED_IN_MEMORY test.
py:<module>:19
The output from ray memory displays this as the object being PINNED_IN_MEMORY. If we del b, the reference can be freed.
3. Pending task references
@ray.remote
def f(arg):
while True:
pass
a = ray.put(None)
b = f.remote(a)
In this example, we first create an object via ray.put() and then submit a task that depends on the object.
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
25 MiB 1 1 1 0 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 7207 Driver a67dc375e60ddd1affffffffffffffffffffffff0100000001000000 ? LOCAL_REFERENCE (task call)
| test.py:
:<module>:29
192.168.0.15 7241 Worker ffffffffffffffffffffffffffffffffffffffff0100000001000000 10 MiB PINNED_IN_MEMORY (deserialize task arg)
__main__.f
192.168.0.15 7207 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 15 MiB USED_BY_PENDING_TASK (put object) |
test.py:
<module>:28
While the task is running, we see that ray memory shows both a LOCAL_REFERENCE and a USED_BY_PENDING_TASK reference for the object in the driver process. The worker process also holds a reference to the object because the Python arg is directly referencing the memory in the plasma, so it can’t be evicted; therefore it is PINNED_IN_MEMORY.
4. Serialized ObjectRef references
@ray.remote
def f(arg):
while True:
pass
a = ray.put(None)
b = f.remote([a])
In this example, we again create an object via ray.put(), but then pass it to a task wrapped in another object (in this case, a list).
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
15 MiB 2 0 1 0 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 7411 Worker ffffffffffffffffffffffffffffffffffffffff0100000001000000 ? LOCAL_REFERENCE (deserialize task arg)
__main__.f
192.168.0.15 7373 Driver a67dc375e60ddd1affffffffffffffffffffffff0100000001000000 ? LOCAL_REFERENCE (task call)
| test.py:
:<module>:38
192.168.0.15 7373 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 15 MiB USED_BY_PENDING_TASK (put object)
| test.py:
<module>:37
Now, both the driver and the worker process running the task hold a LOCAL_REFERENCE to the object in addition to it being USED_BY_PENDING_TASK on the driver. If this was an actor task, the actor could even hold a LOCAL_REFERENCE after the task completes by storing the ObjectRef in a member variable.
5. Captured ObjectRef references
a = ray.put(None)
b = ray.put([a])
del a
In this example, we first create an object via ray.put(), then capture its ObjectRef inside of another ray.put() object, and delete the first ObjectRef. In this case, both objects are still pinned.
--- Summary for node address: 192.168.0.15 ---
Mem Used by Objects Local References Pinned Count Pending Tasks Captured in Objects Actor Handles
233 MiB 1 0 0 1 0
--- Object references for node address: 192.168.0.15 ---
IP Address PID Type Object Ref Size Reference Type Call Site
192.168.0.15 7473 Driver ffffffffffffffffffffffffffffffffffffffff0100000001000000 15 MiB CAPTURED_IN_OBJECT (put object) |
test.py:
<module>:41
192.168.0.15 7473 Driver ffffffffffffffffffffffffffffffffffffffff0100000002000000 218 MiB LOCAL_REFERENCE (put object) |
test.py:
<module>:42
In the output of ray memory, we see that the second object displays as a normal LOCAL_REFERENCE, but the first object is listed as CAPTURED_IN_OBJECT.
Memory Aware Scheduling#
By default, Ray does not take into account the potential memory usage of a task or actor when scheduling. This is simply because it cannot estimate ahead of time how much memory is required. However, if you know how much memory a task or actor requires, you can specify it in the resource requirements of its ray.remote decorator to enable memory-aware scheduling:
Important
Specifying a memory requirement does NOT impose any limits on memory usage. The requirements are used for admission control during scheduling only (similar to how CPU scheduling works in Ray). It is up to the task itself to not use more memory than it requested.
To tell the Ray scheduler a task or actor requires a certain amount of available memory to run, set the memory argument. The Ray scheduler will then reserve the specified amount of available memory during scheduling, similar to how it handles CPU and GPU resources:
# reserve 500MiB of available memory to place this task
@ray.remote(memory=500 * 1024 * 1024)
def some_function(x):
pass
# reserve 2.5GiB of available memory to place this actor
@ray.remote(memory=2500 * 1024 * 1024)
class SomeActor:
def __init__(self, a, b):
pass
In the above example, the memory quota is specified statically by the decorator, but you can also set them dynamically at runtime using .options() as follows:
# override the memory quota to 100MiB when submitting the task
some_function.options(memory=100 * 1024 * 1024).remote(x=1)
# override the memory quota to 1GiB when creating the actor
SomeActor.options(memory=1000 * 1024 * 1024).remote(a=1, b=2)
Questions or Issues?#
You can post questions or issues or feedback through the following channels:
Discussion Board: For questions about Ray usage or feature requests.
GitHub Issues: For bug reports.
Ray Slack: For getting in touch with Ray maintainers.
StackOverflow: Use the [ray] tag questions about Ray.