Memory Management¶
This page describes how memory management works in Ray.
Concepts¶
There are several ways that Ray applications use memory:
- Ray system memory: this is memory used internally by Ray
Redis: 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 returning values from remote functions. Objects are reference counted and evicted when they fall out of scope. There is an object store server running on each node.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.
Frequently Asked Questions (FAQ)¶
My application failed with ObjectStoreFullError. What happened?
Ensure that you’re removing ObjectRef references when they’re no longer needed. See Debugging using ‘ray memory’ for information on how to identify what objects are in scope in your application.
This exception is raised when the object store on a node was full of pinned objects when the application tried to create a new object (either by calling ray.put() or returning an object from a task). If you’re sure that the configured object store size was large enough for your application to run, ensure that you’re removing ObjectRef references when they’re no longer in use so their objects can be evicted from the object store.
I’m running Ray inside IPython or a Jupyter Notebook and there are ObjectRef references causing problems even though I’m not storing them anywhere.
Try Enabling LRU Fallback, which will cause unused objects referenced by IPython to be LRU evicted when the object store is full instead of erroring.
IPython stores the output of every cell in a local Python variable indefinitely. This causes Ray to pin the objects even though your application may not actually be using them.
My application used to run on previous versions of Ray but now I’m getting ObjectStoreFullError.
Either modify your application to remove ObjectRef references when they’re no longer needed or try Enabling LRU Fallback to revert to the old behavior.
In previous versions of Ray, there was no reference counting and instead objects in the object store were LRU evicted once the object store ran out of space. Some applications (e.g., applications that keep references to all objects ever created) may have worked with LRU eviction but do not with reference counting.
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 Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; worker pid=18301
45b95b1c8bd3a9c4ffffffff010000c801000000 LOCAL_REFERENCE ? (deserialize task arg) __main__..f
; driver pid=18281
f66d17bae2b0e765ffffffff010000c801000000 LOCAL_REFERENCE ? (task call) test.py:<module>:12
45b95b1c8bd3a9c4ffffffff010000c801000000 USED_BY_PENDING_TASK ? (task call) test.py:<module>:10
ef0a6c221819881cffffffff010000c801000000 LOCAL_REFERENCE ? (task call) test.py:<module>:11
ffffffffffffffffffffffff0100008801000000 LOCAL_REFERENCE 77 (put object) test.py:<module>:9
-----------------------------------------------------------------------------------------------------
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, and where in the application the reference was created.
There are five types of references that can keep an object pinned:
1. Local ObjectRef references
@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().
-----------------------------------------------------------------------------------------------------
Object Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; driver pid=18867
ffffffffffffffffffffffff0100008801000000 LOCAL_REFERENCE 77 (put object) ../test.py:<module>:9
45b95b1c8bd3a9c4ffffffff010000c801000000 LOCAL_REFERENCE ? (task call) ../test.py:<module>:10
-----------------------------------------------------------------------------------------------------
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.
-----------------------------------------------------------------------------------------------------
Object Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; driver pid=25090
ffffffffffffffffffffffff0100008801000000 PINNED_IN_MEMORY 229 test.py:<module>:7
-----------------------------------------------------------------------------------------------------
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.
-----------------------------------------------------------------------------------------------------
Object Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; worker pid=18971
ffffffffffffffffffffffff0100008801000000 PINNED_IN_MEMORY 77 (deserialize task arg) __main__..f
; driver pid=18958
ffffffffffffffffffffffff0100008801000000 USED_BY_PENDING_TASK 77 (put object) ../test.py:<module>:9
45b95b1c8bd3a9c4ffffffff010000c801000000 LOCAL_REFERENCE ? (task call) ../test.py:<module>:10
-----------------------------------------------------------------------------------------------------
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 it is PINNED_IN_MEMORY, because the Python arg is directly referencing the memory in the plasma, so it can’t be evicted.
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).
-----------------------------------------------------------------------------------------------------
Object Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; worker pid=19002
ffffffffffffffffffffffff0100008801000000 LOCAL_REFERENCE 77 (deserialize task arg) __main__..f
; driver pid=18989
ffffffffffffffffffffffff0100008801000000 USED_BY_PENDING_TASK 77 (put object) ../test.py:<module>:9
45b95b1c8bd3a9c4ffffffff010000c801000000 LOCAL_REFERENCE ? (task call) ../test.py:<module>:10
-----------------------------------------------------------------------------------------------------
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.
-----------------------------------------------------------------------------------------------------
Object Ref Reference Type Object Size Reference Creation Site
=====================================================================================================
; driver pid=19047
ffffffffffffffffffffffff0100008802000000 LOCAL_REFERENCE 1551 (put object) ../test.py:<module>:10
ffffffffffffffffffffffff0100008801000000 CAPTURED_IN_OBJECT 77 (put object) ../test.py:<module>:9
-----------------------------------------------------------------------------------------------------
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.
Enabling LRU Fallback¶
By default, Ray will raise an exception if the object store is full of pinned objects when an application tries to create a new object. However, in some cases applications might keep references to objects much longer than they actually use them, so simply LRU evicting objects from the object store when it’s full can prevent the application from failing.
Please note that relying on this is not recommended - instead, if possible you should try to remove references as they’re no longer needed in your application to free space in the object store.
To enable LRU eviction when the object store is full, initialize ray with the lru_evict option set:
ray.init(lru_evict=True)
ray start --lru-evict
Object Spilling¶
Ray 1.2.0+ has beta support for spilling objects to external storage once the capacity of the object store is used up. Please file a GitHub issue if you encounter any problems with this new feature. Eventually, object spilling will be enabled by default, but for now you need to enable it manually:
To enable object spilling to the local filesystem (single node clusters only):
ray.init(
_system_config={
"automatic_object_spilling_enabled": True,
"object_spilling_config": json.dumps(
{"type": "filesystem", "params": {"directory_path": "/tmp/spill"}},
)
},
)
To enable object spilling to remote storage (any URI supported by smart_open):
ray.init(
_system_config={
"automatic_object_spilling_enabled": True,
"max_io_workers": 4, # More IO workers for remote storage.
"min_spilling_size": 100 * 1024 * 1024, # Spill at least 100MB at a time.
"object_spilling_config": json.dumps(
{"type": "smart_open", "params": {"uri": "s3:///bucket/path"}},
)
},
)
When spilling is happening, the following INFO level messages will be printed to the raylet logs (e.g., /tmp/ray/session_latest/logs/raylet.out):
local_object_manager.cc:166: Spilled 50 MiB, 1 objects, write throughput 230 MiB/s
local_object_manager.cc:334: Restored 50 MiB, 1 objects, read throughput 505 MiB/s
You can also view cluster-wide spill stats by using the ray memory command:
--- Aggregate object store stats across all nodes ---
Plasma memory usage 50 MiB, 1 objects, 50.0% full
Spilled 200 MiB, 4 objects, avg write throughput 570 MiB/s
Restored 150 MiB, 3 objects, avg read throughput 1361 MiB/s
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(object):
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.