This page describes how memory management works in Ray.
Concepts
~~~~~~~~
There are several ways that Ray applications use memory:
..image:: images/memory.svg
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.put`` and 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. In Ray 1.3+, objects will be `spilled to disk <#object-spilling>`__ 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.
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.
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.
--- 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``.
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()``.
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."
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.
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.
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.
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.
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``.
Ray 1.3+ spills objects to external storage once the object store is full. By default, objects are spilled to Ray's temporary directory in the local filesystem.
Single node
-----------
Ray uses object spilling by default. Without any setting, objects are spilled to `[temp_folder]/spill`. `temp_folder` is `/tmp` for Linux and MacOS by default.
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:
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:
..code-block:: python
# 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
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:
