Miscellaneous Topics

This page will cover some miscellaneous topics in Ray.

Dynamic Remote Parameters

You can dynamically adjust resource requirements or return values of ray.remote during execution with .options.

For example, here we instantiate many copies of the same actor with varying resource requirements. Note that to create these actors successfully, Ray will need to be started with sufficient CPU resources and the relevant custom resources:

class Counter(object):
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

a1 = Counter.options(num_cpus=1, resources={"Custom1": 1}).remote()
a2 = Counter.options(num_cpus=2, resources={"Custom2": 1}).remote()
a3 = Counter.options(num_cpus=3, resources={"Custom3": 1}).remote()

You can specify different resource requirements for tasks (but not for actor methods):

def g():
    return ray.get_gpu_ids()

object_gpu_ids = g.remote()
assert ray.get(object_gpu_ids) == [0]

dynamic_object_gpu_ids = g.options(num_cpus=1, num_gpus=1).remote()
assert ray.get(dynamic_object_gpu_ids) == [0]

And vary the number of return values for tasks (and actor methods too):

def f(n):
    return list(range(n))

id1, id2 = f.options(num_returns=2).remote(2)
assert ray.get(id1) == 0
assert ray.get(id2) == 1

And specify a name for tasks (and actor methods too) at task submission time:

import setproctitle

def f(x):
   assert setproctitle.getproctitle() == "ray::special_f"
   return x + 1

obj = f.options(name="special_f").remote(3)
assert ray.get(obj) == 4

This name will appear as the task name in the machine view of the dashboard, will appear as the worker process name when this task is executing (if a Python task), and will appear as the task name in the logs.


Accelerator Types

Ray supports resource specific accelerator types. The accelerator_type field can be used to force to a task to run on a node with a specific type of accelerator. Under the hood, the accelerator type option is implemented as a custom resource demand of "accelerator_type:<type>": 0.001. This forces the task to be placed on a node with that particular accelerator type available. This also lets the multi-node-type autoscaler know that there is demand for that type of resource, potentially triggering the launch of new nodes providing that accelerator.

from ray.accelerators import NVIDIA_TESLA_V100

@ray.remote(num_gpus=1, accelerator_type=NVIDIA_TESLA_V100)
def train(data):
    return "This function was run on a node with a Tesla V100 GPU"

See ray.util.accelerators to see available accelerator types. Current automatically detected accelerator types include Nvidia GPUs.

Overloaded Functions

Ray Java API supports calling overloaded java functions remotely. However, due to the limitation of Java compiler type inference, one must explicitly cast the method reference to the correct function type. For example, consider the following.

Overloaded normal task call:

public static class MyRayApp {

  public static int overloadFunction() {
    return 1;

  public static int overloadFunction(int x) {
    return x;

// Invoke overloaded functions.
Assert.assertEquals((int) Ray.task((RayFunc0<Integer>) MyRayApp::overloadFunction).remote().get(), 1);
Assert.assertEquals((int) Ray.task((RayFunc1<Integer, Integer>) MyRayApp::overloadFunction, 2).remote().get(), 2);

Overloaded actor task call:

public static class Counter {
  protected int value = 0;

  public int increment() {
    this.value += 1;
    return this.value;

public static class CounterOverloaded extends Counter {
  public int increment(int diff) {
    super.value += diff;
    return super.value;

  public int increment(int diff1, int diff2) {
    super.value += diff1 + diff2;
    return super.value;
ActorHandle<CounterOverloaded> a = Ray.actor(CounterOverloaded::new).remote();
// Call an overloaded actor method by super class method reference.
Assert.assertEquals((int) a.task(Counter::increment).remote().get(), 1);
// Call an overloaded actor method, cast method reference first.
a.task((RayFunc1<CounterOverloaded, Integer>) CounterOverloaded::increment).remote();
a.task((RayFunc2<CounterOverloaded, Integer, Integer>) CounterOverloaded::increment, 10).remote();
a.task((RayFunc3<CounterOverloaded, Integer, Integer, Integer>) CounterOverloaded::increment, 10, 10).remote();
Assert.assertEquals((int) a.task(Counter::increment).remote().get(), 33);

Cython Code in Ray

To use Cython code in Ray, run the following from directory $RAY_HOME/examples/cython:

pip install scipy # For BLAS example
pip install -e .
python cython_main.py --help

You can import the cython_examples module from a Python script or interpreter.


  • You must include the following two lines at the top of any *.pyx file:

# cython: embedsignature=True, binding=True
  • You cannot decorate Cython functions within a *.pyx file (there are ways around this, but creates a leaky abstraction between Cython and Python that would be very challenging to support generally). Instead, prefer the following in your Python code:

some_cython_func = ray.remote(some_cython_module.some_cython_func)
  • You cannot transfer memory buffers to a remote function (see example8, which currently fails); your remote function must return a value

  • Have a look at cython_main.py, cython_simple.pyx, and setup.py for examples of how to call, define, and build Cython code, respectively. The Cython documentation is also very helpful.

  • Several limitations come from Cython’s own unsupported Python features.

  • We currently do not support compiling and distributing Cython code to ray clusters. In other words, Cython developers are responsible for compiling and distributing any Cython code to their cluster (much as would be the case for users who need Python packages like scipy).

  • For most simple use cases, developers need not worry about Python 2 or 3, but users who do need to care can have a look at the language_level Cython compiler directive (see here).

Inspecting Cluster State

Applications written on top of Ray will often want to have some information or diagnostics about the cluster. Some common questions include:

  1. How many nodes are in my autoscaling cluster?

  2. What resources are currently available in my cluster, both used and total?

  3. What are the objects currently in my cluster?

For this, you can use the global state API.

Node Information

To get information about the current nodes in your cluster, you can use ray.nodes():


Get a list of the nodes in the cluster (for debugging only).


Information about the Ray clients in the cluster.

DeveloperAPI: This API may change across minor Ray releases.

import ray



[{'NodeID': '2691a0c1aed6f45e262b2372baf58871734332d7',
  'Alive': True,
  'NodeManagerAddress': '',
  'NodeManagerHostname': 'host-MBP.attlocal.net',
  'NodeManagerPort': 58472,
  'ObjectManagerPort': 52383,
  'ObjectStoreSocketName': '/tmp/ray/session_2020-08-04_11-00-17_114725_17883/sockets/plasma_store',
  'RayletSocketName': '/tmp/ray/session_2020-08-04_11-00-17_114725_17883/sockets/raylet',
  'MetricsExportPort': 64860,
  'alive': True,
  'Resources': {'CPU': 16.0, 'memory': 100.0, 'object_store_memory': 34.0, 'node:': 1.0}}]

The above information includes:

  • NodeID: A unique identifier for the raylet.

  • alive: Whether the node is still alive.

  • NodeManagerAddress: PrivateIP of the node that the raylet is on.

  • Resources: The total resource capacity on the node.

  • MetricsExportPort: The port number at which metrics are exposed to through a Prometheus endpoint.

Resource Information

To get information about the current total resource capacity of your cluster, you can use ray.cluster_resources().


Get the current total cluster resources.

Note that this information can grow stale as nodes are added to or removed from the cluster.


A dictionary mapping resource name to the total quantity of that

resource in the cluster.

DeveloperAPI: This API may change across minor Ray releases.

To get information about the current available resource capacity of your cluster, you can use ray.available_resources().


Get the current available cluster resources.

This is different from cluster_resources in that this will return idle (available) resources rather than total resources.

Note that this information can grow stale as tasks start and finish.


A dictionary mapping resource name to the total quantity of that

resource in the cluster.

DeveloperAPI: This API may change across minor Ray releases.