Placement Groups

Placement groups allow users to atomically reserve groups of resources across multiple nodes (i.e., gang scheduling). They can be then used to schedule Ray tasks and actors to be packed as close as possible for locality (PACK), or spread apart (SPREAD).

Here are some use cases:

  • Gang Scheduling: Your application requires all tasks/actors to be scheduled and start at the same time.

  • Maximizing data locality: You’d like to place or schedule your tasks and actors close to your data to avoid object transfer overheads.

  • Load balancing: To improve application availability and avoid resource overload, you’d like to place your actors or tasks into different physical machines as much as possible.

Key Concepts

A bundle is a collection of “resources”, i.e. {“GPU”: 4}.

  • A bundle must be able to fit on a single node on the Ray cluster.

  • Bundles are then placed according to the “placement group strategy” across nodes on the cluster.

A placement group is a collection of bundles.

  • Each bundle is given an “index” within the placement group

  • Bundles are then placed according to the “placement group strategy” across nodes on the cluster.

  • After the placement group is created, tasks or actors can be then scheduled according to the placement group and even on individual bundles.

A placement group strategy is an algorithm for selecting nodes for bundle placement. Read more about placement strategies.

Starting a placement group

Ray placement group can be created via the ray.util.placement_group API. Placement groups take in a list of bundles and a placement strategy:

# Import placement group APIs.
from ray.util.placement_group import (
    placement_group,
    placement_group_table,
    remove_placement_group
)

# Initialize Ray.
import ray
ray.init(num_gpus=2, resources={"extra_resource": 2})

bundle1 = {"GPU": 2}
bundle2 = {"extra_resource": 2}

pg = placement_group([bundle1, bundle2], strategy="STRICT_PACK")

Important

Each bundle must be able to fit on a single node on the Ray cluster.

Placement groups are atomically created - meaning that if there exists a bundle that cannot fit in any of the current nodes, then the entire placement group will not be ready.

# Wait until placement group is created.
ray.get(pg.ready())

# You can also use ray.wait.
ready, unready = ray.wait([pg.ready()], timeout=0)

# You can look at placement group states using this API.
pprint(placement_group_table(pg))

Infeasible placement groups will be pending until resources are available. The Ray Autoscaler will be aware of placement groups, and auto-scale the cluster to ensure pending groups can be placed as needed.

Strategy types

Ray currently supports the following placement group strategies:

STRICT_PACK

All bundles must be placed into a single node on the cluster.

PACK

All provided bundles are packed onto a single node on a best-effort basis. If strict packing is not feasible (i.e., some bundles do not fit on the node), bundles can be placed onto other nodes nodes.

STRICT_SPREAD

Each bundle must be scheduled in a separate node.

SPREAD

Each bundle will be spread onto separate nodes on a best effort basis. If strict spreading is not feasible, bundles can be placed overlapping nodes.

Quick Start

Let’s see an example of using placement group. Note that this example is done within a single node.

import ray
from pprint import pprint

# Import placement group APIs.
from ray.util.placement_group import (
    placement_group,
    placement_group_table,
    remove_placement_group
)

ray.init(num_gpus=2, resources={"extra_resource": 2})

Let’s create a placement group. Recall that each bundle is a collection of resources, and tasks or actors can be scheduled on each bundle.

Note

When specifying bundles,

  • “CPU” will correspond with num_cpus as used in ray.remote

  • “GPU” will correspond with num_gpus as used in ray.remote

  • “MEM” will correspond with memory as used in ray.remote

  • Other resources will correspond with resources as used in ray.remote.

Once the placement group reserves resources, original resources are unavailable until the placement group is removed. For example:

# Two "CPU"s are available.
ray.init(num_cpus=2)

# Create a placement group.
pg = placement_group([{"CPU": 2}])
ray.get(pg.ready())

# Now, 2 CPUs are not available anymore because they are pre-reserved by the placement group.
@ray.remote(num_cpus=2)
def f():
    return True

# Won't be scheduled because there are no 2 cpus.
f.remote()

# Will be scheduled because 2 cpus are reserved by the placement group.
f.options(placement_group=pg).remote()
gpu_bundle = {"GPU": 2}
extra_resource_bundle = {"extra_resource": 2}

# Reserve bundles with strict pack strategy.
# It means Ray will reserve 2 "GPU" and 2 "extra_resource" on the same node (strict pack) within a Ray cluster.
# Using this placement group for scheduling actors or tasks will guarantee that they will
# be colocated on the same node.
pg = placement_group([gpu_bundle, extra_resource_bundle], strategy="STRICT_PACK")

# Wait until placement group is created.
ray.get(pg.ready())

Now let’s define an actor that uses GPU. We’ll also define a task that use extra_resources.

@ray.remote(num_gpus=1)
class GPUActor:
    def __init__(self):
        pass

@ray.remote(resources={"extra_resource": 1})
def extra_resource_task():
    import time
    # simulate long-running task.
    time.sleep(10)

# Create GPU actors on a gpu bundle.
gpu_actors = [GPUActor.options(
        placement_group=pg,
        # This is the index from the original list.
        placement_group_bundle_index=0) # Index of gpu_bundle is 0.
    .remote() for _ in range(2)]

# Create extra_resource actors on a extra_resource bundle.
extra_resource_actors = [extra_resource_task.options(
        placement_group=pg,
        # This is the index from the original list.
        placement_group_bundle_index=1) # Index of extra_resource_bundle is 1.
    .remote() for _ in range(2)]

Now, you can guarantee all gpu actors and extra_resource tasks are located on the same node because they are scheduled on a placement group with the STRICT_PACK strategy.

Note that you must remove the placement group once you are finished with your application. Workers of actors and tasks that are scheduled on placement group will be all killed.

Warning

Do not lose the reference to the placement group - you will not be able to remove it. This behavior will change in a later release.

# This API is asynchronous.
remove_placement_group(pg)

# Wait until placement group is killed.
import time
time.sleep(1)
# Check the placement group has died.
pprint(placement_group_table(pg))

"""
{'bundles': {0: {'GPU': 2.0}, 1: {'extra_resource': 2.0}},
'name': 'unnamed_group',
'placement_group_id': '40816b6ad474a6942b0edb45809b39c3',
'state': 'REMOVED',
'strategy': 'STRICT_PACK'}
"""

ray.shutdown()

Lifecycle

When placement group is first created, the request is sent to the GCS. The GCS reserve resources to nodes based on its scheduling strategy. Ray guarantees the atomic creation of placement group.

Placement groups are pending creation if there are no nodes that can satisfy resource requirements for a given strategy. The Ray Autoscaler will be aware of placement groups, and auto-scale the cluster to ensure pending groups can be placed as needed.

If nodes that contain some bundles of a placement group die, bundles will be rescheduled on different nodes by GCS. This means that the initial creation of placement group is “atomic”, but once it is created, there could be partial placement groups.

Placement groups are tolerant to worker nodes failures (bundles on dead nodes are rescheduled). However, placement groups are currently unable to tolerate head node failures (GCS failures).