Testing autoscaling locally#

Testing autoscaling behavior is important for autoscaler development and for debugging applications that depend on autoscaler behavior. You can run the autoscaler locally, without launching a real cluster, using one of the following methods:

Using RAY_FAKE_CLUSTER=1 ray start#

Complete the following steps:

  1. Navigate to the root directory of the Ray repo you have cloned locally.

  2. Locate the fake_multi_node/example.yaml example file and fill in the number of CPUs and GPUs the local machine has for the head node type config. The YAML follows the same format as cluster autoscaler configurations, but some fields are not supported.

  3. Configure worker types and other autoscaling configs as desired in the YAML file.

  4. Start the fake cluster locally:

$ ray stop --force
$ RAY_FAKE_CLUSTER=1 ray start \
    --autoscaling-config=./python/ray/autoscaler/_private/fake_multi_node/example.yaml \
    --head --block

  1. Connect your application to the fake local cluster with ray.init("auto").

  2. Run ray status to view the status of your cluster, or cat /tmp/ray/session_latest/logs/monitor.* to view the autoscaler monitor log:

$ ray status
======== Autoscaler status: 2021-10-12 13:10:21.035674 ========
Node status
---------------------------------------------------------------
Healthy:
 1 ray.head.default
 2 ray.worker.cpu
Pending:
 (no pending nodes)
Recent failures:
 (no failures)

Resources
---------------------------------------------------------------
Usage:
 0.0/10.0 CPU
 0.00/70.437 GiB memory
 0.00/10.306 GiB object_store_memory

Demands:
 (no resource demands)

Using ray.cluster_utils.AutoscalingCluster#

To programmatically create a fake multi-node autoscaling cluster and connect to it, you can use cluster_utils.AutoscalingCluster. Here’s an example of a basic autoscaling test that launches tasks triggering autoscaling:

cluster = AutoscalingCluster(
    head_resources={"CPU": 2},
    worker_node_types={
        "cpu_node": {
            "resources": {
                "CPU": 4,
                "object_store_memory": 1024 * 1024 * 1024,
            },
            "node_config": {},
            "min_workers": 0,
            "max_workers": 2,
        },
        "gpu_node": {
            "resources": {
                "CPU": 2,
                "GPU": 1,
                "object_store_memory": 1024 * 1024 * 1024,
            },
            "node_config": {},
            "min_workers": 0,
            "max_workers": 2,
        },
        "tpu_node": {
            "resources": {
                "CPU": 2,
                "TPU": 4,
                "object_store_memory": 1024 * 1024 * 1024,
            },
            "node_config": {},
            "min_workers": 0,
            "max_workers": 2,
        },
        "tpu_v5e_node": {
            "resources": {
                "CPU": 4,
                "TPU": 8,
                "object_store_memory": 1024 * 1024 * 1024,
            },
            "node_config": {},
            "min_workers": 0,
            "max_workers": 2,
        },
        "tpu_v6e_node": {
            "resources": {
                "CPU": 4,
                "TPU": 8,
                "object_store_memory": 1024 * 1024 * 1024,
            },
            "node_config": {},
            "min_workers": 0,
            "max_workers": 2,
        },
    },
    autoscaler_v2=autoscaler_v2,
)

try:
    cluster.start()
    ray.init("auto")

    # Triggers the addition of a GPU node.
    @ray.remote(num_gpus=1)
    def f():
        print("gpu ok")

    # Triggers the addition of a CPU node.
    @ray.remote(num_cpus=3)
    def g():
        print("cpu ok")

    # Triggers the addition of a TPU node.
    @ray.remote(resources={"TPU": 4})
    def h():
        print("tpu ok")

    # Triggers the addition of a 8-chip TPU node.
    @ray.remote(resources={"TPU": 8})
    def i():
        print("8-chip tpu ok")

    ray.get(f.remote())
    ray.get(g.remote())
    ray.get(h.remote())
    ray.get(i.remote())
    ray.shutdown()
finally:
    cluster.shutdown()

Python documentation:

class ray.cluster_utils.AutoscalingCluster(head_resources: dict, worker_node_types: dict, autoscaler_v2: bool = False, **config_kwargs)[source]#

Create a local autoscaling cluster for testing.

See test_autoscaler_fake_multinode.py for an end-to-end example.

DeveloperAPI: This API may change across minor Ray releases.

start(_system_config=None, override_env: Dict | None = None)[source]#

Start the cluster.

After this call returns, you can connect to the cluster with ray.init(“auto”).

shutdown()[source]#

Terminate the cluster.

Features and limitations of fake_multinode#

Most of the features of the autoscaler are supported in fake multi-node mode. For example, if you update the contents of the YAML file, the autoscaler picks up the new configuration and applies changes, as it does in a real cluster. Node selection, launch, and termination are governed by the same bin-packing and idle timeout algorithms as in a real cluster.

However, there are a few limitations:

  1. All node raylets run uncontainerized on the local machine, so they share the same IP address. See the fake_multinode_docker section for an alternative local multi-node setup.

  2. Configurations for auth, setup, initialization, Ray start, file sync, and anything cloud-specific aren’t supported.

  3. You must limit the number of nodes, node CPU, and object store memory to avoid overloading your local machine.

Testing containerized multi-node clusters locally with Docker Compose#

To go one step further and test a multi-node setup locally where each node uses its own container, with a separate filesystem, IP address, and Ray processes, you can use the fake_multinode_docker node provider.

The setup is similar to the fake_multinode provider. However, you need to start a monitoring process (docker_monitor.py) that runs the docker compose command.

Prerequisites:

  1. Make sure you have Docker installed.

  2. Make sure you have the Docker Compose V2 plugin installed.

Using RAY_FAKE_CLUSTER=1 ray up#

Complete the following steps:

  1. Navigate to the root directory of the Ray repo you have cloned locally.

  2. Locate the fake_multi_node/example_docker.yaml example file and fill in the number of CPUs and GPUs the local machine has for the head node type config. The YAML follows the same format as cluster autoscaler configurations, but some fields are not supported.

  3. Configure worker types and other autoscaling configs as desired in the YAML file.

  4. Make sure the shared_volume_dir is empty on the host system.

  5. Start the monitoring process:

$ python ./python/ray/autoscaler/_private/fake_multi_node/docker_monitor.py \
    ./python/ray/autoscaler/_private/fake_multi_node/example_docker.yaml

  1. Start the Ray cluster using ray up:

$ RAY_FAKE_CLUSTER=1 ray up -y ./python/ray/autoscaler/_private/fake_multi_node/example_docker.yaml

  1. Connect your application to the fake local cluster with ray.init("ray://localhost:10002").

  2. Alternatively, get a shell on the head node:

$ docker exec -it fake_docker_fffffffffffffffffffffffffffffffffffffffffffffffffff00000_1 bash

Using ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster#

You use this utility to write tests that use multi-node behavior. Use the DockerCluster class to set up a Docker Compose cluster in a temporary directory, start the monitoring process, wait for the cluster to come up, connect to it, and update the configuration.

See the API documentation and example test cases for how to use this utility.

class ray.autoscaler._private.fake_multi_node.test_utils.DockerCluster(config: Dict[str, Any] | None = None)[source]#

Docker cluster wrapper.

Creates a directory for starting a fake multinode docker cluster.

Includes APIs to update the cluster config as needed in tests, and to start and connect to the cluster.

connect(client: bool = True, timeout: int = 120, **init_kwargs)[source]#

Connect to the docker-compose Ray cluster.

Assumes the cluster is at RAY_TESTHOST (defaults to 127.0.0.1).

Parameters:

  • client – If True, uses Ray client to connect to the cluster. If False, uses GCS to connect to the cluster.

  • timeout – Connection timeout in seconds.

  • **init_kwargs – kwargs to pass to ray.init().

remote_execution_api() → RemoteAPI[source]#

Create an object to control cluster state from within the cluster.

static wait_for_resources(resources: Dict[str, float], timeout: int = 60)[source]#

Wait until Ray cluster resources are available

Parameters:

  • resources – Minimum resources needed before this function returns.

  • timeout – Timeout in seconds.

update_config(config: Dict[str, Any] | None = None)[source]#

Update autoscaling config.

Does a deep update of the base config with a new configuration. This can change autoscaling behavior.

Parameters:

config – Partial config to update current config with.

setup()[source]#

Setup docker compose cluster environment.

Creates the temporary directory, writes the initial config file, and pulls the docker image, if required.

teardown(keep_dir: bool = False)[source]#

Tear down docker compose cluster environment.

Parameters:

keep_dir – If True, cluster directory will not be removed after termination.

start()[source]#

Start docker compose cluster.

Starts the monitor process and runs ray up.

stop()[source]#

Stop docker compose cluster.

Runs ray down and stops the monitor process.

kill_node(node_id: str | None = None, num: int | None = None, rand: str | None = None)[source]#

Kill node.

If node_id is given, kill that node.

If num is given, construct node_id from this number, and kill that node.

If rand is given (as either worker or any), kill a random node.

Features and limitations of fake_multinode_docker#

The fake multinode docker node provider creates fully fledged nodes in their own containers. However, some limitations remain:

  1. Configurations for auth, setup, initialization, Ray start, file sync, and anything cloud-specific aren’t supported, but might be in the future.

  2. You must limit the number of nodes, node CPU, and object store memory to avoid overloading your local machine.

  3. In docker-in-docker setups, you must follow a careful setup to make the fake multinode docker provider work. See the following sections.

Shared directories within the Docker environment#

The containers mount two locations to host storage:

  • /cluster/node: This location (in the container) points to cluster_dir/nodes/<node_id> (on the host). This location is individual per node, so the host can examine contents stored in this directory.

  • /cluster/shared: This location (in the container) points to cluster_dir/shared (on the host). This location is shared across nodes and effectively acts as a shared filesystem (comparable to NFS).

Setting up in a Docker-in-Docker (dind) environment#

When setting up in a Docker-in-Docker (dind) environment, such as the Ray OSS Buildkite environment, keep some things in mind. To make this clear, consider these concepts:

  • The host is the non-containerized machine that executes the code, for example a Buildkite runner.

  • The outer container is the container running directly on the host. In the Ray OSS Buildkite environment, two containers are started: a dind network host and a container with the Ray source code and wheel.

  • The inner container is a container started by the fake multinode docker node provider.

The control plane for the multinode docker node provider lives in the outer container. However, docker compose commands run from the connected docker-in-docker network. In the Ray OSS Buildkite environment, this is the dind-daemon container running on the host docker. For example, if you mounted /var/run/docker.sock from the host instead, it would be the host docker daemon. We refer to both as the host daemon from now on.

The outer container modifies files that must be mounted in the inner containers and modified from there as well. This means that the host daemon must also have access to these files.

Similarly, the inner containers expose ports, but because the host daemon starts the containers, the ports are only accessible on the host or the dind container.

For the Ray OSS Buildkite environment, we set the following environment variables:

  • RAY_TEMPDIR="/ray-mount". This environment variable defines where the temporary directory for the cluster files should be created. This directory must be accessible to the host, the outer container, and the inner container. In the inner container, we can control the directory name.

  • RAY_HOSTDIR="/ray". When the shared directory has a different name on the host, we can rewrite the mount points dynamically. In this example, the outer container is started with -v /ray:/ray-mount or similar, so the directory on the host is /ray and in the outer container /ray-mount (see RAY_TEMPDIR).

  • RAY_TESTHOST="dind-daemon". Because the host daemon starts the containers, we can’t connect to localhost. The ports aren’t exposed to the outer container. Thus, we can set the Ray host with this environment variable.

Lastly, Docker Compose requires a Docker image. The default Docker image is rayproject/ray:nightly. The Docker image requires openssh-server to be installed and enabled. In Buildkite, we build a new image from rayproject/ray:nightly-py38-cpu to avoid installing this on the fly for every node, which is the default. This base image is built in one of the previous build steps.

Thus, we set

  • RAY_DOCKER_IMAGE="rayproject/ray:multinode-py38"

  • RAY_HAS_SSH=1

to use this Docker image and inform our multinode infrastructure that SSH is already installed.

Local development#

If you’re doing local development on the fake multi-node docker module, you can set

  • FAKE_CLUSTER_DEV="auto"

This mounts the ray/python/ray/autoscaler directory to the started nodes. Note that this probably won’t work in your docker-in-docker setup.

If you want to specify which top-level Ray directories to mount, you can use:

  • FAKE_CLUSTER_DEV_MODULES="autoscaler,tune"

This mounts both ray/python/ray/autoscaler and ray/python/ray/tune within the node containers. The list of modules should be comma-separated and without spaces.