Ray AIR XGBoostTrainer on Kubernetes

Note

To learn the basics of Ray on Kubernetes, we recommend taking a look at the introductory guide first.

In this guide, we show you how to run a sample Ray machine learning workload on Kubernetes infrastructure.

We will run Ray’s XGBoost training benchmark with a 100 gigabyte training set. To learn more about using Ray’s XGBoostTrainer, check out the XGBoostTrainer documentation.

Optional: Autoscaling

This guide includes notes on how to deploy the XGBoost benchmark with optional Ray Autoscaler support. In this guide’s example, we know that we need 1 Ray head and 9 Ray workers, so autoscaling is not strictly required. Read this discussion for guidance on whether to use autoscaling.

Kubernetes infrastructure setup

If you are new to Kubernetes and you are planning to deploy Ray workloads on a managed Kubernetes service, we recommend taking a look at this introductory guide first.

For the workload in this guide, it is recommended to use a pool or group of Kubernetes nodes with the following properties:

  • 10 nodes total

  • A capacity of 16 CPU and 64 Gi memory per node. For the major cloud providers, suitable instance types include

    • m5.4xlarge (Amazon Web Services)

    • Standard_D5_v2 (Azure)

    • e2-standard-16 (Google Cloud)

  • Each node should be configured with 1000 gigabytes of disk space (to store the training set).

Optional: Set up an autoscaling node pool

If you would like to try running the workload with autoscaling enabled, use an autoscaling node group or pool with a 1 node minimum and a 10 node maximum. The 1 static node will be used to run the Ray head pod. This node may also host the KubeRay operator and Kubernetes system components. After the workload is submitted, 9 additional nodes will scale up to accommodate Ray worker pods. These nodes will scale back down after the workload is complete.

Deploy the KubeRay operator

Once you have set up your Kubernetes cluster, deploy the KubeRay operator. Refer to the Getting Started guide for instructions on this step.

Deploy a Ray cluster

Now we’re ready to deploy the Ray cluster that will execute our workload.

Tip

The Ray cluster we’ll deploy is configured such that one Ray pod will be scheduled per 16-CPU Kubernetes node. The pattern of one Ray pod per Kubernetes node is encouraged, but not required. Broadly speaking, it is more efficient to use a few large Ray pods than many small ones.

We recommend taking a look at the config file applied in the following command.

# Starting from the parent directory of cloned Ray master,
pushd ray/doc/source/cluster/cluster_under_construction/ray-clusters-on-kubernetes/configs/
kubectl apply -f xgboost-benchmark.yaml
popd

A Ray head pod and 9 Ray worker pods will be created.

Optional: Deploying an autoscaling Ray cluster

If you’ve set up an autoscaling node group or pool, you may wish to deploy an autoscaling cluster by applying the config xgboost-benchmark-autoscaler.yaml. One Ray head pod will be created. Once the workload starts, the Ray autoscaler will trigger creation of Ray worker pods. Kubernetes autoscaling will then create nodes to place the Ray pods.

Run the workload

To observe the startup progress of the Ray head pod, run the following command.

# If you're on MacOS, first `brew install watch`.
watch -n 1 kubectl get pod

Once the Ray head pod enters Running state, we are ready to execute the XGBoost workload. We will use Ray Job Submission to kick off the workload.

Connect to the cluster.

First, we connect to the Job server. Run the following blocking command in a separate shell.

kubectl port-forward service/raycluster-xgboost-benchmark-head-svc 8265:8265

Submit the workload.

We’ll use the Ray Job Python SDK to submit the XGBoost workload.

from ray.job_submission import JobSubmissionClient

client = JobSubmissionClient("http://127.0.0.1:8265")

kick_off_xgboost_benchmark = (
    # Clone ray. If ray is already present, don't clone again.
    "git clone https://github.com/ray-project/ray || true;"
    # Run the benchmark.
    " python ray/release/air_tests/air_benchmarks/workloads/xgboost_benchmark.py"
    " --size 100G --disable-check"
)


submission_id = client.submit_job(
    entrypoint=kick_off_xgboost_benchmark,
)

print("Use the following command to follow this Job's logs:")
print(f"ray job logs '{submission_id}' --follow")

To submit the workload, run the above Python script. The script is available in the Ray repository.

# From the parent directory of cloned Ray master.
pushd ray/doc/source/cluster/cluster_under_construction/ray-clusters-on-kubernetes/doc_code/
python xgboost_submit.py
popd

Observe progress.

The benchmark may take up to 30 minutes to run. Use the following tools to observe its progress.

Job logs

To follow the job’s logs, use the command printed by the above submission script.

# Subsitute the Ray Job's submission id.
ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --follow

Kubectl

Observe the pods in your cluster with

# If you're on MacOS, first `brew install watch`.
watch -n 1 kubectl get pod

Ray Dashboard

View localhost:8265 in your browser to access the Ray Dashboard.

Ray Status

Observe autoscaling status and Ray resource usage with

# Substitute the name of your Ray cluster's head pod.
watch -n 1 kubectl exec -it raycluster-xgboost-benchmark-head-xxxxx -- ray status

Note

Under some circumstances and for certain cloud providers, the K8s API server may become briefly unavailable during Kuberentes cluster resizing events.

Don’t worry if that happens – the Ray workload should be uninterrupted. For the example in this guide, wait until the API server is back up, restart the port-forwarding process, and re-run the job log command.

Job completion

Benchmark results

Once the benchmark is complete, the job log will display the results:

Results: {'training_time': 1338.488839321999, 'prediction_time': 403.36653568099973}

The performance of the benchmark is sensitive to the underlying cloud infrastructure – you might not match the numbers quoted in the benchmark docs.

Model parameters

The file model.json in the Ray head pod contains the parameters for the trained model. Other result data will be available in the directory ray_results in the head pod. Refer to the the XGBoostTrainer documentation for details.

Scale-down

If autoscaling is enabled, Ray worker pods will scale down after 60 seconds. After the Ray worker pods are gone, your Kubernetes infrastructure should scale down the nodes that hosted these pods.

Clean-up

Delete your Ray cluster with the following command:

kubectl delete raycluster raycluster-xgboost-benchmark

If you’re on a public cloud, don’t forget to clean up the underlying node group and/or Kubernetes cluster.