Ray Train XGBoostTrainer on VMs#


To learn the basics of Ray on VMs, 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 AWS. The similar steps can be used to deploy on GCP or Azure as well.

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.

VM cluster setup#

For the workload in this guide, it is recommended to use the following setup:

  • 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).

The corresponding cluster configuration file is as follows:

# This is a Ray cluster configuration for exploration of the 100Gi Ray XGBoostTrainer benchmark.

# The configuration includes 1 Ray head node and 9 worker nodes.

cluster_name: ray-cluster-xgboost-benchmark

# The maximum number of worker nodes to launch in addition to the head
# node.
max_workers: 9

  image: "rayproject/ray-ml:2.0.0"
  container_name: "ray_container"

  type: aws
  region: us-west-2
  availability_zone: us-west-2a

  ssh_user: ubuntu

  # Configurations for the head node.
      InstanceType: m5.4xlarge
      ImageId: latest_dlami
        - DeviceName: /dev/sda1
            VolumeSize: 1000

  # Configurations for the worker nodes.
    # To experiment with autoscaling, set min_workers to 0.
    # min_workers: 0
    min_workers: 9
    max_workers: 9
      InstanceType: m5.4xlarge
      ImageId: latest_dlami
        - DeviceName: /dev/sda1
            VolumeSize: 1000

head_node_type: head

Optional: Set up an autoscaling cluster

If you would like to try running the workload with autoscaling enabled, change min_workers of worker nodes to 0. After the workload is submitted, 9 workers nodes will scale up to accommodate the workload. These nodes will scale back down after the workload is complete.

Deploy a Ray cluster#

Now we’re ready to deploy the Ray cluster with the configuration that’s defined above. Before running the command, make sure your aws credentials are configured correctly.

ray up -y cluster.yaml

A Ray head node and 9 Ray worker nodes will be created.

Run the 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.

ray dashboard cluster.yaml

This will forward remote port 8265 to port 8265 on localhost.

Submit the workload#

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

from ray.job_submission import JobSubmissionClient

client = JobSubmissionClient("")

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/train_tests/xgboost_lightgbm/train_batch_inference_benchmark.py"
    " xgboost --size=100G --disable-check"

submission_id = client.submit_job(

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.

# Download the above script.
curl https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/doc_code/xgboost_submit.py -o xgboost_submit.py
# Run the script.
python xgboost_submit.py

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' --address="http://localhost:8265" --follow

Ray Dashboard#

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

Ray Status#

Observe autoscaling status and Ray resource usage with

ray exec cluster.yaml 'ray status'

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 node contains the parameters for the trained model. Other result data will be available in the directory ray_results in the head node. Refer to the XGBoostTrainer documentation for details.


If autoscaling is enabled, Ray worker nodes will scale down after the specified idle timeout.


Delete your Ray cluster with the following command:

ray down -y cluster.yaml