Ray on Amazon SageMaker training jobs

This repository demonstrates how to use Ray for distributed data processing and model training within Amazon SageMaker training jobs.

Prerequisites

AWS account with Amazon SageMaker AI access
Ray 2.0.0+
SageMaker Python SDK >=3.5.0

Project Structure

ray-sagemaker-training/
├── scripts/
│    └── launcher.py
├── examples/
│    ├── ray-remote/
│    │    ├── pytorch/                    # Homogeneous cluster
│    │    │    ├── notebook.ipynb
│    │    │    └── scripts/
│    │    │         ├── train.py
│    │    │         ├── model.py
│    │    │         └── requirements.txt
│    │    └── pytorch-heterogeneous/      # Heterogeneous cluster
│    │         ├── notebook.ipynb
│    │         └── scripts/
│    │              ├── train.py
│    │              ├── model.py
│    │              └── requirements.txt
│    ├── ray-data/
│    │    ├── pytorch/
│    │    │    ├── notebook.ipynb
│    │    │    └── scripts/
│    │    │         ├── inference.py
│    │    │         ├── model.py
│    │    │         └── requirements.txt
│    │    └── pytorch-heterogeneous/
│    │         ├── notebook.ipynb
│    │         └── scripts/
│    │              ├── inference.py
│    │              ├── model.py
│    │              └── requirements.txt
│    ├── ray-tune/
│    │    ├── pytorch/
│    │    │    ├── notebook.ipynb
│    │    │    └── scripts/
│    │    │         ├── tune.py
│    │    │         ├── model.py
│    │    │         └── requirements.txt
│    │    └── pytorch-heterogeneous/
│    │         ├── notebook.ipynb
│    │         └── scripts/
│    │              ├── tune.py
│    │              ├── model.py
│    │              └── requirements.txt
│    └── ray-torchtrainer/
│         ├── huggingface/
│         │    ├── notebook.ipynb
│         │    └── scripts/
│         │         ├── train_ray.py
│         │         └── requirements.txt
│         └── huggingface-heterogeneous/
│              ├── notebook.ipynb
│              └── scripts/
│                   ├── train_ray.py
│                   └── requirements.txt
└── images/

Key Components

Launcher

The launcher.py script serves as the entry point for SageMaker training jobs and handles:

Setting up the Ray environment for both single-node and multi-node scenarios
Supporting both homogeneous and heterogeneous instance group clusters
Coordinating between head and worker nodes in a distributed setup
Configuring EFA/RDMA networking for supported GPU instances
Optionally launching Prometheus for metrics collection
Executing the appropriate user script (Python .py or Bash .sh)
Graceful shutdown with configurable wait period

Important Notes

The launcher.py script is not intended to be modified by users. This script serves as a universal entrypoint for SageMaker training jobs and handles Ray cluster setup, coordination between nodes, and execution of your custom scripts.

Ray Autoscaler is not supported. SageMaker training jobs use a fixed number of instances defined at job creation time. The Ray cluster size is determined by the SageMaker cluster configuration (instance_count or instance_groups), and cannot be dynamically scaled during execution. All nodes are provisioned at the start of the job and remain available until the job completes.

You should:

Write your own Ray scripts for data processing or model training
Use launcher.py as the entrypoint in your SageMaker jobs
Make sure your requirements.txt or your container includes ray[data,train,tune,serve] and sagemaker
Specify the custom script path using the -e / --entrypoint argument

Required Parameters and Environment Variables

The launcher.py script requires specific parameters to execute your custom training scripts. You can configure these through command line arguments or environment variables.

Parameter Reference

Argument	Type	Required	Default	Description
`-e`, `--entrypoint`	string	Yes	None	Path to your script (e.g., `train.py`, `training/train.py`, `run.sh`)
`--head-instance-group`	string	Yes*	None	Instance group name for Ray head node (heterogeneous clusters only)
`--head-num-cpus`	int	No	Instance default	Number of CPUs reserved for head node
`--head-num-gpus`	int	No	Instance default	Number of GPUs reserved for head node
`--include-dashboard`	bool	No	True	Enable Ray dashboard
`--launch-prometheus`	bool	No	False	Launch local Prometheus on the head node. Internet connectivity required
`--prometheus-path`	string	No	None	Path to prometheus binary if provided as InputData
`--wait-shutdown`	int	No	None	Seconds to wait before Ray shutdown

*Required only for heterogeneous clusters

Environment Variables Reference

All parameters above can also be set as environment variables via the environment dict in your ModelTrainer or Estimator configuration. Environment variables are used as fallback when the corresponding command line argument is not provided.

Variable	Type	Required	Description
`head_instance_group`	string	No	Alternative way to set head instance group name (heterogeneous clusters only)
`head_num_cpus`	int	No	Alternative way to set number of CPUs reserved for head node
`head_num_gpus`	int	No	Alternative way to set number of GPUs reserved for head node
`launch_prometheus`	bool	No	Alternative way to launch local Prometheus on the head node. Internet connectivity required
`prometheus_path`	string	No	Path to prometheus binary if provided as InputData
`wait_shutdown`	int	No	Alternative way to set shutdown wait time

Script definition

The entry script can be a Python (.py) or Bash (.sh) file.

Python entry scripts must contain a __main__ block:

import ray

# Your Ray code here

if __name__ == "__main__":
    # This block will be executed by the launcher
    pass

Bash entry scripts are executed directly via bash <script_path>.

Examples

The repository includes 8 example notebooks covering 4 Ray patterns, each with both homogeneous and heterogeneous cluster configurations.

Each notebook copies launcher.py into its local scripts/ directory and launches a SageMaker training job using the PySDK v3 ModelTrainer API.

Pattern	Description	Homogeneous	Heterogeneous
ray-remote	Distributed task-level parallelism using `@ray.remote` for data cleaning and PyTorch model training (sentiment classification)	1x `ml.m5.2xlarge`	1x `ml.t3.large` (head) + 2x `ml.m5.2xlarge` (workers)
ray-data	Batch inference with `ray.data` using ResNet152 on Imagenette dataset	1x `ml.m5.2xlarge`	1x `ml.t3.large` (head) + 2x `ml.m5.2xlarge` (workers)
ray-tune	Hyperparameter tuning with `ray.tune` and ASHA scheduler on CIFAR-10	1x `ml.m5.2xlarge`	1x `ml.t3.large` (head) + 2x `ml.m5.2xlarge` (workers)
ray-torchtrainer	Distributed LLM fine-tuning (LoRA/QLoRA) with `ray.train.torch.TorchTrainer`, HuggingFace Transformers, and optional MLflow/W&B tracking	1x `ml.g5.12xlarge`	1x `ml.t3.2xlarge` (head) + 4x `ml.g5.xlarge` (workers)

In heterogeneous configurations, the head node is configured as coordinator-only (head_num_cpus=0, head_num_gpus=0), while the worker instance group handles computation.

Example Usage

The launcher script has been designed to be flexible and dynamic, allowing you to specify any entry script through arguments or environment variables, rather than hardcoded imports.

Entrypoint Argument

The launcher uses one argument:

-e / --entrypoint: Path to the script to execute (Python files must contain if __name__ == "__main__": block)

Usage Examples

See the content of examples

1. Using SageMaker ModelTrainer (Recommended)

from sagemaker.train.configs import (
    Compute,
    OutputDataConfig,
    SourceCode,
    StoppingCondition,
)
from sagemaker.train.model_trainer import ModelTrainer

args = [
    "-e",
    "train.py",
    "--epochs",
    "25",
    "--learning_rate",
    "0.001",
    "--batch_size",
    "100",
]

# Define the source code configuration
source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command=f"python launcher.py {' '.join(args)}",
)

# Define compute configuration
compute_configs = Compute(
    instance_type="ml.m5.2xlarge",
    instance_count=1,
    keep_alive_period_in_seconds=0,
)

# Define training job name and output path
job_name = "train-ray-training"
output_path = f"s3://{bucket_name}/{job_name}"

# Create the ModelTrainer
model_trainer = ModelTrainer(
    training_image=image_uri,
    source_code=source_code,
    base_job_name=job_name,
    compute=compute_configs,
    stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
    output_data_config=OutputDataConfig(s3_output_path=output_path),
    role=role,
)

...

# Start the training job
model_trainer.train(input_data_config=[train_input], wait=False)

2. Heterogeneous Cluster with SageMaker ModelTrainer

from sagemaker.train.configs import (
    Compute,
    InstanceGroup,
    OutputDataConfig,
    RemoteDebugConfig,
    SourceCode,
    StoppingCondition,
)
from sagemaker.train.model_trainer import ModelTrainer

# Define instance groups with different instance types
instance_groups = [
    InstanceGroup(
        instance_group_name="head-instance-group",
        instance_type="ml.t3.large",       # CPU-only for coordination
        instance_count=1,
    ),
    InstanceGroup(
        instance_group_name="worker-instance-group-1",
        instance_type="ml.m5.2xlarge",     # Compute instances for training
        instance_count=2,
    ),
]

args = [
    "--entrypoint",
    "train.py",
    "--epochs",
    "100",
    "--learning_rate",
    "0.001",
    "--batch_size",
    "100",
]

# Define the source code configuration
source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command=f"python launcher.py {' '.join(args)}",
)

# Define compute with instance groups
compute_configs = Compute(
    instance_groups=instance_groups,
    keep_alive_period_in_seconds=0,
)

# Define training job name and output path
job_name = "train-ray-training"
output_path = f"s3://{bucket_name}/{job_name}"

# Create the ModelTrainer
model_trainer = ModelTrainer(
    training_image=image_uri,
    source_code=source_code,
    base_job_name=job_name,
    compute=compute_configs,
    stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
    output_data_config=OutputDataConfig(
        s3_output_path=output_path, compression_type="NONE"
    ),
    environment={
        "head_instance_group": "head-instance-group",  # Specify which group is the head
        "head_num_cpus": "0",   # Head node as coordinator only
        "head_num_gpus": "0",   # Head node as coordinator only
    },
    role=role,
).with_remote_debug_config(RemoteDebugConfig(enable_remote_debug=True))

...

# Start the training job
model_trainer.train(input_data_config=[train_input], wait=False)

Key environment variables for heterogeneous clusters:

head_instance_group: Specifies which instance group should act as the Ray head node
head_num_cpus: Number of CPUs to reserve for the head node (set to "0" for coordinator-only mode)
head_num_gpus: Number of GPUs to reserve for the head node (set to "0" for coordinator-only mode)

Entry Script Requirements

Your entry scripts must follow this pattern:

# my_script.py
import ray

# Your Ray code here

if __name__ == "__main__":
    # This block will be executed by the launcher
    # Ray is already initialized — use ray.cluster_resources(), @ray.remote, etc.
    pass

Ray Dashboard

For accessing the Ray Dashboard during the execution of Ray workload, we can leverage the native feature to access SageMaker training jobs by using AWS System Manager (SSM)

Step 1: Setup IAM Permissions:

Please refer to the official AWS Documentation

Step 2:

Enable remote debugging for SageMaker training jobs:

from sagemaker.train.configs import (
    CheckpointConfig,
    Compute,
    OutputDataConfig,
    RemoteDebugConfig,
    SourceCode,
    StoppingCondition,
)
from sagemaker.train.model_trainer import ModelTrainer

# Define the script to be run
source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command="python launcher.py --entrypoint train_ray.py",
)

# Define the compute
compute_configs = Compute(
    instance_type=instance_type,
    instance_count=instance_count,
    keep_alive_period_in_seconds=0,
)

...

# Define the ModelTrainer
model_trainer = ModelTrainer(
    training_image=image_uri,
    source_code=source_code,
    base_job_name=job_name,
    compute=compute_configs,
    stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
    output_data_config=OutputDataConfig(s3_output_path=output_path),
    checkpoint_config=CheckpointConfig(
        s3_uri=output_path + "/checkpoint", local_path="/opt/ml/checkpoints"
    ),
    role=role,
).with_remote_debug_config(RemoteDebugConfig(enable_remote_debug=True))

Step 3:

Access the training container, by starting a Port Forwarding to the port 8265 (Default Ray Dashboard port) with the following command:

aws ssm start-session --target sagemaker-training-job:<training-job-name>_algo-<n> \
--region <aws_region> \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["8265"],"localPortNumber":["8265"]}'

In a multi-node cluster, you can check the head node by investigating the CloudWatch logs:

2025-06-25 08:47:18,755 - __main__ - INFO - Found multiple hosts, initializing Ray as a multi-node cluster
2025-06-25 08:47:18,755 - __main__ - INFO - Head node: algo-1, Current host: algo-3

Step 4:

Access the Ray Dashboard from your browser: localhost:8265:

Observability with Prometheus and Grafana

To allow system metrics collection through Prometheus and Grafana on the SageMaker cluster during the execution of the Ray workload, we can leverage the native feature to access SageMaker training jobs by using AWS System Manager (SSM)

Prometheus and Grafana hosted in an external server

With this approach, both Prometheus and Grafana server should be deployed on an external system.

Note: Internet connectivity on the SageMaker cluster is required

Step 1: Setup IAM Permissions:

Please refer to the official AWS Documentation

Step 2:

Enable remote debugging for SageMaker training jobs:

from sagemaker.train.configs import (
    CheckpointConfig,
    Compute,
    OutputDataConfig,
    RemoteDebugConfig,
    SourceCode,
    StoppingCondition,
)
from sagemaker.train.model_trainer import ModelTrainer

# Define the script to be run
source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command="python launcher.py --entrypoint train_ray.py",
)

# Define the compute
compute_configs = Compute(
    instance_type=instance_type,
    instance_count=instance_count,
    keep_alive_period_in_seconds=0,
)

...

# Define the ModelTrainer
model_trainer = ModelTrainer(
    training_image=image_uri,
    source_code=source_code,
    base_job_name=job_name,
    compute=compute_configs,
    stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
    environment={
        "RAY_GRAFANA_HOST": "<GRAFANA_HOST>",
        "RAY_PROMETHEUS_HOST": "<PROMETHEUS_HOST>",
        "RAY_PROMETHEUS_NAME": "prometheus",
    },
    output_data_config=OutputDataConfig(s3_output_path=output_path),
    checkpoint_config=CheckpointConfig(
        s3_uri=output_path + "/checkpoint", local_path="/opt/ml/checkpoints"
    ),
    role=role,
).with_remote_debug_config(RemoteDebugConfig(enable_remote_debug=True))

Step 3 - Port Forwarding to the Prometheus port in the Grafana server environment:

To make sure your Grafana server will collect the captured metrics by Prometheus, we have to access the training container, by starting a Port Forwarding to the port 8080 (Default port where Ray exports metrics) with the following command:

aws ssm start-session --target sagemaker-training-job:<training-job-name>_algo-<n> \
--region <aws_region> \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["8080"],"localPortNumber":["<YOUR_LOCAL_PORT>"]}'

In a multi-node cluster, you can check the head node by investigating the CloudWatch logs:

2025-06-25 08:47:18,755 - __main__ - INFO - Found multiple hosts, initializing Ray as a multi-node cluster
2025-06-25 08:47:18,755 - __main__ - INFO - Head node: algo-1, Current host: algo-3

Step 4 - Configure prometheus.yml to scrape metrics on the local port:

Configure your prometheus.yml file to scrape metrics on the local port where you are forwarding the Ray metrics:

...
scrape_configs:
  - job_name: 'ray'
    static_configs:
      - targets: ['localhost:<YOUR_LOCAL_PORT>']
    metrics_path: '/metrics'
  ...

Step 5 - Port Forwarding to the Ray Dashboard port:

Access the training container, by starting a Port Forwarding to the port 8265 (Default Ray Dashboard port) with the following command:

aws ssm start-session --target sagemaker-training-job:<training-job-name>_algo-<n> \
--region <aws_region> \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["8265"],"localPortNumber":["8265"]}'

In a multi-node cluster, you can check the head node by investigating the CloudWatch logs:

2025-06-25 08:47:18,755 - __main__ - INFO - Found multiple hosts, initializing Ray as a multi-node cluster
2025-06-25 08:47:18,755 - __main__ - INFO - Head node: algo-1, Current host: algo-3

Step 6:

Access the Ray Dashboard from your browser: localhost:8265:

Local Prometheus on the SageMaker cluster and Grafana running on an external server

Ray provides the capability to run local prometheus to collect system metrics during the execution of the workload. With this approach, a Grafana server deployed on an external system is required to get access to the metric visualizations.

Note: Internet connectivity on the SageMaker cluster is required

Step 1: Setup IAM Permissions:

Please refer to the official AWS Documentation

Step 2:

Enable remote debugging for SageMaker training jobs:

from sagemaker.train.configs import (
    CheckpointConfig,
    Compute,
    OutputDataConfig,
    RemoteDebugConfig,
    SourceCode,
    StoppingCondition,
)
from sagemaker.train.model_trainer import ModelTrainer

# Define the script to be run
source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command="python launcher.py --entrypoint train_ray.py",
)

# Define the compute
compute_configs = Compute(
    instance_type=instance_type,
    instance_count=instance_count,
    keep_alive_period_in_seconds=0,
)

...

# Define the ModelTrainer
model_trainer = ModelTrainer(
    training_image=image_uri,
    source_code=source_code,
    base_job_name=job_name,
    compute=compute_configs,
    stopping_condition=StoppingCondition(max_runtime_in_seconds=18000),
    environment={
        "launch_prometheus": "true",
        "RAY_GRAFANA_HOST": "<GRAFANA_HOST>",
    },
    output_data_config=OutputDataConfig(s3_output_path=output_path),
    checkpoint_config=CheckpointConfig(
        s3_uri=output_path + "/checkpoint", local_path="/opt/ml/checkpoints"
    ),
    role=role,
).with_remote_debug_config(RemoteDebugConfig(enable_remote_debug=True))

Step 3 - Port Forwarding to the Prometheus port in the Grafana server environment:

To make sure your Grafana server will collect the captured metrics by Prometheus, we have to access the training container, by starting a Port Forwarding to the port 9090 (Default Prometheus port) with the following command:

aws ssm start-session --target sagemaker-training-job:<training-job-name>_algo-<n> \
--region <aws_region> \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["9090"],"localPortNumber":["9090"]}'

In a multi-node cluster, you can check the head node by investigating the CloudWatch logs:

2025-06-25 08:47:18,755 - __main__ - INFO - Found multiple hosts, initializing Ray as a multi-node cluster
2025-06-25 08:47:18,755 - __main__ - INFO - Head node: algo-1, Current host: algo-3

Step 4 - Port Forwarding to the Ray Dashboard port:

Access the training container, by starting a Port Forwarding to the port 8265 (Default Ray Dashboard port) with the following command:

aws ssm start-session --target sagemaker-training-job:<training-job-name>_algo-<n> \
--region <aws_region> \
--document-name AWS-StartPortForwardingSession \
--parameters '{"portNumber":["8265"],"localPortNumber":["8265"]}'

In a multi-node cluster, you can check the head node by investigating the CloudWatch logs:

2025-06-25 08:47:18,755 - __main__ - INFO - Found multiple hosts, initializing Ray as a multi-node cluster
2025-06-25 08:47:18,755 - __main__ - INFO - Head node: algo-1, Current host: algo-3

Step 5:

Access the Ray Dashboard from your browser: localhost:8265:

(Optional) Provide prometheus binary file

By default, Ray downloads the Prometheus binary from the internet when launching Prometheus for metrics collection. In environments with limited internet connectivity or for better control over dependencies, you can pre-download the Prometheus binary, upload it to S3, and provide it as a training parameter.

Step 1: Download Prometheus Binary

Download the appropriate Prometheus binary for your target environment (typically Linux AMD64 for SageMaker training instances):

wget https://github.com/prometheus/prometheus/releases/download/v3.4.2/prometheus-3.4.2.linux-amd64.tar.gz

Step 2: Upload to S3

Upload the downloaded binary to your S3 bucket:

import boto3
from sagemaker.core.helper.session_helper import Session

sagemaker_session = Session()
s3_client = boto3.client('s3')

bucket_name = sagemaker_session.default_bucket()
default_prefix = sagemaker_session.default_bucket_prefix

# Define S3 path for prometheus binary
if default_prefix:
    input_path = f"{default_prefix}/datasets/your-project-name"
else:
    input_path = f"datasets/your-project-name"

prometheus_s3_path = f"s3://{bucket_name}/{input_path}/prometheus/prometheus-3.4.2.linux-amd64.tar.gz"

# Upload the binary to S3
s3_client.upload_file(
    "./prometheus-3.4.2.linux-amd64.tar.gz",
    bucket_name,
    f"{input_path}/prometheus/prometheus-3.4.2.linux-amd64.tar.gz",
)

print(f"Prometheus binary uploaded to: {prometheus_s3_path}")

Step 3: Configure Training Input

Add the Prometheus binary as a training input channel:

from sagemaker.train.configs import InputData, S3DataSource

prometheus_input = InputData(
    channel_name="prometheus",
    data_source=S3DataSource(
        s3_data_type="S3Prefix",
        s3_uri=prometheus_s3_path,
        s3_data_distribution_type="FullyReplicated",
    ),
)

# Add to your training data inputs
data = [
    train_input,
    config_input,
    prometheus_input,
]

Step 4: Configure the launcher to use the provided binary

Pass the --prometheus-path argument pointing to where SageMaker mounts the input channel:

args = [
    "--entrypoint",
    "train_ray.py",
    "--prometheus-path",
    "/opt/ml/input/data/prometheus/prometheus-3.4.2.linux-amd64.tar.gz",
]

source_code = SourceCode(
    source_dir="./scripts",
    requirements="requirements.txt",
    command=f"python launcher.py {' '.join(args)}",
)

model_trainer = ModelTrainer(
    ...
    source_code=source_code,
    environment={
        "launch_prometheus": "true",
        "RAY_GRAFANA_HOST": "<GRAFANA_HOST>",
    },
    ...
)

Authors

Bruno Pistone - Sr. WW Gen AI/ML Specialist Solutions Architect - Amazon SageMaker AI

Giuseppe A. Porcelli - Principal, ML Specialist Solutions Architect - Amazon SageMaker AI

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CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md

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Ray on Amazon SageMaker training jobs

Table of Contents

Prerequisites

Project Structure

Key Components

Launcher

Important Notes

Required Parameters and Environment Variables

Parameter Reference

Environment Variables Reference

Script definition

Examples

Example Usage

Entrypoint Argument

Usage Examples

1. Using SageMaker ModelTrainer (Recommended)

2. Heterogeneous Cluster with SageMaker ModelTrainer

Entry Script Requirements

Ray Dashboard

Step 1: Setup IAM Permissions:

Step 2:

Step 3:

Step 4:

Observability with Prometheus and Grafana

Prometheus and Grafana hosted in an external server

Step 1: Setup IAM Permissions:

Step 2:

Step 3 - Port Forwarding to the Prometheus port in the Grafana server environment:

Step 4 - Configure prometheus.yml to scrape metrics on the local port:

Step 5 - Port Forwarding to the Ray Dashboard port:

Step 6:

Local Prometheus on the SageMaker cluster and Grafana running on an external server

Step 1: Setup IAM Permissions:

Step 2:

Step 3 - Port Forwarding to the Prometheus port in the Grafana server environment:

Step 4 - Port Forwarding to the Ray Dashboard port:

Step 5:

(Optional) Provide prometheus binary file

Step 1: Download Prometheus Binary

Step 2: Upload to S3

Step 3: Configure Training Input

Step 4: Configure the launcher to use the provided binary

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