Amazon Search Doubles ML Training Speed with AWS Batch

Introduction

Amazon Search is a cornerstone of the Amazon retail ecosystem, powering millions of product queries every day. Behind the scenes, the search experience relies on sophisticated machine learning models that must be trained, fine‑tuned, and deployed at scale. As the volume of data and the complexity of models grew, the team faced a persistent bottleneck: GPU‑accelerated training jobs were underutilized, leading to higher costs and slower iteration cycles. The challenge was to orchestrate training workloads across a fleet of GPU‑enabled instances—P5, P4, and others—while maximizing throughput and minimizing idle time.

The solution emerged from a deeper look at how workloads were submitted and managed. Traditional approaches had relied on manual provisioning of SageMaker training jobs, which left a lot of room for inefficiency. By introducing AWS Batch into the workflow, Amazon Search was able to automate the scheduling of SageMaker jobs, automatically scale GPU resources, and enforce fine‑grained resource constraints. The result was a two‑fold increase in training speed, a dramatic reduction in cost per training run, and a more predictable training pipeline that could keep pace with the rapid development of new search models.

This post walks through the architecture, the key decisions that led to the performance gains, and a step‑by‑step guide to replicating the setup in your own environment. Whether you are a data scientist, a DevOps engineer, or a product manager looking to accelerate ML training, the insights here will help you understand how to leverage AWS Batch to orchestrate SageMaker training jobs at scale.

The Challenge

When a SageMaker training job is launched directly, it typically sits in a queue until an appropriate GPU instance becomes available. Once the instance is allocated, the job runs for a fixed duration, but the instance remains idle for the remainder of the billing hour. This leads to a high cost of ownership and a low utilization rate of expensive GPU hardware. Additionally, the manual process of launching and monitoring jobs introduces human error and makes it difficult to enforce consistent resource limits across teams.

Key pain points included:

• Idle GPU time: Jobs often finished well before the hour ended, leaving GPUs under‑used.
• Resource fragmentation: Different teams requested different instance types, causing a fragmented pool that was hard to optimize.
• Manual scaling: Scaling up required manual intervention, leading to delays and inconsistent performance.
• Limited observability: Tracking job status, failures, and resource usage was scattered across notebooks, CI pipelines, and the SageMaker console.

Why AWS Batch?

AWS Batch provides a managed job queueing and scheduling service that can automatically provision the right compute resources based on job requirements. By defining job definitions that specify the Docker image, environment variables, and GPU requirements, Batch can place jobs on the most appropriate instance family. Batch’s integration with the EC2 Spot Fleet and on‑demand instances allows for cost‑effective scaling, while its ability to enforce retry policies and job dependencies ensures reliability.

For Amazon Search, Batch became the glue that connected the data engineering pipeline, the model training scripts, and the SageMaker training service. Instead of launching a SageMaker job directly from a notebook or a CI pipeline, the team would submit a Batch job that, in turn, invoked the SageMaker training API. This indirection provided a single point of control for resource allocation, logging, and monitoring.

Architecture Overview

+---------------------+          +---------------------+
|  Data Engineering   |          |  Model Development  |
|  Pipeline (Glue,   |          |  Scripts (Python)   |
|  Lambda, etc.)      |          +---------------------+
+----------+----------+                    |
           |                               |
           v                               v
+---------------------+          +---------------------+
|  AWS Batch Queue    |          |  SageMaker Training |
|  (Job Definitions)  |          |  Service (P5/P4)    |
+----------+----------+          +----------+----------+
           |                               |
           v                               v
+---------------------+          +---------------------+
|  EC2 Spot Fleet     |          |  CloudWatch Logs    |
|  (GPU Instances)    |          |  CloudWatch Metrics |
+---------------------+          +---------------------+

1. Job Submission – A data engineer or a CI pipeline pushes a new training job into the Batch queue.
2. Job Definition – The Batch job definition pulls the latest training script from S3, sets environment variables, and defines GPU requirements.
3. Instance Provisioning – Batch evaluates the job’s resource needs and requests the appropriate instance type from an EC2 Spot Fleet. If a Spot instance is unavailable, Batch falls back to on‑demand.
4. SageMaker Invocation – Once the instance is ready, the Batch container runs a lightweight wrapper that calls the SageMaker training API, passing in the training script, hyperparameters, and data locations.
5. Training Execution – SageMaker runs the training job on the allocated GPU instance, streams logs to CloudWatch, and writes checkpoints to S3.
6. Cleanup – After training completes, the Batch job terminates the Spot instance, ensuring no idle GPU time remains.

Key Design Decisions

1. Unified Job Definition

All training jobs share a single Batch job definition that references a Docker image containing the AWS SDK, the SageMaker CLI, and the wrapper script. By keeping the job definition lightweight and version‑controlled, the team avoided the need to update Batch every time a new model was added.

2. Spot‑First, On‑Demand Backup

Spot instances offered up to 70% cost savings compared to on‑demand. Batch’s built‑in Spot Fleet integration allowed the team to specify a spot‑price threshold. If the spot market price exceeded the threshold, Batch automatically switched to on‑demand instances, guaranteeing job completion.

3. Fine‑Grained Resource Constraints

The wrapper script parsed a JSON manifest that described the required instance type, number of GPUs, and memory. This manifest was passed as an environment variable to the Batch job, ensuring that each job requested exactly what it needed and no more.

4. Retry and Dependency Policies

Batch’s retry strategy was set to three attempts with exponential back‑off. Additionally, training jobs that depended on data preprocessing were chained using job dependencies, ensuring that data was ready before training began.

5. Centralized Logging and Metrics

All logs from the wrapper, SageMaker training, and Spot instance lifecycle events were forwarded to CloudWatch Logs. Custom metrics such as GPU utilization, training duration, and cost per hour were published to CloudWatch Metrics, enabling real‑time dashboards.

Performance Gains

After implementing the Batch‑based workflow, the team observed the following improvements:

• Throughput: The number of training jobs completed per day doubled, from 12 to 24.
• GPU Utilization: Idle GPU time dropped from 35% to 8%.
• Cost Savings: Monthly GPU compute cost fell from $120,000 to $65,000.
• Predictability: Job start‑to‑finish time variance reduced from 45 minutes to 10 minutes.

These gains were achieved without changing the underlying model code or hyperparameters, demonstrating that orchestration can unlock significant value.

Step‑by‑Step Replication Guide

1. Create an S3 Bucket for training scripts and manifests.
2. Build a Docker Image that includes the AWS SDK, SageMaker CLI, and the wrapper script. Push it to ECR.
3. Define a Batch Job Definition:

   {
     "jobDefinitionName": "sagemaker-training",
     "type": "container",
     "containerProperties": {
       "image": "<ECR_URI>:latest",
       "vcpus": 4,
       "memory": 8192,
       "resourceRequirements": [
         {"value": "1", "type": "GPU"}
       ],
       "environment": [
         {"name": "S3_SCRIPT_BUCKET", "value": "<BUCKET>"}
       ]
     }
   }
   

4. Set Up an EC2 Spot Fleet with the desired GPU instance families and a maximum price.
5. Create a Batch Queue that uses the Spot Fleet as the compute environment.
6. Submit a Job from the CLI or SDK:

   aws batch submit-job \
     --job-name train-search-model \
     --job-queue search-training-queue \
     --job-definition sagemaker-training \
     --container-overrides environment=[{name=MODEL_NAME,value=search_v2}]
   

7. Monitor via CloudWatch Logs and Metrics.

Common Pitfalls and How to Avoid Them

• Spot Interruptions: Even with a fallback, spot interruptions can still occur. Enable SageMaker’s built‑in checkpointing to resume training.
• Version Drift: Keep the wrapper Docker image under version control. Use immutable tags for ECR images.
• Resource Over‑Allocation: Over‑requesting GPUs leads to wasted cost. Use historical data to set realistic resource requirements.
• Logging Overhead: Excessive log verbosity can inflate CloudWatch costs. Use structured logging and filter by severity.

Conclusion

By integrating AWS Batch into its training pipeline, Amazon Search transformed a fragmented, manual process into a scalable, cost‑effective workflow. The key to success lay in treating the training job as a first‑class citizen in the Batch ecosystem, allowing the platform to manage GPU provisioning, spot‑fleet economics, and job dependencies automatically. The result was a two‑fold increase in training throughput, significant cost savings, and a robust foundation that can support the next wave of search model innovations.

If you’re looking to accelerate your own machine‑learning training workloads, consider evaluating AWS Batch as an orchestration layer. The principles outlined here—spot‑first provisioning, unified job definitions, fine‑grained resource constraints, and centralized observability—are broadly applicable across many ML use cases.