Conducting Load Testing of Amazon SageMaker Inference Endpoints with Subprocess

Introduction

Amazon SageMaker makes it easy for data scientists and developers to build and train machine learning models without the hassle of setting up and managing infrastructure.

Once your model is trained, you can deploy it as a real-time endpoint, perfect for tasks requiring quick, interactive responses. For example, you could use a real-time endpoint to make predictions in response to user input on a website or mobile app.

However, deploying a real-time endpoint is just the first step. You need to perform load testing to ensure that your endpoint can handle the expected workload while meeting latency requirements. Load testing involves simulating a realistic workload and measuring how well your endpoint performs.

By following these load tests, you can ensure that your Amazon SageMaker endpoint performs optimally and meets your requirements for both throughput and latency.

Our Solution Architecture

We’ve set up our Amazon SageMaker endpoint to work seamlessly with AWS Lambda, with Amazon API Gateway handling requests and responses between the frontend client and the Amazon SageMaker endpoint. This integration ensures smooth communication and efficient handling of requests, allowing us to provide a seamless experience for our users.

Purpose of Load Testing

• Evaluate the resilience and efficiency of the resume parsing API under different load levels.
• Simulate realistic workload on the API.
• Identify potential bottlenecks.
• Uncover any issues affecting performance.
• Monitor API performance metrics.
• Analyze results to find out areas for improvement.

Benefits of Load Testing

• Ensure optimal performance.
• Enhance user experience.
• Mitigate risks of downtime or slowdowns.
• Critical for maintaining service reliability.
• Helps in preemptive troubleshooting.
• Guides optimization efforts for better scalability.

Implementation Steps

Python’s subprocess library is a powerful tool in your arsenal. In this blog post, we will explore how to use subprocess for parallel execution, enabling you to perform the concurrent load testing of the Amazon SageMaker inference endpoint.

The Scenario

Let’s say you have a Python script, call1.py, that you want to run multiple times simultaneously. Each instance of call1.py performs a specific task, and you want to execute them concurrently to save time and leverage your machine’s processing power.

Using Subprocess for Parallel/Concurrent Execution

We will achieve this using Python’s subprocess library. Here is a breakdown of the steps involved:

To illustrate the concept of running Python scripts (call.py) concurrently under a main script, here are simplified snapshots of the call.py script and the main script (main.py). In this example, main.py will spawn multiple instances of call.py to run concurrently.

“call.py” Script

“main.py” Script

This sequence of steps outlines how to use Python’s subprocess library to execute a command (python call1.py) multiple times in parallel. It first defines the command to be executed and the number of instances to run concurrently. Then, it spawns subprocesses for each instance, allowing them to run independently. After all subprocesses have finished their tasks, it prints a completion message. This approach maximizes CPU utilization and reduces overall execution time by running tasks concurrently.

Screenshots of Amazon CloudWatch Metrics are:

Conclusion

Python’s subprocess library offers a straightforward and efficient method for executing tasks in parallel, enabling substantial reductions in script execution time. Whether working with large datasets, performing complex calculations, or running simulations, parallel execution with subprocess empowers you to utilize your computing resources fully.

By embracing parallel execution, you can:

• Maximize CPU Utilization: Distributing tasks across multiple processes allows you to utilize available CPU cores, optimizing performance fully.
• Reduce Script Execution Time: Running tasks concurrently significantly decreases the time required to complete them, enhancing workflow efficiency.
• Handle Multiple Tasks Concurrently: With subprocess, you can seamlessly manage and execute multiple tasks simultaneously, improving multitasking capabilities.

Now equipped with the knowledge of leveraging subprocesses for parallel execution, you can streamline your Python workflows and enhance productivity. Whether you’re tackling data processing, computational tasks, or any other intensive workload, implementing parallel execution techniques can lead to substantial performance gains. Embrace the power of subprocess to unlock the full potential of your Python scripts and make the most of your computing resources.

Drop a query if you have any questions regarding Load Testing or Amazon SageMaker and we will get back to you quickly.

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