AI Benchmarking & Testing

Role in the Project

Validates the accuracy, latency, and throughput of AI models before deployment.

Strengths & Weaknesses

Strengths:

• Ensures reliable performance across different environments.
• Allows early detection of biases or performance bottlenecks.

Weaknesses:

• Requires extensive test datasets.
• Performance varies across different hardware configurations.

Available Technologies & Comparison

• TorchBench (Chosen for PyTorch benchmarking) vs. MLPerf (Industry standard, but complex setup).
• Locust (Chosen for API load testing) vs. JMeter (Heavier setup for AI inference testing).

Chosen Approach

• TorchBench for AI model performance evaluation.
• Locust for stress testing AI inference APIs.

Example of Locust AI API test:

from locust import HttpUser, task
class AIUser(HttpUser):
    @task
    def detect_objects(self):
        self.client.post("/api/detect", json={"image_id": "12345"})

This structured AI pipeline ensures that EYNTRY remains accurate, scalable, and efficient across cloud and edge environments.