How to Integrate Docker into CI/CD for Automated Inference Benchmarking
How to Integrate Docker into CI/CD for Automated Inference Benchmarking
In the previous posts, we discussed how Docker ensures a consistent and reliable testing environment. But in the real world - we don’t want to run benchmarks manually every time a model or engine changes. This is where the next step comes in: full automation using CI/CD.
What Does This Mean?
CI/CD (Continuous Integration / Continuous Deployment) is a process where every code change (e.g., an updated model or a new inference version) triggers an automated testing pipeline - including benchmarking.
The goal: to know immediately if a change improved or hurt performance.
How Docker Fits Into This Process
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Docker Image as a Fixed Work Unit Every benchmark run is done from the exact same Image - with the same libraries, CUDA versions, etc. This ensures that the measurement compares apples to apples.
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Pipeline Running the Container For every commit or model change, the CI (e.g., Jenkins, GitHub Actions, or GitLab CI) pulls the Docker Image and runs the tests inside it.
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Performance Measurement and Result Collection The scripts inside the container measure response times, memory usage, and throughput. The results are automatically sent to a log or a dedicated dashboard.
A Simple Example of a Typical Workflow
- A new model change is pushed to the main branch.
- The CI triggers a container with the testing environment.
- An inference benchmark is performed on a fixed dataset.
- The results are compared to the previous version.
- If there’s a performance drop - an automatic alert is sent.
Why is This Important?
- Full Consistency - Every run is identical to the previous environment.
- Quick Feedback - Every change is automatically tested.
- Historical Tracking - It’s easy to see how each change affected performance over time.
Conclusion
Integrating Docker into CI/CD is the way to make benchmarking something that happens automatically, accurately, consistently, and quickly. Instead of “running when there’s time,” it becomes part of the DNA of the development process - ensuring that the model is always truly improving, not just changing.