regression

SGLang serving configuration comparisons are automated by running a regression script that tests a base configuration against a variant, evaluating accuracy, performance, and decode health. This streamlines validating changes like LoRA toggles or backend swaps.

To profile SGLang model performance, you can execute an automated testing suite that generates detailed profiling data for both CPU and GPU usage. This requires specific Python libraries and Docker to capture resource utilization metrics.

Yes, Docker is required to run SGLang regression testing. The automated profiling and comparison workflows depend on Docker alongside specific Python libraries like torch and torch-profiler to execute the test suites and generate resource metrics.

Yes, you can compare a new LoRA configuration against an SGLang baseline. The regression process executes accuracy, performance, prompt-check, and profiling tests to validate the variant serving configuration against the control.

Automated profiling for SGLang serving configurations works by executing a suite of tests that generates detailed CPU and GPU usage data. This process applies to model evaluation and serving configuration optimization workflows to validate changes.

If your SGLang serving configuration regression test fails, you should review the generated accuracy, performance, and profiling metrics. Comparing these detailed CPU and GPU outputs against your baseline configuration helps identify the specific degradation.