voyager-benchlisted

# Benchmark Model Performance Measure and analyze model performance on Axelera AI hardware ## Use This Skill When / Not When - Use when: the user wants to quantify a working pipeline (FPS, latency, throughput, model-variant comparisons). - Not when: performance is a suspected fault or regression -- route to voyager-debug. - Not when: the pipeline still needs to be built -- route to voyager-launch. ## Instructions Benchmark the specified model/pipeline: **$ARGUMENTS** {{INCLUDE common/voyager-sdk-setup.md}} {{INCLUDE common/voyager-task-integration.md}} ### Step 1: Environment Setup ```bash # Environment activation is handled by Step 0/Step 3 of the setup # include (venv/ or axelera-env/); verify it is active python -c "import axelera" 2>/dev/null || echo "SDK env not active" # Verify hardware only when .voyager-runtime.json reports execute_on_device axdevice ``` ### Step 2: Basic Benchmarking Run benchmark on deployed model using inference with performance flags: ```bash # Benchmark with video file (run N frames, no display, show stats) ./inference.py <model> media/traffic1_1080p.mp4 --no-display --frames 500 --show-stats # Benchmark with SDK fake video source (synthetic frames, low I/O overhead) ./inference.py <model> fakevideo:640x480@30 --no-display --frames 1000 --show-stats # Benchmark and save tracer data to CSV ./inference.py <model> <source> --no-display --frames 1000 --show-stats --save-tracers perf.csv ``` ### Step 3: Performance Metrics Key met