triton-inference-server/model_analyzer
Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.
What it solves
It helps users find the most efficient configuration for AI models running on a Triton Inference Server. Instead of manually guessing settings, it automates the process of finding the right balance between performance (latency) and resource usage (compute and memory) on specific hardware.
How it works
Model Analyzer uses several search modes to explore different configuration parameters—such as maximum batch size, dynamic batching, and instance groups—to see how they affect performance. It offers four search strategies:
- Optuna Search: Uses a hyperparameter optimization framework for a comprehensive search.
- Quick Search: Uses a heuristic hill-climbing algorithm to find optimal settings quickly.
- Automatic Brute Search: Exhaustively tests all combinations of specified parameters.
- Manual Brute Search: Allows users to define their own specific sweeps of parameters.
Who it’s for
ML engineers and DevOps professionals who deploy models using Triton Inference Server and need to optimize their hardware utilization and meet specific Quality of Service (QoS) requirements.
Highlights
- Versatile Model Support: Works with single models, multiple concurrent models, ensemble models, BLS models, and Large Language Models (LLMs).
- QoS Constraints: Allows users to filter results based on specific requirements, such as a maximum latency budget.
- Detailed Reporting: Generates summary and detailed reports to visualize the trade-offs between different configurations.
- Hardware-Specific Optimization: Tailors configurations to the specific piece of hardware being used.