Your Browser is Out of Date

Nytro.ai uses technology that works best in other browsers.
For a full experience use one of the browsers below
Design Guide—Generative AI in the Enterprise - Inferencing
Introduction AI Model Inferencing Architecture Concepts and Components Solution Architecture Validation Results Performance Characterization and Sizing Inferencing Operations
Model deployment
Life cycle
Backup and restore
Secure model serving
Model monitoring
Dell Professional Services for Generative AI Conclusion References

Model monitoring

Model monitoring

  • Triton Inference Server provides model metrics that offer valuable insights into the performance and behavior of the deployed models during inference. These metrics help monitor and optimize the inference process in production environments. Some of the key model metrics provided by Triton Inference Server include:

    • Latency—Triton reports the average time it takes to process a single inference request (in milliseconds).
    • Throughput—Triton measures the number of inference requests processed per second.
    • GPU use—For models running on GPUs, Triton provides metrics on GPU use, indicating how much of the GPU's processing power is being used.
    • Memory use—Triton reports the GPU memory used by each model instance, ensuring that the GPU memory is efficiently used and avoiding memory-related issues.
    • Inference count—Triton monitors the number of inferences made by each model instance.

    These metrics can be accessed and monitored through Triton's integrated monitoring endpoints, Prometheus, or other monitoring and visualization tools like Grafana. By analyzing these model metrics, developers and system administrators can gain a comprehensive understanding of the model's performance, resource use, and overall health, enabling them to optimize the deployment and ensure the model operates efficiently in production.