GenAI Studio
  • Edge AI SDK/GenAI Studio
  • Getting Started
    • About GenAI Studio
    • Quickstart
      • Prerequisite
      • Installation
      • Utilities
    • Feature Overview
      • Inference Chat
      • Fine-tuning
      • Model Management
      • Application
    • Version History
      • Version 1.1.0 (Coming Soon)
      • Version 1.0.0
  • Inference
    • Chat Inference
    • AI Agents
  • Finetune
    • Text-to-Text
      • Overview
      • Full Parameter
      • LoRA
    • Text-to-Image (Coming Soon)
    • Dataset Management
    • Schedule
  • Model
    • Model Management
  • Validation
  • Convert
  • Administration
    • Resource Monitoring
  • System Configuration
    • AI Providers
      • LLM Setup
      • Embedder Setup
      • Vector DB
      • Transcription Setup
    • System Administration
      • Users
      • Workspace Chats
      • Invites
      • GPU Resource
      • Register an App
    • Appearance Customization
    • Tools
      • Embedded Chat Widgets
      • Event Logs
      • Security & Access
  • Application
    • Text to Image
    • Background Removal
    • OCR
  • FAQ
    • General
    • Technical
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  • Real-Time System Monitoring
  • Integrated Grafana and Prometheus for Real-Time System Performance Tracking, with a Focus on GPU Metrics
  • Benefits
  1. Administration

Resource Monitoring

PreviousConvertNextSystem Configuration

Last updated 3 days ago

Real-Time System Monitoring

Integrated Grafana and Prometheus for Real-Time System Performance Tracking, with a Focus on GPU Metrics

GenAI Studio integrates Grafana and Prometheus to provide comprehensive, real-time, and historical monitoring of system performance, with a strong emphasis on GPU-specific metrics. This integration allows for:

  • Real-Time Monitoring: The system continuously collects and tracks key performance indicators (KPIs) across various system components. This includes traditional metrics like CPU usage, memory utilization, disk I/O, and network activity.

  • Historical Data Analysis: Prometheus stores time-series data, enabling in-depth analysis of past performance trends, identification of bottlenecks, and capacity planning.

  • GPU-Focused Metrics: In addition to standard system metrics, the solution gathers and visualizes critical GPU metrics. These metrics may include:

    • GPU utilization (%)

    • GPU memory usage (total, used, and free)

    • GPU temperature

    • GPU power consumption

    • GPU clock speeds (core and memory)

    • GPU compute unit/core utilization

    • Specific metrics related to GPU workloads (e.g., frame rates in graphics applications, tensor core usage in machine learning).

Benefits

Proactive Issue Detection: By monitoring system and GPU metrics in real-time, potential problems can be identified and addressed before they lead to performance degradation or system failures.

Performance Optimization: Historical data analysis helps identify performance bottlenecks and areas for optimization, leading to more efficient resource utilization.

Resource Management: The system provides insights into resource usage patterns, enabling better capacity planning and allocation of resources.

Improved Reliability: Early detection of issues and proactive intervention contribute to increased system reliability and uptime.

Enhanced Visibility: Customizable dashboards provide a clear and comprehensive view of system and GPU performance, facilitating better understanding and decision-making.