Procedure

  1. Setting Up Monitoring Services
    • Set up and configure Prometheus to use metrics from services using exporters (Node Exporter for system metrics, DCGM for GPU metrics).
    • Make Grafana displays to show CPU, GPU, memory use, and services response times.
    • Make notifications in Prometheus Alertmanager for extreme limits like very high memory use or GPU heat.
  2. Deploying Jupyter Environments
    • Install JupyterLab or JupyterHub for multi-user access.
    • Configure GPU support using CUDA and cuDNN.
    • Either Docker or Kubernetes can be used for deployment.
    • Get and enable Jupyter extensions for checking usage (nbresuse) and Git.
  3. Integrating Additional Services
    • Logging:
      • Set up the ELK stack for log management.
      • Set up Fluentd to send logs from Jupyter and monitoring services to Elasticsearch.
    • Alerting:
      • Establish Prometheus Alertmanager for issuing alerts through email or Slack according to set conditions.
      • Integrate PagerDuty for incident management and escalation.
    • CI/CD Pipelines:
      • Set up Jenkins or GitLab CI/CD to automatically deploy and test updates to your service.
    • Service Mesh:
      • Use Istio to control, secure, and observe the whole network.
  4. Monitoring and Optimization
    • Use Prometheus and Grafana to monitor CPU, GPU, memory utilization, and request latency metrics.
    • Identify bottlenecks using logs and dashboards (e.g., high CPU causing slow responses).
    • Improve performance by adjusting Kubernetes resource requests and limits or scaling services based on observed metrics.
    • Modify load balancing and scheduling policies for better efficiency if needed.
  5. Reporting and Analysis
    • Export Grafana dashboards and metric logs to analyze performance trends.
    • Use gathered metrics to conduct performance reviews and recommend optimizations.
    • Create a summary report on the performance of deployed services and areas for improvement.