Theory
- Deploying Monitoring Services
- Prometheus is an open-source tool to collect and store time-series data on its CPU, memory, and GPU utilization.
- Grafana is utilized to showcase data from Prometheus via dashboards.
- NVIDIA DCGM: Specifically designed for checking the performance of the GPU, its memory, temperature, and design load.
- Facilitates proactive management through real-time alerts.
- Aids in enhancing resource distribution and lowering operational expenses.
- Provides insights for scaling and capacity planning.
- Setting Up Jupyter Environments
- Jupyter environments offer interactive computing for data activities like exploration, model building, and analysis in notebooks supporting Python, R, and Julia.
- Active Coding: Can run cell-wise for step-by-step run-through.
- GPU assistance: Taps CUDA and cuDNN to speed up data processing jobs.
- Extensions let you add plugins for version control (Git), visualization (Plotly), and resource monitor (nbresuse).
- Multi-user Cluster Environments Using JupyterHub.
- Simplifies GPU resource management for deep learning tasks.
- Adding More Services
- Logging Services:
- ELK Stack provides centralized logging, easy-to-use search, and visualization.
- Fluentd is used to collect logs from different sources.
- Alerting Systems:
- PagerDuty helps in responding to incidents.
- CI/CD Pipelines:
- Jenkins is used for testing, deploying, and monitoring.
- GitLab CI/CD has built-in deployment with version control.
- Service Mesh:
- Istio manages secure microservices traffic and observability.
- Makes security, performance, and observability better for systems.
- Reduces manual overhead for deployment and monitoring.
- Key Indicators That Metrics are Useful
- Usage of CPU and GPU: Computes effectiveness of resource usage.
- Monitoring Memory and Storage Usage: Aids in bottleneck prevention.
- Latency and Throughput: Analyzes how effectively service requests are handled.
- Detection of failures or congestion in service delivery.