1. Introduction
  2. Alumet core, plugins, agent
  4. Installation and Configuration
  5. Installing Alumet
  6. Running Alumet
  7. Configuration file
  8. Distributed measurement with the "relay" mode
  10. Tutorials for common use cases
  11. Measuring the energy consumption of an AI model training
  12. Monitoring an entire system
  14. Plugins reference
  15. Measurement Sources
    1. grace-hopper: Grace and Grace Hopper superchips
    2. nvidia-jetson: NVIDIA Jetson edge devices
    3. nvidia-nvml: dedicated NVIDIA GPUs
    4. perf: fine-grained Linux performance counters
    5. procfs: system and per-process measurements
    6. quarch: query a Quarch module
    7. rapl: x86 CPU energy consumption
    8. raw-cgroups: Linux control groups accounting
    9. Integration with HPC platforms
      1. oar: measure OAR jobs
      2. slurm: measure Slurm jobs
      3. kwollect-input: pull measurements from kwollect
    11. k8s: measure Kubernetes pods
  17. Data Transforms
    1. energy-attribution: attribute the energy consumed by the hardware to the software
    2. energy-estimation-tdp: estimate the energy consumption when it cannot be measured
    3. process-to-cgroup-bridge: Turn per-process data into per-cgroup data
  19. Measurement Outputs
    1. CSV files
    2. ElasticSearch / OpenSearch
    3. InfluxDB
    4. Kwollect API
    5. MongoDB
    6. OpenTelemetry
    7. Prometheus
  21. Special plugins
    1. Relay client and server
    2. socket-control
  24. Community
  25. Who is behind ALUMET?
  26. Contributing