Nvidia SMI and M3DB Integration

Input and output integration overview

The Nvidia SMI Plugin enables the retrieval of detailed statistics about NVIDIA GPUs attached to the host system, providing essential insights for performance monitoring.

This plugin allows Telegraf to stream metrics to M3DB using the Prometheus Remote Write protocol, enabling scalable ingestion through the M3 Coordinator.

Integration details

Nvidia SMI

The Nvidia SMI Plugin is designed to gather metrics regarding the performance and status of NVIDIA GPUs on the host machine. By leveraging the capabilities of the nvidia-smi command-line tool, this plugin pulls crucial information such as GPU memory utilization, temperature, fan speed, and various performance metrics. This data is essential for monitoring GPU health and performance in real-time, particularly in environments where GPU performance directly impacts computing tasks, such as machine learning, 3D rendering, and high-performance computing. The plugin provides flexibility by allowing users to specify the path to the nvidia-smi binary and configure polling timeouts, accommodating both Linux and Windows systems where the nvidia-smi tool is commonly located. With its ability to collect detailed statistics on each GPU, this plugin becomes a vital resource for any infrastructure relying on NVIDIA hardware, facilitating proactive management and performance tuning.

M3DB

This configuration uses Telegraf’s HTTP output plugin with prometheusremotewrite format to send metrics directly to M3DB through the M3 Coordinator. M3DB is a distributed time series database designed for scalable, high-throughput metric storage. It supports ingestion of Prometheus remote write data via its Coordinator component, which manages translation and routing into the M3DB cluster. This approach enables organizations to collect metrics from systems that aren’t natively instrumented for Prometheus (e.g., Windows, SNMP, legacy systems) and ingest them efficiently into M3’s long-term, high-performance storage engine. The setup is ideal for high-scale observability stacks with Prometheus compatibility requirements.

Configuration

Nvidia SMI

[[inputs.nvidia_smi]]
  ## Optional: path to nvidia-smi binary, defaults "/usr/bin/nvidia-smi"
  ## We will first try to locate the nvidia-smi binary with the explicitly specified value (or default value),
  ## if it is not found, we will try to locate it on PATH(exec.LookPath), if it is still not found, an error will be returned
  # bin_path = "/usr/bin/nvidia-smi"

  ## Optional: timeout for GPU polling
  # timeout = "5s"

M3DB

# Configuration for sending metrics to M3
[outputs.http]
  ## URL is the address to send metrics to
  url = "https://M3_HOST:M3_PORT/api/v1/prom/remote/write"

  ## HTTP Basic Auth credentials
  username = "admin"
  password = "password"

  ## Data format to output.
  data_format = "prometheusremotewrite"

  ## Outgoing HTTP headers
  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"
    X-Prometheus-Remote-Write-Version = "0.1.0"

Input and output integration examples

Nvidia SMI

1. Real-Time GPU Monitoring for ML Training: Continuously monitor the GPU utilization and memory usage during machine learning model training. This enables data scientists to ensure that their GPUs are not being overutilized or underutilized, optimizing resource allocation and reviewing performance bottlenecks in real-time.

2. Automated Alerts for Overheating GPUs: Implement a system using the Nvidia SMI plugin to track GPU temperatures and set alerts for instances where temperatures exceed safe thresholds. This proactive monitoring can prevent hardware damage and improve system reliability by alerting administrators to potential cooling issues before they result in failure.

3. Performance Baselines for GPU Resources: Establish baseline performance metrics for your GPU resources. By regularly collecting data and analyzing trends in GPU usage, organizations can identify anomalies and optimize their workloads accordingly, leading to enhanced operational efficiency.

4. Dockerized GPU Usage Insights: In a containerized environment, use the plugin to monitor GPU performance from within a Docker container. This allows developers to track GPU performance of their applications in production, facilitating troubleshooting and performance optimization within isolated environments.

M3DB

1. Large-Scale Cloud Infrastructure Monitoring: Deploy Telegraf agents across thousands of virtual machines and containers to collect metrics and stream them into M3DB through the M3 Coordinator. This provides reliable, long-term visibility with minimal storage overhead and high availability.

2. Legacy System Metrics Ingestion: Use Telegraf to gather metrics from older systems that lack native Prometheus exporters (e.g., Windows servers, SNMP devices) and forward them to M3DB via remote write. This bridges modern observability workflows with legacy infrastructure.

3. Centralized App Telemetry Aggregation: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., exec, http, jolokia) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.

4. Hybrid Cloud Observability: Install Telegraf agents on-prem and in the cloud to collect and remote-write metrics into a centralized M3DB cluster. This ensures consistent visibility across environments while avoiding the complexity of running Prometheus federation layers.

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