Nvidia SMI and Mimir 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 sends Telegraf metrics directly to Grafana’s Mimir database using HTTP, providing scalable and efficient long-term storage and analysis for Prometheus-compatible metrics.

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.

Mimir

Grafana Mimir supports the Prometheus Remote Write protocol, enabling Telegraf collected metrics to be efficiently ingested into Mimir clusters for large-scale, long-term storage. This integration leverages Prometheus’s well-established standards, allowing users to combine Telegraf’s extensive data collection capabilities with Mimir’s advanced features, such as query federation, multi-tenancy, high availability, and cost-efficient storage. Grafana Mimir’s architecture is optimized for handling high volumes of metric data and delivering fast query responses, making it ideal for complex monitoring environments and distributed systems.

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"

Mimir

[[outputs.http]]
  url = "http://data-load-balancer-backend-1:9009/api/v1/push"
  data_format = "prometheusremotewrite"
  username = "*****"
  password = "******"
  [outputs.http.headers]
     Content-Type = "application/x-protobuf"
     Content-Encoding = "snappy"
     X-Scope-OrgID = "****"

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.

Mimir

1. Enterprise-Scale Kubernetes Monitoring: Integrate Telegraf with Grafana Mimir to stream metrics from Kubernetes clusters at enterprise scale. This enables comprehensive visibility, improved resource allocation, and proactive troubleshooting across hundreds of clusters, leveraging Mimir’s horizontal scalability and high availability.

2. Multi-tenant SaaS Application Observability: Use this plugin to centralize metrics from diverse SaaS tenants into Grafana Mimir, enabling tenant isolation and accurate billing based on resource usage. This approach provides reliable observability, efficient cost management, and secure multi-tenancy support.

3. Global Edge Network Performance Tracking: Stream latency and availability metrics from globally distributed edge servers into Grafana Mimir. Organizations can quickly identify performance degradation or outages, leveraging Mimir’s fast querying capabilities to ensure optimal service reliability and user experience.

4. Real-Time Analytics for High-Volume Microservices: Implement Telegraf metrics collection in high-volume microservices architectures, feeding data into Grafana Mimir for real-time analytics and anomaly detection. Mimir’s powerful querying enables teams to detect anomalies and quickly respond, maintaining high service availability and performance.

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