Nvidia SMI and Sumo Logic 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.

The Sumo Logic plugin is designed to facilitate the sending of metrics from Telegraf to Sumo Logic’s HTTP Source. By utilizing this plugin, users can analyze their metric data in the Sumo Logic platform, leveraging various output data formats.

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.

Sumo Logic

This plugin facilitates the transmission of metrics to Sumo Logic’s HTTP Source, employing specified data formats for HTTP messages. Telegraf, which must be version 1.16.0 or higher, can send metrics encoded in several formats, including graphite, carbon2, and prometheus. These formats correspond to different content types recognized by Sumo Logic, ensuring that the metrics are correctly interpreted for analysis. Integration with Sumo Logic allows users to leverage a comprehensive analytics platform, enabling rich visualizations and insights from their metric data. The plugin provides configuration options such as setting URLs for the HTTP Metrics Source, choosing the data format, and specifying additional parameters like timeout and request size, which enhance flexibility and control in data monitoring workflows.

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"

Sumo Logic

[[outputs.sumologic]]
  ## Unique URL generated for your HTTP Metrics Source.
  ## This is the address to send metrics to.
  # url = "https://events.sumologic.net/receiver/v1/http/"

  ## Data format to be used for sending metrics.
  ## This will set the "Content-Type" header accordingly.
  ## Currently supported formats:
  ## * graphite - for Content-Type of application/vnd.sumologic.graphite
  ## * carbon2 - for Content-Type of application/vnd.sumologic.carbon2
  ## * prometheus - for Content-Type of application/vnd.sumologic.prometheus
  ##
  ## More information can be found at:
  ## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#content-type-headers-for-metrics
  ##
  ## NOTE:
  ## When unset, telegraf will by default use the influx serializer which is currently unsupported
  ## in HTTP Source.
  data_format = "carbon2"

  ## Timeout used for HTTP request
  # timeout = "5s"

  ## Max HTTP request body size in bytes before compression (if applied).
  ## By default 1MB is recommended.
  ## NOTE:
  ## Bear in mind that in some serializer a metric even though serialized to multiple
  ## lines cannot be split any further so setting this very low might not work
  ## as expected.
  # max_request_body_size = 1000000

  ## Additional, Sumo specific options.
  ## Full list can be found here:
  ## https://help.sumologic.com/03Send-Data/Sources/02Sources-for-Hosted-Collectors/HTTP-Source/Upload-Metrics-to-an-HTTP-Source#supported-http-headers

  ## Desired source name.
  ## Useful if you want to override the source name configured for the source.
  # source_name = ""

  ## Desired host name.
  ## Useful if you want to override the source host configured for the source.
  # source_host = ""

  ## Desired source category.
  ## Useful if you want to override the source category configured for the source.
  # source_category = ""

  ## Comma-separated key=value list of dimensions to apply to every metric.
  ## Custom dimensions will allow you to query your metrics at a more granular level.
  # dimensions = ""
</code></pre>

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.

Sumo Logic

1. Real-Time System Monitoring Dashboard: Utilize the Sumo Logic plugin to continuously feed performance metrics from your servers into a Sumo Logic dashboard. This setup allows tech teams to visualize system health and load in real-time, enabling quicker identification of any performance bottlenecks or system failures through detailed graphs and metrics.

2. Automated Alerting System: Configure the plugin to send metrics that trigger alerts in Sumo Logic for specific thresholds such as CPU usage or memory consumption. By setting up automated alerts, teams can proactively address issues before they escalate into critical failures, significantly improving response times and overall system reliability.

3. Cross-System Metrics Aggregation: Integrate multiple Telegraf instances across different environments (development, testing, production) and funnel all metrics to a central Sumo Logic instance using this plugin. This aggregation enables comprehensive analysis across environments, facilitating better monitoring and informed decision-making across the software development lifecycle.

4. Custom Metrics with Dimensions Tracking: Use the Sumo Logic plugin to send customized metrics that include dimensions identifying various aspects of your infrastructure (e.g., environment, service type). This granular tracking allows for more tailored analytics, enabling your team to dissect performance across different application layers or business functions.

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