Nvidia SMI and Azure Data Explorer 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 Azure Data Explorer plugin allows integration of metrics collection with Azure Data Explorer, enabling users to analyze and query their telemetry data efficiently. With this plugin, users can configure ingestion settings to suit their needs and leverage Azure’s powerful analytical capabilities.

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.

Azure Data Explorer

The Azure Data Explorer plugin allows users to write metrics, logs, and time series data collected from various Telegraf input plugins into Azure Data Explorer, Azure Synapse, and Real-Time Analytics in Fabric. This integration serves as a bridge, allowing applications and services to monitor their performance metrics or logs efficiently. Azure Data Explorer is optimized for analytics over large volumes of diverse data types, making it an excellent choice for real-time analytics and monitoring solutions in cloud environments. The plugin empowers users to configure metrics ingestion based on their requirements, define table schemas dynamically, and set various ingestion methods while retaining flexibility regarding roles and permissions needed for database operations. This supports scalable and secure monitoring setups for modern applications that utilize cloud services.

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"

Azure Data Explorer

[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true

  ##  Ingestion method to use.
  ##  Available options are
  ##    - managed  --  streaming ingestion with fallback to batched ingestion or the "queued" method below
  ##    - queued   --  queue up metrics data and process sequentially
  # ingestion_type = "queued"

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.

Azure Data Explorer

1. Real-Time Monitoring Dashboard: By integrating metrics from various services into Azure Data Explorer using this plugin, organizations can build comprehensive dashboards that reflect real-time performance metrics. This allows teams to respond proactively to performance issues and optimize system health without delay.

2. Centralized Log Management: Utilize Azure Data Explorer to consolidate logs from multiple applications and services. By utilizing the plugin, organizations can streamline their log analysis processes, making it easier to search, filter, and derive insights from historical data accumulated over time.

3. Data-Driven Alerting Systems: Enhance monitoring capabilities by configuring alerts based on metrics sent via this plugin. Organizations can set thresholds and automate incident responses, significantly reducing downtime and improving the reliability of critical operations.

4. Machine Learning Model Training: By leveraging the data sent to Azure Data Explorer, organizations can perform large-scale analytics and prepare the data for feeding into machine learning models. This plugin enables the structuring of data that can subsequently be used for predictive analytics, leading to enhanced decision-making capabilities.

Feedback

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