Logs Collection

This guide provides instructions for IT administrators on collecting NVIDIA Run:ai logs for support, including prerequisites, CLI commands, and log file retrieval. It also covers enabling verbose logging for Prometheus and the NVIDIA Run:ai Scheduler.

Collect Logs to Send to Support

To collect NVIDIA Run:ai logs, follow these steps:

Prerequisites

• Ensure that you have administrator-level access to the Kubernetes cluster where NVIDIA Run:ai is installed.
• The NVIDIA Run:ai CLI must be installed.

Step-by-step Instructions

1. Open a terminal on any machine where the NVIDIA Run:ai CLI is installed and configured with access to the Kubernetes cluster. The user running this command typically requires system administrator permissions, including access to pods and namespaces.
2. Collect the Logs. Execute the following command to collect the logs. See the CLI commands reference for more details:

   runai diagnostics collect-logs

This command gathers diagnostic logs from your Kubernetes cluster to facilitate troubleshooting or support requests with NVIDIA Run:ai Support. The following optional flags are available:

Flag	Description
--output-dir <path>	Directory where the log archive is saved. Defaults to the directory from which the command is run.
--namespaces <list>	Comma-separated list of namespaces to collect logs from. Defaults to runai, runai-reservation, runai-backend, training-operator, knative, gpu-operator, nim-operator.
--no-previous	Excludes previous pod logs from the collection.

1. After the command completes, the CLI displays the path of the generated compressed log file. Send this file to NVIDIA Run:ai Support for troubleshooting.

Note

The command collects diagnostic logs from Kubernetes namespaces in a NVIDIA Run:ai installation. By default, logs are collected from the following namespaces: runai, runai-reservation, runai-backend, training-operator, knative, gpu-operator, and nim-operator. Use the --namespaces flag to target specific namespaces.

Logs Verbosity

Increase log verbosity to capture more detailed information, providing deeper insights into system behavior and make it easier to identify and resolve issues.

Prerequisites

Before you begin, ensure you have the following:

• Access to the Kubernetes cluster where NVIDIA Run:ai is installed

  • Including necessary permissions to view and modify configurations.
• kubectl installed and configured:

  • The Kubernetes command-line tool, kubectl, must be installed and configured to interact with the cluster.
  • Sufficient privileges to edit configurations and view logs.
• Monitoring Disk Space

  • When enabling verbose logging, ensure adequate disk space to handle the increased log output, especially when enabling debug or high verbosity levels.

Adding Verbosity

Adding verbosity to Prometheus

To increase the logging verbosity for Prometheus, follow these steps:

1. Edit the RunaiConfig to adjust Prometheus log levels. Copy the following command to your terminal:

   kubectl edit runaiconfig runai -n runai

2. In the configuration file that opens, add or modify the following section to set the log level to debug:

   spec:
     prometheus:
       spec:
         logLevel: debug

3. Save the changes. To view the Prometheus logs with the new verbosity level, run:

   kubectl logs -n runai prometheus-runai-0 

   This command streams the last 100 lines of logs from Prometheus, providing detailed information useful for debu

Adding verbosity to the Scheduler

To enable extended logging for the NVIDIA Run:ai scheduler:

1. Edit the RunaiConfig to adjust scheduler verbosity:

   kubectl edit runaiconfig runai -n runai

2. Add or modify the following section under the scheduler settings:

   runai-scheduler:
     args:
       verbosity: 6

   This increases the verbosity level of the scheduler logs to provide more detailed output.

Warning: Enabling verbose logging can significantly increase disk space usage. Monitor your storage capacity and adjust the verbosity level as necessary.