Troubleshooting Low M2-15 Runner Count In PyTorch Infra

by Alex Johnson 56 views

An alert regarding a low number of m2-15 runners within the PyTorch development infrastructure requires immediate investigation. This document outlines the alert details, potential causes, and troubleshooting steps to resolve the issue efficiently. Understanding the root cause and implementing corrective actions is crucial for maintaining the smooth operation of the PyTorch ecosystem. Let's dive in and get this sorted out!

Understanding the Alert: Too Few m2-15 Runners

Alert Details

  • Occurred At: December 5, 10:51 am PST
  • State: FIRING - Indicating an active problem.
  • Team: pytorch-dev-infra - Specifies the team responsible for addressing the alert.
  • Priority: P1 - Signifies a high-priority issue requiring urgent attention.
  • Description: "The number of m2-15 runners is unusually low. Is there a queue? Does the number of runners on GH match EC2? Try running cattle spa."
  • Reason: "Threshold Crossed: no data points were received for 4 periods and 4 missing data points were treated as [Breaching]."
  • View Alert: A direct link to the alert in AWS CloudWatch for detailed information.
  • Source: cloudwatch - Identifies the monitoring service that triggered the alert.
  • Fingerprint: A unique identifier for the specific alert instance.

Initial Assessment

The alert description immediately points to a potential shortage of available runners. The key questions to address are:

  1. Is there a queue? This implies that jobs are waiting to be executed but cannot be assigned to available runners.
  2. Do the number of runners on GH match EC2? This suggests a possible discrepancy between the runners configured in GitHub Actions and the actual EC2 instances that should be providing the compute resources.
  3. Try running cattle spa: This is a specific instruction to execute a diagnostic script or tool named "cattle spa", which should provide further insights into the issue.

Investigating Potential Causes for Low Runner Count

Several factors could contribute to a low number of m2-15 runners. Here's a breakdown of the most likely culprits:

1. GitHub Actions Runner Issues

  • Runner Unavailability: The runners themselves might be in a non-operational state. They could be offline, experiencing errors, or stuck in a loop. Verifying their status within the GitHub Actions interface is essential.
  • Runner Configuration Problems: Incorrect configurations can prevent runners from picking up jobs. Double-check the labels, permissions, and other settings associated with the m2-15 runners.
  • GitHub Actions Platform Issues: While less frequent, there might be underlying problems within the GitHub Actions platform itself, such as service outages or rate limits. Checking the GitHub status page is a good practice to rule this out.

2. EC2 Instance Problems

  • EC2 Instance Failures: The EC2 instances hosting the runners could be failing due to hardware issues, software crashes, or network connectivity problems. Reviewing EC2 instance status checks and system logs is critical.
  • Auto-Scaling Issues: If the runners are managed by an auto-scaling group, there might be problems with the scaling policies. The group might not be scaling up to the required number of instances, or instances might be terminating unexpectedly.
  • Resource Constraints: The EC2 instances might be running out of resources, such as CPU, memory, or disk space. Monitoring resource utilization metrics is important to identify bottlenecks.

3. Queueing and Job Management

  • Job Overload: A sudden surge in job submissions could overwhelm the available runners, creating a backlog. Analyzing job submission patterns and queue lengths can help determine if this is the case.
  • Job Configuration Issues: Some jobs might be misconfigured, preventing them from being assigned to the m2-15 runners. Reviewing job configurations and labels can identify potential conflicts.
  • Concurrency Limits: There might be concurrency limits imposed on the runners or the jobs, restricting the number of jobs that can be executed simultaneously.

4. Network Connectivity

  • Network Outages: Problems on the network may prevent runners to communicate with the main server.

Troubleshooting Steps: A Practical Approach

Follow these steps to diagnose and resolve the "too few m2-15 runners" alert. Be systematic and document your findings.

Step 1: Verify Runner Status in GitHub Actions

  1. Access GitHub Actions: Navigate to the GitHub Actions interface for the relevant repository or organization.
  2. Check Runner Status: Examine the status of the m2-15 runners. Look for any runners that are offline, idle, or experiencing errors.
  3. Investigate Offline Runners: If runners are offline, try to determine the cause. Are they intentionally shut down? Did they crash? Check the runner logs for clues.
  4. Restart Runners: If appropriate, attempt to restart the offline runners. This can often resolve temporary issues.

Step 2: Examine EC2 Instance Health

  1. Access the AWS Console: Log in to the AWS Management Console.
  2. Navigate to EC2: Go to the EC2 service.
  3. Locate m2-15 Instances: Identify the EC2 instances associated with the m2-15 runners. You might need to consult your infrastructure documentation or configuration to determine this.
  4. Check Instance Status: Review the instance status checks. Ensure that the instances are running and healthy.
  5. Examine System Logs: Analyze the system logs for any errors or warnings that might indicate problems with the instances.
  6. Reboot Unhealthy Instances: If instances are unhealthy, try rebooting them. This can sometimes resolve underlying issues.

Step 3: Run the "cattle spa" Tool

  1. Locate the Tool: Find the "cattle spa" tool or script. The alert description links to a Google document that might provide more information.
  2. Execute the Tool: Run the tool according to its instructions. Make sure you have the necessary permissions and credentials.
  3. Analyze the Output: Carefully examine the output of the tool. It should provide insights into the state of the runners, the queue, and the EC2 instances.

Step 4: Investigate Queue Length and Job Configuration

  1. Monitor Queue Length: Observe the length of the job queue. Is it consistently growing? This could indicate a job overload.
  2. Review Job Configurations: Examine the configurations of the jobs that are waiting in the queue. Are they correctly labeled? Are they targeting the m2-15 runners?
  3. Adjust Concurrency Limits: If necessary, adjust the concurrency limits on the runners or the jobs to optimize resource utilization.

Step 5: Check Network Connectivity

  1. Verify Network Settings: Ensure the m2-15 runners can properly communicate with the main servers.
  2. Troubleshoot Network Issues: If the m2-15 runners cannot properly communicate with the main servers, troubleshoot any issues on the network.

Remediation and Prevention

Once you've identified the root cause of the low runner count, take appropriate action to resolve the issue. This might involve:

  • Restarting Runners or EC2 Instances: As mentioned earlier, restarting components can often resolve temporary problems.
  • Adjusting Auto-Scaling Policies: If the runners are managed by an auto-scaling group, review and adjust the scaling policies to ensure that the group scales up to the required number of instances.
  • Optimizing Job Configurations: Fine-tune job configurations to ensure that they are efficiently utilizing the available runners.
  • Increasing Resources: If resource constraints are the issue, consider increasing the CPU, memory, or disk space allocated to the EC2 instances.
  • Improving Monitoring: Enhance monitoring to provide better visibility into the health and performance of the runners and the EC2 instances.

Preventative Measures

To prevent future occurrences of this alert, consider implementing the following measures:

  • Proactive Monitoring: Implement comprehensive monitoring of runner health, EC2 instance status, and queue lengths. Set up alerts to trigger when key metrics deviate from expected values.
  • Regular Maintenance: Schedule regular maintenance tasks to ensure that the runners and EC2 instances are running smoothly. This might involve updating software, patching vulnerabilities, and performing routine checks.
  • Capacity Planning: Regularly review capacity planning to ensure that you have sufficient resources to handle the expected workload. Adjust the auto-scaling policies accordingly.
  • Automation: Automate as much of the runner management process as possible. This can reduce the risk of human error and improve efficiency.

Conclusion

The "too few m2-15 runners" alert is a critical issue that requires prompt attention. By following the troubleshooting steps outlined in this document, you can effectively diagnose the root cause and implement corrective actions. Remember to document your findings and track your progress. Proactive monitoring and preventative measures are essential for maintaining a healthy and efficient PyTorch development infrastructure. For more information on cloud monitoring, see this article about AWS CloudWatch. Understanding the underlying issues and applying systematic solutions will help ensure the continuous and reliable operation of your PyTorch development environment.