P2 Alert: Queued Jobs On Linux.aws.h100 - Investigation Needed

by SLV Team 63 views
P2 Alert: Queued Jobs on Linux.aws.h100 - Investigation Needed

Hey guys! We've got a P2 alert situation on our hands concerning the linux.aws.h100 jobs in our PyTorch infrastructure. It seems like things are backing up, and we need to dive in to figure out what's causing the queueing. Let's break down the alert details and formulate a plan of attack to get things running smoothly again.

Understanding the Alert

First off, let's dissect this alert. The core issue is that jobs for linux.aws.h100 instances are sitting in a queue for an extended period. Specifically, the alert highlights these key metrics:

  • Max queue time: A whopping 157 minutes! That's a long time for jobs to be waiting.
  • Max queue size: We've got a queue of 6 runners. This indicates a significant backlog.

These metrics alone are enough to raise a red flag. Our alerting system is designed to catch these scenarios, ensuring we don't have jobs stuck in limbo for too long. The alert description further clarifies that this alert triggers when regular runner types experience prolonged queueing or when a large number of them are queuing simultaneously.

The reason provided by the alert gives us the specific context: [runner=linux.aws.h100] max_queue_size=6, max_queue_time_mins=157. This confirms that the issue is isolated to the linux.aws.h100 runners and that both the queue size and queue time have exceeded our defined thresholds.

To get a visual representation of the situation, the alert conveniently provides a link to our metrics dashboard: http://hud.pytorch.org/metrics. This is the first place we should head to get a bird's-eye view of the queueing patterns and identify any potential bottlenecks.

Initial Investigation Steps

Okay, so we know jobs are queued, and it's impacting linux.aws.h100 instances. What do we do next? Here’s a breakdown of the initial steps we should take:

  1. Visit the Metrics Dashboard: The provided link (http://hud.pytorch.org/metrics) is our first port of call. We need to visualize the queueing metrics over time. Look for any spikes or unusual patterns that might correlate with the increased queue time and size. Are there any specific timeframes where the queueing is particularly severe? Are other runners also experiencing issues, or is it isolated to linux.aws.h100?

  2. Identify Queueing Runners: The alert message mentions a queue size of 6 runners. We need to pinpoint which specific runners are experiencing the queueing. This will help us narrow down the scope of the problem. Are these runners consistently overloaded, or is this a recent phenomenon?

  3. Check Recent Changes: Has there been any recent infrastructure changes, code deployments, or configuration updates that might be contributing to the queueing? Sometimes, seemingly small changes can have unforeseen consequences. Coordinate with other teams to see if they've made any modifications that could be relevant.

  4. Examine Job Logs: Once we've identified the queueing runners, we need to delve into the job logs for those runners. Are there any error messages, timeouts, or other anomalies that might indicate why jobs are getting stuck? Look for patterns in the logs that might provide clues about the root cause.

  5. Review Resource Utilization: Is there a resource bottleneck affecting the linux.aws.h100 instances? Check CPU usage, memory consumption, disk I/O, and network traffic. Are these resources being maxed out, leading to the queueing? We should also check if there are any resource limits in place that might be artificially restricting the number of concurrent jobs.

  6. Consult the Runbook: The alert includes a link to a runbook (https://hud.pytorch.org/metrics). This runbook should contain documented procedures and troubleshooting steps for common queueing issues. It's a valuable resource that can guide us through the investigation process. It's unusual for the Runbook link to point to the same metrics dashboard, so we should double-check if there's a dedicated runbook for queueing issues specifically.

Potential Causes and Solutions

While we haven't dived deep into the investigation yet, it's helpful to brainstorm potential causes and solutions. This helps us frame our investigation and prioritize our efforts. Here are a few common reasons for job queueing:

  • Resource Exhaustion: As mentioned earlier, if the linux.aws.h100 instances are running out of CPU, memory, or other resources, jobs will inevitably queue up. Solutions might involve scaling up the instances, optimizing resource usage, or distributing the workload across more instances.

  • Code Issues: Bugs in the code being executed by the jobs can lead to long execution times or infinite loops, effectively clogging up the queue. Identifying and fixing these code issues is crucial. This might involve debugging, code profiling, or rolling back recent deployments.

  • External Dependencies: Jobs might be waiting for external services or dependencies that are slow or unavailable. This could be databases, APIs, or other network resources. We need to investigate these dependencies and ensure they are healthy and responsive.

  • Concurrency Limits: There might be limits in place on the number of concurrent jobs that can run on the linux.aws.h100 instances. These limits might be intentional (to prevent resource exhaustion) or unintentional (misconfigured settings). Reviewing and adjusting these limits might be necessary.

  • Scheduler Issues: Problems with the job scheduler itself can cause queueing. This is less common but should be considered. We might need to examine the scheduler logs and configuration to identify any issues.

  • Increased Workload: Sometimes, queueing is simply a result of a sudden increase in the workload. If this is the case, we need to assess our capacity and potentially scale up our infrastructure to handle the increased demand.

Diving Deeper: A Step-by-Step Approach

Let's create a more detailed plan for our investigation:

  1. Metrics Analysis (First 30 minutes):

    • Go to the metrics dashboard: (http://hud.pytorch.org/metrics).
    • Focus on the queue time and queue size metrics for linux.aws.h100 runners.
    • Identify the exact timeframe when the queueing started and peaked.
    • Look for any correlated events or metrics that might provide clues. For example, is there a sudden increase in job submissions? A spike in resource utilization?
    • Check if other runner types are also experiencing queueing. This will help us determine if the issue is isolated to linux.aws.h100 or more widespread.

  2. Runner Identification and Log Review (Next 60 minutes):

    • Identify the specific runners that are queueing. (This information might be available in the metrics dashboard or in internal logs).
    • Access the logs for these runners.
    • Filter the logs for the timeframe identified in Step 1.
    • Look for error messages, warnings, timeouts, or any other anomalies.
    • Pay close attention to the types of jobs that are queueing. Are they all the same type of job? Are there any patterns?

  3. Resource Utilization Analysis (Next 60 minutes):

    • Monitor the resource utilization (CPU, memory, disk I/O, network) of the linux.aws.h100 instances.
    • Use monitoring tools to track resource usage over time.
    • Identify any resource bottlenecks or spikes in utilization.
    • Check if there are any resource limits in place that might be contributing to the queueing.

  4. Code and Configuration Review (Ongoing):

    • If the logs point to specific code issues, investigate the relevant code modules.
    • Check for recent code changes or deployments that might be the cause.
    • Review the configuration of the job scheduler and the linux.aws.h100 instances.
    • Look for any misconfigurations or suboptimal settings.

  5. Collaboration and Communication (Ongoing):

    • Keep the team informed of your progress and findings.
    • Collaborate with other teams (e.g., infrastructure, networking) if necessary.
    • Document your investigation steps and findings.

Taking Action: Remediation and Prevention

Once we've identified the root cause of the queueing, we need to take action to remediate the issue and prevent it from happening again. Here are some potential actions:

  • Scaling Resources: If resource exhaustion is the culprit, we might need to scale up the linux.aws.h100 instances or add more instances to the pool.

  • Optimizing Code: If code issues are causing the queueing, we need to fix the bugs and optimize the code for performance.

  • Tuning Configuration: We might need to adjust the configuration of the job scheduler, the linux.aws.h100 instances, or other related systems to improve performance and prevent queueing.

  • Addressing Dependencies: If external dependencies are the issue, we need to ensure they are healthy and responsive. This might involve optimizing database queries, improving network connectivity, or working with the teams responsible for those dependencies.

  • Improving Monitoring and Alerting: We should review our monitoring and alerting setup to ensure we can detect queueing issues early and prevent them from escalating.

  • Capacity Planning: We need to regularly assess our capacity and plan for future growth. This will help us avoid resource bottlenecks and ensure we can handle increasing workloads.

Let's Get to Work!

Okay, team, that's the game plan! We've got a P2 alert, and we need to address it promptly. Let's start by diving into the metrics dashboard and then systematically work through the investigation steps. Remember to communicate your findings and collaborate effectively. We'll get this sorted out!

Let's keep each other updated on our progress, and don't hesitate to ask for help if you get stuck. Good luck, everyone!