Troubleshooting Empty SLURM Labels In ROCm Device Metrics Exporter

by StackCamp Team 67 views

This article addresses an issue encountered while integrating SLURM with the ROCm device metrics exporter. The user, Mubarak, reported that while the metrics exporter runs successfully within an Apptainer container and detects SLURM labels, these labels appear with empty values despite jobs running on the GPUs. This comprehensive guide will explore the problem, analyze the configuration and logs provided, and offer potential solutions to resolve this integration challenge.

Understanding the Problem

Mubarak is using the ROCm device metrics exporter, deployed via Apptainer, to monitor GPU performance within a SLURM-managed cluster. The exporter is configured to collect SLURM job information (job ID, user, partition, etc.) and include it as labels in the exported metrics. However, despite the exporter logging that it has received and updated the SLURM environment variables, the actual metrics exposed show empty values for these SLURM-related labels. This discrepancy hinders the ability to correlate GPU metrics with specific SLURM jobs, making it difficult to analyze performance and resource utilization on a per-job basis.

Analyzing the Configuration and Logs

The provided information includes:

  1. Metrics Output: The output from curl localhost:5000/metrics shows that SLURM-related labels (job_id, job_partition, job_user, cluster_name) are present in the metrics, but their values are empty strings.
  2. Exporter Logs: The logs from within the Apptainer container (tail /run/exporter.log) indicate that the exporter is correctly receiving SLURM environment variables from the prolog script. The logs show messages like received job env map[...] and updated map[...], which confirm that the exporter is aware of the SLURM job context.

This creates a puzzling situation where the exporter seems to be receiving the SLURM information but not properly applying it to the metrics being exposed. To effectively troubleshoot, we need to delve deeper into the configuration, scripts, and potential interactions between SLURM, Apptainer, and the metrics exporter.

Potential Causes and Solutions

Several factors could contribute to this issue. Let's explore the most likely causes and discuss potential solutions.

1. Incorrect Configuration of the Metrics Exporter

It's possible that there's a misconfiguration in the exporter's config.json file that prevents it from correctly mapping the SLURM environment variables to the metric labels. The configuration needs to be carefully reviewed to ensure that the SLURM labels are defined correctly and that the exporter is configured to read the environment variables injected by the SLURM prolog script.

  • Solution: Carefully examine the config.json file, paying close attention to the sections related to labels and SLURM integration. Ensure that the label names match the expected SLURM environment variable names (e.g., SLURM_JOB_ID for job_id). Verify that the configuration specifies the correct mechanism for reading environment variables.
{
  "labels": {
    "job_id": "SLURM_JOB_ID",
    "job_user": "SLURM_JOB_USER",
    "job_partition": "SLURM_JOB_PARTITION",
    "cluster_name": "SLURM_CLUSTER_NAME"
  },
  "slurm_integration": {
    "enabled": true,
    "prolog_path": "/path/to/prolog.sh",
    "epilog_path": "/path/to/epilog.sh"
  }
}

Ensure that slurm_integration is enabled and the paths to the prolog and epilog scripts are correct. This is crucial for the exporter to interact with SLURM's job scheduling system.

2. Issues with the Prolog and Epilog Scripts

The prolog and epilog scripts are responsible for setting up the environment for SLURM jobs and cleaning up afterwards. If these scripts are not correctly configured, they might not be properly injecting the SLURM environment variables into the Apptainer container's environment. This can lead to the exporter receiving incomplete or incorrect information.

  • Solution: Inspect the prolog script to ensure it's exporting the necessary SLURM environment variables. The script should make these variables available to the exporter process running inside the Apptainer container. Similarly, check the epilog script for any potential interference with these variables.

A typical prolog script might look like this:

#!/bin/bash

# Export SLURM environment variables
export SLURM_JOB_ID=$SLURM_JOB_ID
export SLURM_JOB_USER=$SLURM_JOB_USER
export SLURM_JOB_PARTITION=$SLURM_JOB_PARTITION
export SLURM_CLUSTER_NAME=$SLURM_CLUSTER_NAME
export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES

# Start the metrics exporter in the background
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &

And a corresponding epilog script:

#!/bin/bash

# Stop the metrics exporter
apptainer exec instance://metrics-amd /path/to/stop-exporter.sh

These scripts are critical for ensuring the exporter's lifecycle is tied to the SLURM job's execution, and that the necessary environment variables are set.

3. Apptainer Environment Isolation

Apptainer containers provide a level of environment isolation, which might prevent the exporter process inside the container from directly accessing the SLURM environment variables set outside the container. This isolation is a key feature of containerization, but it can also introduce challenges when integrating with system-level services like SLURM.

  • Solution: Ensure that the necessary environment variables are explicitly passed into the Apptainer container when the exporter is launched. This can be achieved using Apptainer's --env or --env-all options, or by bind-mounting a file containing the environment variables into the container.

When starting the exporter within the Apptainer container, ensure you're passing the SLURM environment variables:

apptainer exec --env-all instance://metrics-amd /path/to/exporter

This command passes all environment variables from the host system into the container, which should include the SLURM variables.

4. Timing and Race Conditions

There might be a timing issue where the exporter tries to collect metrics before the SLURM environment variables are fully populated within the container. This can happen if the exporter starts too early in the job lifecycle, before the prolog script has had a chance to set up the environment.

  • Solution: Introduce a delay in the exporter's startup sequence to ensure that the SLURM environment is fully initialized before metrics collection begins. This can be done by adding a sleep command in the prolog script before starting the exporter.

Modify the prolog script to include a delay:

#!/bin/bash

# Export SLURM environment variables
export SLURM_JOB_ID=$SLURM_JOB_ID
export SLURM_JOB_USER=$SLURM_JOB_USER
export SLURM_JOB_PARTITION=$SLURM_JOB_PARTITION
export SLURM_CLUSTER_NAME=$SLURM_CLUSTER_NAME
export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES

# Wait for a short period to ensure SLURM environment is set
sleep 5

# Start the metrics exporter in the background
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &

This delay gives SLURM time to fully propagate the environment variables.

5. Permissions and Access Rights

The exporter process might not have the necessary permissions to access the SLURM environment variables or the files created by the prolog script. This can happen if the exporter is running under a different user account or if the file permissions are not correctly set.

  • Solution: Ensure that the exporter process has the necessary permissions to read the SLURM environment variables and access any files created by the prolog script. This might involve adjusting file permissions or running the exporter under a user account that has the required privileges.

Verify that the user running the exporter has the necessary permissions to access SLURM-related files and directories. This is crucial for seamless integration.

6. Caching or Stale Data

The exporter might be caching the metric labels and not updating them correctly when a new SLURM job starts. This can lead to the exporter displaying stale or incorrect information for the SLURM labels.

  • Solution: Implement a mechanism to refresh the metric labels whenever a new SLURM job starts. This might involve clearing the cache or reloading the configuration file when the prolog script is executed.

Ensure that the exporter is designed to handle job transitions gracefully and refresh its internal state accordingly. This prevents stale data from being reported.

7. Bugs in the Metrics Exporter Code

It's always possible that there's a bug in the metrics exporter code that prevents it from correctly handling SLURM integration. This is less likely, but it's still a possibility that should be considered.

  • Solution: Review the exporter's source code for any potential bugs related to SLURM integration. If a bug is found, submit a patch or contact the exporter's developers for assistance.

If you suspect a bug, providing detailed information and steps to reproduce the issue can significantly expedite the resolution process.

Debugging Steps

To effectively troubleshoot this issue, consider the following debugging steps:

  1. Verbose Logging: Increase the logging verbosity of the metrics exporter to get more detailed information about its internal operations. This can help identify where the SLURM environment variables are being processed and where the issue might be occurring.
  2. Environment Variable Inspection: Within the Apptainer container, use commands like printenv or echo $SLURM_JOB_ID to verify that the SLURM environment variables are actually present and have the correct values. This can help rule out issues with environment variable propagation.
  3. Probing the Exporter's API: Use curl or a similar tool to query the exporter's /metrics endpoint at different points in the job lifecycle (before, during, and after job execution) to see how the SLURM labels are changing over time. This can help identify timing issues or caching problems.
  4. Simplified Test Case: Create a simplified test case that isolates the SLURM integration logic. This can help narrow down the source of the problem and make it easier to reproduce and debug.

Conclusion

Integrating SLURM with the ROCm device metrics exporter offers valuable insights into GPU performance within a cluster environment. By systematically addressing potential causes, such as configuration errors, script issues, environment isolation, timing problems, permissions, caching, and potential code bugs, you can effectively troubleshoot and resolve the issue of empty SLURM labels. The debugging steps outlined above will further aid in pinpointing the exact cause and implementing the appropriate solution.

By following these steps, Mubarak and other users facing similar challenges can achieve seamless SLURM integration, enabling comprehensive monitoring and analysis of GPU resource utilization within their high-performance computing environments.

This article dives into troubleshooting steps for resolving empty SLURM labels when using the ROCm device metrics exporter, specifically within an Apptainer container. We aim to provide a comprehensive guide for users like Mubarak, who are experiencing this issue, to effectively diagnose and fix their SLURM integration challenges.

Why SLURM Integration Matters

SLURM (Simple Linux Utility for Resource Management) is a widely-used open-source job scheduler, particularly in high-performance computing (HPC) environments. Integrating SLURM with a metrics exporter like the ROCm device metrics exporter is crucial for several reasons:

  • Resource Accounting: It allows you to accurately track resource usage (GPU time, memory, etc.) on a per-job basis. This is essential for billing, capacity planning, and identifying resource-intensive applications.
  • Performance Analysis: By correlating job-specific information with GPU metrics, you can identify performance bottlenecks and optimize application performance.
  • Job Monitoring: Integrating SLURM labels enables real-time monitoring of GPU utilization for individual jobs, providing insights into job progress and potential issues.
  • Capacity Management: Understanding how SLURM jobs utilize GPU resources is vital for efficient cluster management and capacity planning.

When SLURM labels are missing or empty, it becomes significantly harder to achieve these benefits, hindering effective resource management and performance analysis. Thus, resolving this issue is paramount for anyone leveraging the ROCm device metrics exporter in a SLURM environment.

Common Causes of Empty SLURM Labels

As highlighted in the initial problem description, the exporter logs indicate that it receives SLURM environment variables. However, these variables do not translate into the metrics output. This discrepancy suggests a problem in how these variables are processed or propagated. Let's explore the most common causes:

1. Configuration File Issues (config.json)

The config.json file is the heart of the metrics exporter's configuration. Incorrect settings here can directly lead to empty SLURM labels. Key configurations to verify include:

  • Label Mapping: Ensure that the labels section correctly maps SLURM environment variables (e.g., SLURM_JOB_ID, SLURM_JOB_USER) to the desired metric labels (job_id, job_user).
  • SLURM Integration Enabled: Confirm that the slurm_integration section is enabled ("enabled": true) and that the paths to the prolog and epilog scripts are accurate.
{
  "labels": {
    "job_id": "SLURM_JOB_ID",
    "job_user": "SLURM_JOB_USER",
    "job_partition": "SLURM_JOB_PARTITION"
  },
  "slurm_integration": {
    "enabled": true,
    "prolog_path": "/path/to/your/prolog.sh",
    "epilog_path": "/path/to/your/epilog.sh"
  }
}
  • Typos and Syntax Errors: Even minor typos or syntax errors in the config.json can prevent the exporter from parsing the configuration correctly. Always validate the JSON syntax. Tools like online JSON validators can be invaluable for this.

2. Prolog and Epilog Script Problems

The prolog and epilog scripts play a vital role in setting the stage for SLURM job execution and cleaning up afterward. They are responsible for making SLURM environment variables available to the metrics exporter.

  • Environment Variable Export: The prolog script must explicitly export the necessary SLURM environment variables. A common mistake is assuming that these variables are automatically available to the exporter.
#!/bin/bash

# Export SLURM environment variables
export SLURM_JOB_ID=$SLURM_JOB_ID
export SLURM_JOB_USER=$SLURM_JOB_USER
export SLURM_JOB_PARTITION=$SLURM_JOB_PARTITION
export SLURM_CLUSTER_NAME=$SLURM_CLUSTER_NAME
export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES

# Start the metrics exporter in the background
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &
  • Correct Script Paths: Ensure that the paths to the prolog and epilog scripts specified in the config.json are correct and accessible to SLURM.
  • Epilog Interference: The epilog script should not inadvertently clear or modify SLURM environment variables before the exporter has a chance to collect them. While less common, this is a potential pitfall.
  • Script Execution Permissions: Verify that the prolog and epilog scripts have execute permissions (chmod +x script.sh). SLURM will not be able to run them if they lack these permissions.

3. Apptainer Containerization and Environment Isolation

Apptainer, like other containerization technologies, provides environment isolation. This isolation can prevent the metrics exporter running inside the container from directly accessing SLURM environment variables set on the host system.

  • Explicit Environment Passing: The most effective solution is to explicitly pass SLURM environment variables into the Apptainer container when launching the exporter. Apptainer's --env or --env-all flags are crucial for this.
apptainer exec --env-all instance://metrics-amd /path/to/exporter

--env-all passes all environment variables, but you can use --env to pass specific variables if desired.

  • Bind Mounting: Alternatively, you could bind-mount a file containing the environment variables into the container. This approach is less common but can be useful in certain situations.

4. Timing and Race Conditions

A subtle but significant issue can arise from timing. If the metrics exporter starts before the SLURM environment is fully populated within the container, it will collect empty labels.

  • Introducing a Delay: The most straightforward fix is to introduce a short delay in the prolog script before launching the exporter. A sleep command can provide enough time for the environment to be set up.
#!/bin/bash

# Export SLURM environment variables (as before)

# Introduce a delay
sleep 5

# Start the metrics exporter
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &

The duration of the delay (sleep 5) might need adjustment based on your system's performance and load.

5. Permissions and Access Rights

The metrics exporter must have the necessary permissions to access SLURM environment variables and any files created by the prolog script. Incorrect permissions can lead to failures in accessing this information.

  • User Context: Ensure that the exporter is running under a user account that has the required privileges. If the exporter is running as a different user than the one executing the SLURM job, it might not have access to the SLURM environment.
  • File Permissions: Check the permissions of the prolog and epilog scripts, as well as any files they create. The exporter user needs read access to these files.

6. Caching and Stale Data

Some metrics exporters might cache labels, which can lead to stale SLURM information being displayed. This is particularly relevant in dynamic environments where jobs start and stop frequently.

  • Refresh Mechanisms: The exporter should have a mechanism to refresh labels when a new SLURM job starts. This might involve clearing a cache, reloading the configuration, or explicitly updating the labels.
  • Exporter Design: The exporter's design should account for job transitions and ensure that it retrieves the latest SLURM context.

Debugging and Troubleshooting Strategies

Effective troubleshooting involves a systematic approach. Here's a recommended strategy:

  1. Increase Logging Verbosity: Most metrics exporters offer options to increase logging verbosity. Enable these options to get detailed information about the exporter's operation, including how it processes SLURM environment variables.

  2. Verify Environment Variables: Inside the Apptainer container, use printenv or echo $SLURM_JOB_ID to confirm that the SLURM environment variables are present and have the correct values. This helps isolate issues with environment propagation.

  3. Probe the /metrics Endpoint: Use curl localhost:5000/metrics (or the appropriate address) at various stages of the job lifecycle (before, during, after) to observe how SLURM labels change over time. This can reveal timing issues or caching problems.

  4. Simplify the Test Case: Create a minimal, reproducible test case that focuses solely on SLURM integration. This simplifies debugging and allows you to isolate the problem more effectively.

    • A simple SLURM job that echoes the environment variables can be invaluable in identifying propagation issues.

  5. Check SLURM Logs: SLURM logs can provide insights into whether the prolog and epilog scripts are executing correctly and if any errors are occurring during job setup or teardown. Always review SLURM logs when troubleshooting SLURM-related issues.

Real-World Examples and Scenarios

To solidify understanding, let's consider some real-world scenarios:

  • Scenario 1: Incorrect Label Mapping

    • The config.json file maps SLURM_JOBID (typo) to job_id. The exporter will not find SLURM_JOBID and the label will be empty.
    • Solution: Correct the mapping to SLURM_JOB_ID.

  • Scenario 2: Missing Environment Export in Prolog

    • The prolog script fails to export SLURM_JOB_USER. The job_user label will be empty.
    • Solution: Add export SLURM_JOB_USER=$SLURM_JOB_USER to the prolog script.

  • Scenario 3: Timing Issue with Exporter Startup

    • The exporter starts before SLURM environment variables are set. Labels are initially empty but might populate later.
    • Solution: Introduce a sleep command in the prolog script before starting the exporter.

Conclusion: Achieving Seamless SLURM Integration

Integrating SLURM with the ROCm device metrics exporter is a powerful way to gain visibility into GPU resource utilization in HPC environments. By carefully addressing potential causes like configuration errors, script problems, environment isolation, timing issues, and permissions, you can overcome the challenge of empty SLURM labels.

The debugging strategies outlined in this guide, combined with a systematic approach, will empower you to pinpoint the root cause and implement the necessary fixes. Achieving seamless SLURM integration unlocks a wealth of information for resource accounting, performance analysis, and job monitoring, ultimately leading to more efficient and effective use of your GPU resources.

This article offers a detailed troubleshooting guide for resolving the issue of empty SLURM labels in the ROCm device metrics exporter. This problem often arises when integrating the exporter with a SLURM job scheduler, particularly within containerized environments like Apptainer. We aim to provide a step-by-step approach to identify and fix the root cause, ensuring accurate monitoring of GPU resources in your HPC cluster.

The Importance of SLURM Label Integration

In High-Performance Computing (HPC) environments, SLURM (Simple Linux Utility for Resource Management) is a prevalent workload manager. Integrating SLURM labels with metrics exporters, such as the ROCm device metrics exporter, is critical for a number of reasons:

  1. Resource Utilization Tracking: SLURM labels provide the context needed to attribute GPU usage to specific jobs, users, and partitions. This is essential for resource accounting, chargeback mechanisms, and capacity planning.
  2. Performance Analysis: Correlating GPU metrics with SLURM job information enables detailed performance analysis. You can identify performance bottlenecks related to specific applications, users, or job configurations.
  3. Job Monitoring and Debugging: Real-time monitoring of GPU utilization with SLURM labels helps track job progress and identify potential issues or performance anomalies. This allows for timely intervention and debugging.
  4. Optimized Resource Allocation: Analyzing historical GPU usage data with SLURM context facilitates informed decisions about resource allocation, scheduling policies, and hardware upgrades.

When SLURM labels are missing or empty, these benefits are significantly diminished. It becomes difficult to understand how GPU resources are being used, making it harder to optimize performance and manage the cluster effectively. Therefore, resolving this issue is of utmost importance for any HPC environment utilizing SLURM and the ROCm device metrics exporter.

Diagnosing the Problem: Why are SLURM Labels Empty?

The core issue is that while the ROCm device metrics exporter appears to detect the presence of SLURM labels, their values are not being populated correctly. This indicates a breakdown in the flow of SLURM job information to the exporter. To pinpoint the root cause, we need to examine several key areas:

1. Configuration Verification (config.json)

The config.json file governs the exporter's behavior, including how it handles SLURM integration. Meticulous examination of this file is essential:

  • Label Definitions: The labels section should map SLURM environment variables to the desired metric labels. Ensure accurate mappings between variables like SLURM_JOB_ID, SLURM_JOB_USER, SLURM_JOB_PARTITION and the corresponding label names (job_id, job_user, job_partition).

    {
  "labels": {
    "job_id": "SLURM_JOB_ID",
    "job_user": "SLURM_JOB_USER",
    "job_partition": "SLURM_JOB_PARTITION",
    "cluster_name": "SLURM_CLUSTER_NAME"
  },
  "slurm_integration": {
    "enabled": true,
    "prolog_path": "/path/to/your/prolog.sh",
    "epilog_path": "/path/to/your/epilog.sh"
  }
}

  • SLURM Integration Section: The slurm_integration section must be enabled ("enabled": true). The prolog_path and epilog_path settings must point to the correct locations of your prolog and epilog scripts.

  • Syntax and Typos: Even minor syntax errors (e.g., missing commas, incorrect quotes) or typos can prevent the exporter from parsing the configuration correctly. Use a JSON validator to ensure the config.json is valid.

2. Prolog and Epilog Script Scrutiny

SLURM prolog and epilog scripts are crucial for preparing the environment before a job starts and cleaning up afterward. Problems in these scripts can directly impact SLURM label propagation:

  • Environment Variable Export: The prolog script must explicitly export the SLURM environment variables that the exporter needs. A common mistake is failing to export these variables, making them unavailable to the exporter process.

    #!/bin/bash

# Export SLURM environment variables
export SLURM_JOB_ID=$SLURM_JOB_ID
export SLURM_JOB_USER=$SLURM_JOB_USER
export SLURM_JOB_PARTITION=$SLURM_JOB_PARTITION
export SLURM_CLUSTER_NAME=$SLURM_CLUSTER_NAME
export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES

# Start the metrics exporter in the background
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &

  • Correct Script Execution: Verify that the prolog and epilog scripts are being executed by SLURM. Check SLURM logs for any errors related to script execution.

  • Epilog Interference: Although less common, ensure that the epilog script does not inadvertently clear or modify SLURM environment variables before the exporter has had a chance to collect them.

  • Execute Permissions: Ensure that both prolog and epilog scripts have execute permissions (chmod +x script.sh).

3. Containerization and Environment Isolation (Apptainer)

Containerization technologies like Apptainer provide environment isolation, which can hinder the propagation of SLURM environment variables to the exporter process running inside the container. This is a frequent cause of empty SLURM labels.

  • Explicit Environment Variable Passing: The most reliable solution is to explicitly pass the SLURM environment variables into the Apptainer container when launching the exporter. Apptainer's --env and --env-all flags are essential for this.

    apptainer exec --env-all instance://metrics-amd /path/to/exporter

    The --env-all flag passes all host environment variables into the container. You can use --env VAR=value to pass specific variables if desired.

  • Bind Mounting (Less Common): Alternatively, you could bind-mount a file containing the environment variables into the container, but this approach is generally less convenient than using --env or --env-all.

4. Timing Considerations and Race Conditions

A subtle but critical issue can arise from timing. If the metrics exporter starts within the container before the SLURM environment has been fully established, it will collect empty labels. This is a type of race condition.

  • Introduce a Delay: The most straightforward way to mitigate this is to introduce a short delay in the prolog script before launching the exporter. A sleep command can provide sufficient time for the SLURM environment to be set up.

    #!/bin/bash

# Export SLURM environment variables (as before)

# Introduce a delay (adjust as needed)
sleep 5

# Start the metrics exporter
apptainer exec instance://metrics-amd /path/to/start-exporter.sh &

    The duration of the delay (e.g., sleep 5 seconds) may need adjustment depending on your system's performance and workload.

5. Permissions and Access Control

The metrics exporter process must have the necessary permissions to access SLURM environment variables and any files or directories created by the prolog script. Incorrect permissions can prevent the exporter from accessing this information.

  • User Context: Ensure that the exporter is running under a user account that has the required privileges to access SLURM information. If the exporter runs as a different user than the one executing the SLURM job, it may lack the necessary permissions.
  • File Permissions: Check the permissions of the prolog and epilog scripts and any files they create. The exporter user needs read and execute access to the scripts and read access to any generated files.

6. Caching and Stale Data Issues

Some metrics exporters might cache labels internally. This can lead to stale SLURM information being displayed if the cache is not refreshed when a new SLURM job starts.

  • Label Refresh Mechanisms: Ideally, the exporter should have a mechanism to refresh labels whenever a new SLURM job starts. This could involve clearing the cache, reloading the configuration, or explicitly updating the labels.
  • Exporter Design: The exporter's design should account for job transitions and ensure that it retrieves the most up-to-date SLURM context.

Step-by-Step Troubleshooting Methodology

To systematically address this issue, follow these troubleshooting steps:

  1. Enable Verbose Logging: Increase the logging verbosity of the metrics exporter. This will provide more detailed information about its internal operations, particularly how it processes SLURM environment variables. Refer to the exporter's documentation for instructions on enabling verbose logging.
  2. Verify Environment Variables Inside the Container: Within the Apptainer container, use commands like printenv or echo $SLURM_JOB_ID to confirm that the SLURM environment variables are indeed present and have the correct values. This will help isolate issues related to environment propagation.
  3. Probe the /metrics Endpoint at Different Stages: Use curl (or a similar tool) to query the exporter's /metrics endpoint at various points in the job lifecycle: before job execution, during job execution, and after job completion. This will help you observe how SLURM labels change over time and identify potential timing issues or caching problems.
  4. Create a Minimal Test Case: Develop a simplified test case that focuses solely on SLURM integration. This makes debugging easier by reducing complexity. A simple SLURM job that just echoes the environment variables can be very helpful.
  5. Inspect SLURM Logs: SLURM logs can provide valuable insights into whether the prolog and epilog scripts are executing correctly and if any errors are occurring during job setup or teardown. Always review SLURM logs when troubleshooting SLURM-related problems.

Illustrative Examples and Scenarios

Let's examine some real-world scenarios to illustrate common causes and solutions:

  • Scenario 1: Typo in config.json Label Mapping

    • The config.json file incorrectly maps SLURM_JOBID (with a typo) to the job_id label. The exporter will not find this variable, and the label will remain empty.
    • Solution: Correct the mapping to SLURM_JOB_ID in the config.json file.

  • Scenario 2: Missing Environment Variable Export in Prolog Script

    • The prolog script forgets to export SLURM_JOB_USER. Consequently, the job_user label in the metrics will be empty.
    • Solution: Add the line export SLURM_JOB_USER=$SLURM_JOB_USER to the prolog script.

  • Scenario 3: Race Condition During Exporter Startup

    • The exporter starts too quickly within the container before the SLURM environment is fully initialized. Labels are initially empty but may eventually populate if the exporter refreshes its data.
    • Solution: Introduce a sleep command (e.g., sleep 5) in the prolog script before launching the exporter to ensure the SLURM environment is ready.

Conclusion: Achieving Robust SLURM Integration

Integrating SLURM with the ROCm device metrics exporter offers a powerful mechanism for monitoring GPU resource utilization in HPC environments. Overcoming the challenge of empty SLURM labels requires a thorough understanding of the potential causes and a systematic troubleshooting approach.

By diligently addressing configuration issues, script problems, environment isolation, timing considerations, permissions, and caching, you can achieve robust SLURM integration. The debugging steps and examples provided in this guide will empower you to pinpoint the root cause and implement the necessary fixes, enabling you to unlock the full potential of your GPU resources in a SLURM-managed cluster.