Troubleshooting GPU Pod RunContainerError On Jetson Orin Nano With KubeEdge

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This article addresses a common issue encountered when deploying GPU-enabled pods on a Jetson Orin Nano edge node within a KubeEdge environment. The error, RunContainerError, arises when the pod fails to start due to an inability to inject CDI (Container Device Interface) devices. Specifically, the error message indicates an "unresolvable CDI devices nvidia.com/gpu=tegra: unknown." This article provides a comprehensive guide to diagnosing and resolving this issue, ensuring successful GPU utilization in your KubeEdge deployments.

Understanding the Problem

When working with GPUs in a containerized environment like Kubernetes, the nvidia-device-plugin plays a crucial role. This plugin allows Kubernetes to discover and manage GPU resources, making them available to containers. In the context of KubeEdge, which extends Kubernetes to edge computing, this plugin is essential for leveraging the GPU capabilities of edge devices like the Jetson Orin Nano. The RunContainerError you are encountering suggests that the nvidia-device-plugin is not correctly configured or is unable to communicate the GPU device information to the container runtime. This leads to the container failing to start because it cannot access the requested GPU resources. The core issue revolves around the Container Device Interface (CDI), a standard that allows container runtimes to discover and utilize hardware devices. When the CDI configuration is incorrect or incomplete, the runtime cannot map the requested nvidia.com/gpu resource to the actual GPU device on the Jetson Orin Nano, resulting in the ā€œunresolvable CDI devicesā€ error.

Analyzing the Error

The error message "failed to create task for container: failed to create shim task: OCI runtime create failed: could not apply required modification to OCI specification: error modifying OCI spec: failed to inject CDI devices: unresolvable CDI devices nvidia.com/gpu=tegra: unknown" is the key to understanding the problem. Let's break it down:

  • failed to create task for container: This indicates that the container runtime (in this case, likely Docker with cri-docker) was unable to start the container.
  • failed to create shim task: The "shim" is a component that sits between the container runtime and the actual container process. Its failure suggests a low-level issue in container creation.
  • OCI runtime create failed: This points to a problem during the Open Container Initiative (OCI) runtime's attempt to create the container.
  • could not apply required modification to OCI specification: The OCI specification defines how containers should be created. This message means that the runtime could not apply necessary modifications, specifically related to device injection.
  • error modifying OCI spec: failed to inject CDI devices: This narrows down the issue to the injection of Container Device Interface (CDI) devices.
  • unresolvable CDI devices nvidia.com/gpu=tegra: unknown: This is the most specific part of the error. It indicates that the runtime could not resolve the CDI device nvidia.com/gpu=tegra. This likely means that the CDI configuration for the NVIDIA GPU on the Jetson Orin Nano is either missing or incorrect. The =tegra part further suggests a potential issue with identifying the specific GPU architecture (Tegra) of the Jetson Orin Nano.

The kubectl describe pod output provides additional context. The State: Waiting and Reason: CrashLoopBackOff indicate that the pod is repeatedly failing to start. The Last State: Terminated section shows Reason: ContainerCannotRun and the same error message, confirming the root cause. The Limits and Requests sections show that the pod is requesting one nvidia.com/gpu, which is the resource that the runtime is failing to provide.

Diagnostic Steps

To effectively troubleshoot this issue, follow these steps:

1. Verify NVIDIA Driver and CUDA Installation

Ensure that the correct NVIDIA drivers and CUDA Toolkit are installed on the Jetson Orin Nano. Mismatched or incomplete installations are a frequent cause of GPU-related issues. Check the NVIDIA documentation for the recommended driver and CUDA versions for your specific Jetson Orin Nano model and the KubeEdge version you are using. Use the nvidia-smi command on the edge node to check the driver version and the status of the GPU. If nvidia-smi is not found or reports errors, it indicates a problem with the driver installation.

2. Check NVIDIA Device Plugin DaemonSet

Review the logs of the nvidia-device-plugin-daemonset in the kube-system namespace. The logs you provided show that the plugin is running and has successfully registered with the Kubelet. However, pay close attention to any warnings or error messages during the plugin's initialization. Look for issues related to device discovery, CDI configuration, or communication with the NVIDIA drivers. The plugin's configuration, as shown in the logs, specifies how it discovers and advertises GPU resources. Ensure that the migStrategy (Multi-Instance GPU) and other settings are appropriate for your setup. If you are not using MIG, the migStrategy should be set to none. Verify that the deviceDiscoveryStrategy is set to auto or a specific strategy that matches your needs. Check the resources section to confirm that the nvidia.com/gpu resource is defined with the correct pattern (* in this case, which matches all GPUs).

3. Inspect Kubelet Configuration

The Kubelet, the agent that runs on each node and manages containers, needs to be configured to recognize and utilize the nvidia-device-plugin. Check the Kubelet configuration file (typically located at /var/lib/kubelet/config.yaml or /etc/kubernetes/kubelet.conf) for the following:

  • featureGates: Ensure that DevicePlugins=true is enabled in the featureGates section. This enables the device plugin feature in the Kubelet.
  • devicePlugins: Verify that the Kubelet is configured to discover and register device plugins. This is usually handled automatically, but it's worth checking.
  • containerRuntime and containerRuntimeEndpoint: Confirm that the Kubelet is using the correct container runtime (e.g., Docker) and that the endpoint is correctly configured. If you are using a different container runtime, ensure that it is compatible with the nvidia-device-plugin.

4. Examine CDI Configuration

The core of the issue lies in the CDI configuration. CDI allows container runtimes to discover and utilize hardware devices. The error message "unresolvable CDI devices nvidia.com/gpu=tegra: unknown" indicates that the runtime cannot find the necessary CDI configuration for the NVIDIA GPU on the Jetson Orin Nano. The nvidia-device-plugin is responsible for generating CDI specifications. These specifications define how to inject GPU devices into containers. Check the following:

  • CDI Specification Files: The nvidia-device-plugin typically generates CDI specification files in the /var/run/cdi directory on the node. Verify that these files exist and that they contain the correct information for your GPU devices. The filenames usually follow a pattern like nvidia.com_gpu.yaml. Inspect the contents of these files to ensure that they define the nvidia.com/gpu resource and any required device mappings.
  • CDI Annotation: The nvidia-device-plugin often uses a CDI annotation prefix (cdi.k8s.io/) to indicate that a pod requires CDI devices. Check the pod's annotations to see if this prefix is being used and if the annotation values match the CDI specifications. The plugin's configuration includes a cdiAnnotationPrefix setting, which should be consistent across your environment.
  • Container Runtime Support: Ensure that your container runtime (Docker with cri-docker in this case) supports CDI. Recent versions of Docker and containerd have built-in CDI support. If you are using an older version, you may need to install a separate CDI runtime.

5. Review Pod Definition

Check the pod definition for any misconfigurations. Ensure that the pod is requesting the nvidia.com/gpu resource in both the limits and requests sections. The pod definition should also include any necessary environment variables or volume mounts required by the NVIDIA drivers or the container image. The provided pod description shows that the pod is requesting nvidia.com/gpu: 1, which is correct. However, double-check that the container image (mnist:2.0) is designed to utilize GPUs and that it includes the necessary libraries and drivers. If the image is not GPU-aware, it will not be able to use the injected devices, even if the CDI configuration is correct.

6. Check for Conflicts and Resource Contention

In some cases, other processes or containers might be interfering with the nvidia-device-plugin or the GPU devices. Check for any resource contention issues, such as other applications consuming all the GPU memory. You can use tools like nvidia-smi to monitor GPU usage. Also, ensure that there are no conflicting device plugins or configurations that might be interfering with the nvidia-device-plugin.

Troubleshooting Steps and Solutions

Based on the diagnostic steps, here are potential solutions to the RunContainerError:

1. Correct NVIDIA Driver and CUDA Installation

  • If nvidia-smi reports errors or the driver version is incorrect, reinstall the NVIDIA drivers and CUDA Toolkit. Follow the official NVIDIA documentation for the Jetson Orin Nano. Use the appropriate installation method (e.g., using the JetPack SDK or manual installation). Ensure that the installed driver version is compatible with the CUDA version and the nvidia-device-plugin.

2. Restart Kubelet and Docker

  • After making any changes to the Kubelet configuration or the CDI setup, restart the Kubelet and Docker services to apply the changes. This ensures that the Kubelet re-registers the nvidia-device-plugin and that the container runtime picks up the new CDI configuration.

    sudo systemctl restart kubelet
sudo systemctl restart docker

3. Verify CDI Specification Files

  • If the CDI specification files are missing or contain incorrect information, try restarting the nvidia-device-plugin. This might trigger the plugin to regenerate the CDI specifications. If the files are still incorrect, you might need to manually create or modify them. Refer to the CDI specification documentation for the correct format and content. The CDI specification should define the device nodes, vendor, and other attributes required for the container runtime to access the GPU.

4. Update Container Runtime Configuration

  • If you are using an older version of Docker or containerd, upgrade to a newer version that has built-in CDI support. If necessary, configure the container runtime to use a separate CDI runtime, such as containerd-cdi. Consult the documentation for your container runtime and the CDI runtime for configuration instructions.

5. Check Device Permissions

  • Ensure that the container runtime has the necessary permissions to access the GPU devices. This might involve adding the container runtime user to the video group or adjusting the device permissions in the CDI specification. The CDI specification allows you to define device ownership and permissions, ensuring that the container process can access the GPU devices.

6. Review NVIDIA Container Toolkit Configuration

  • The NVIDIA Container Toolkit is a set of libraries and tools that allow containers to access NVIDIA GPUs. Ensure that the toolkit is correctly installed and configured on the edge node. Check the /etc/nvidia-container-runtime/config.toml file for the runtime's configuration. The toolkit configuration should specify the paths to the NVIDIA drivers and libraries, as well as any runtime options. Incorrect toolkit configuration can prevent containers from accessing the GPUs.

7. Adjust Resource Limits

  • If you suspect resource contention, try adjusting the resource limits for your GPU pods. Ensure that the pods are not requesting more GPU resources than are available on the node. You can use the kubectl edit command to modify the pod's resource limits. It's also important to monitor GPU usage to identify any resource bottlenecks.

8. Consider Device Discovery Strategy

  • The nvidia-device-plugin offers different device discovery strategies. If the auto strategy is not working correctly, try using a specific strategy, such as uuid or pciBusID. The uuid strategy uses the GPU's UUID for device identification, while the pciBusID strategy uses the PCI bus ID. Choosing the right strategy can help the plugin accurately discover and register the GPUs.

Specific Solution for Tegra GPUs

Given the error message "unresolvable CDI devices nvidia.com/gpu=tegra: unknown," the issue is likely related to the specific identification of Tegra GPUs. Tegra is the GPU architecture used in the Jetson Orin Nano. The nvidia-device-plugin might not be correctly identifying the GPU as a Tegra device. To address this, try the following:

1. Verify Tegra-Specific Configuration

  • Check if there are any specific configuration options for Tegra GPUs in the nvidia-device-plugin. You might need to set an environment variable or a configuration flag to explicitly enable Tegra support. Consult the nvidia-device-plugin documentation for any Tegra-specific settings.

2. Update NVIDIA Container Toolkit for Tegra

  • Ensure that you are using a version of the NVIDIA Container Toolkit that supports Tegra GPUs. Some older versions might not have full support for the Tegra architecture. Update the toolkit to the latest version to ensure compatibility.

3. Check Device Tree Configuration

  • On Jetson devices, the device tree describes the hardware configuration. Verify that the device tree is correctly configured for the GPU. Incorrect device tree configuration can prevent the nvidia-device-plugin from discovering the GPU properly. Consult the Jetson documentation for information on device tree configuration.

4. Create Custom CDI Specification (if necessary)

  • If the nvidia-device-plugin is not generating the correct CDI specification for the Tegra GPU, you might need to create a custom CDI specification file. This involves manually defining the device nodes, vendor, and other attributes required for the container runtime to access the GPU. Refer to the CDI specification documentation and the nvidia-device-plugin examples for guidance.

Conclusion

The RunContainerError with the message "unresolvable CDI devices nvidia.com/gpu=tegra: unknown" on a Jetson Orin Nano within a KubeEdge environment indicates a problem with the CDI configuration for the NVIDIA GPU. By following the diagnostic and troubleshooting steps outlined in this article, you should be able to identify and resolve the issue. Key areas to focus on include verifying the NVIDIA driver and CUDA installation, checking the nvidia-device-plugin configuration and logs, inspecting the Kubelet configuration, examining CDI specification files, reviewing pod definitions, and addressing any potential resource contention. For Tegra GPUs specifically, ensure that the NVIDIA Container Toolkit is up-to-date, and the device tree is correctly configured. By systematically addressing these areas, you can ensure that your GPU pods run successfully on your Jetson Orin Nano edge nodes within your KubeEdge deployment, unlocking the full potential of GPU acceleration at the edge.

This comprehensive guide provides a structured approach to troubleshooting GPU-related issues in KubeEdge environments, empowering you to optimize your edge computing workloads and maximize the performance of your GPU-accelerated applications.