Managing Docker Images on Nvidia Jetson Orin Nano Dev Board

Issue Overview

Users of the Nvidia Jetson Orin Nano Dev Board have reported challenges related to managing Docker images created during the build process, particularly when using the command jetson-containers build ollama. The primary symptoms include:

• Multiple Docker Images: Users notice several images being created, including some with the <none> tag, indicating they are not properly associated with a named image.

• Dependency Issues: When attempting to delete certain images, users receive messages indicating that these images are in use. This is particularly evident with images that appear to be remnants of multi-stage builds.

• Storage Concerns: The presence of multiple large images can lead to concerns about storage space on the device, especially since some images are quite large (e.g., 6.68GB and 9.33GB).

• Frequency: This issue seems to occur consistently when users build the ollama container, particularly in setups involving GPU acceleration.

• Impact: The inability to manage Docker images effectively can hinder development workflows, consume unnecessary storage, and complicate debugging processes.

Possible Causes

Several potential causes have been identified for the issues users are experiencing:

• Multi-Stage Docker Builds: The <none> tagged images are likely remnants from a multi-stage Dockerfile used in building ollama, where intermediate stages do not have a final name.

• Image Dependencies: The final image depends on previous layers (such as CUDA), preventing their removal even if they appear unnecessary.

• Docker Layer Management: Docker manages layers by SHA checksums, which can lead to multiple entries for shared layers across different images, complicating the image list without actually consuming more disk space.

• User Misunderstanding: Some users may not fully understand how Docker handles image layers and dependencies, leading to confusion about what can be safely deleted.

Troubleshooting Steps, Solutions & Fixes

To address the issues related to Docker image management on the Nvidia Jetson Orin Nano Dev Board, follow these troubleshooting steps and solutions:

1. Identify Existing Images:

   • Use the command:

     docker images
     

   • This will list all existing images along with their tags and sizes.

2. Understanding Image Dependencies:

   • Recognize that some images cannot be deleted if they are in use as dependencies for other images. To check dependencies, you can inspect an image:

     docker inspect <image_id>
     

3. Removing Unused Images:

   • To remove dangling images (those with <none> tags), use:

     docker rmi $(docker images -f "dangling=true" -q)
     

4. Untagging Images:

   • If you wish to keep certain layers but not have them listed as separate images, you can untag them using:

     docker rmi <image_id>:<tag>
     

5. Cleaning Up Unused Resources:

   • Regularly clean up unused containers and networks with:

     docker system prune
     

6. Best Practices for Future Builds:

   • Consider using named tags for your builds to avoid confusion with <none> tags.
   • Regularly monitor your Docker environment for unused or redundant images.

7. Documentation and Updates:

   • Check for updates or documentation related to the jetson-containers and ollama projects for any changes in best practices or known issues.

8. Further Investigation:

   • If issues persist after following these steps, consider reaching out to community forums or support channels specific to Nvidia Jetson for additional insights or updates regarding Docker image management.

By following these steps, users should be able to manage their Docker images more effectively on the Nvidia Jetson Orin Nano Dev Board while minimizing confusion and maximizing available storage space.