I am currently facing a technical challenge in creating a Docker image on ARM architecture (an NVIDIA Jetson AGX Xavier Developer Kit device) using the ultralytics/ultralytics:latest-jetson-jetpack5 image from Ultralytics. This base image currently weighs 13 GB, with an 11 GB base. Our goal is to create a complete Docker image of approximately 22 GB, which exceeds the available capacities of an NVIDIA card. We are seeking specific recommendations, best practices, or any relevant documentation to optimize the use of the ultralytics/ultralytics:latest-jetson-jetpack5 image on ARM architecture while reducing the size of the final image. Any suggestions, advice, or documentation you could provide would be greatly appreciated to help us resolve this issue.
Hi - This is the forum for AI Workbench. If you have something relevant to Jetson, you should post there.
I will go ahead and move this over to the correct Jetson forum.
Latest Autonomous Machines/Jetson & Embedded Systems topics - NVIDIA Developer Forums
Hi,
Xavier has 28GB storage so 22G should work?
If you need more storage, have you tried to use an external SSD?
Thanks.
This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.