Deep Learning AMI and AWS Deep Learning Containers are now enabled with support for SOCI snapshotter and index. Seekable OCI (SOCI) is a technology that enables efficient container image management through selective file downloading. It uses a layer-based indexing system to map file locations within container images, allowing containers to start with only the necessary files loaded (lazy loading). This approach reduces network bandwidth usage and improves container startup times, making it particularly valuable for organizations managing large container images in cloud environments.

In this post, we look at how to use SOCI on publicly available Deep Learning AMIs and Containers, when to use the various SOCI modes provided by the tool, and how to quickly and efficiently use this tool in your workloads today.

Background

As organizations deploy artificial intelligence (AI) and machine learning (ML) workloads at scale, container startup time has become a bottleneck in production environments. Whether it’s spinning up training jobs, serving inference endpoints, or scaling GPU clusters automatically, the time spent downloading multi-gigabyte container images directly impacts cost, user experience, and operational efficiency. Traditional container deployment approaches force teams to download entire images before workloads can begin. This process can take multiple minutes to start up images commonly used in production. During development, a few minutes of wait time is barely noticeable. In production, those same minutes add up fast.