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SOCI Index for AWS Deep Learning Containers

Table of Contents

Background on SOCI Index

The Software Component Index (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 significantly reduces network bandwidth usage and improves container startup times, making it particularly valuable for organizations managing large container images in cloud environments.

Why SOCI Matters: With vs Without Comparison

Without SOCI (Traditional Approach): - Must download the ENTIRE container image before starting (e.g., 17.6GB image takes ~4m26s) - Container can only start after all layers are downloaded - Total time to run: ~4m26s + startup overhead

With SOCI (Lazy Loading): - Only downloads the SOCI index and metadata (~37.9KB takes ~8.9s) - Container starts immediately; additional data fetched on-demand as needed - Total time to run: ~8.9s + startup overhead

Key Benefits: - ~97% faster initial pull time (4m26s → 8.9s) - ~99.8% less data downloaded upfront (17.6GB → ~37.9KB) - Immediate container startup - Ideal for large images, rapid scaling, and bandwidth-constrained environments

See the Performance Comparison section for detailed benchmarks.

SOCI Index Components

A SOCI index, which enables lazy loading of container images, consists of two key components:

  1. SOCI Index Manifest: Lists zTOCs and references the target image
  2. zTOCs (Zone Table of Contents): Comprising:
  3. TOC: A table of contents containing file metadata and corresponding offset information in the decompressed TAR archive
  4. zInfo: A collection of checkpoints representing compression engine states at various points in the layer

SOCI Index Manifest Version

DLC uses SOCI Index Manifest v2. SOCI Index Manifest v1 and SOCI Index Manifest v2 both allow lazily loading container images. SOCI Index Manifest v1 is a standalone artifact that can be discovered using the OCI Referrers API. SOCI Index Manifest v2 uses SOCI-enabled images where the SOCI index is bundled with the original image into a single, multi-architecture image. The SOCI snapshotter v0.10.0 will no longer consume SOCI Index Manifest v1 by default. For detailed differences between v1 and v2, please refer to SOCI Index Manifest v2 Documentation.

DLC SOCI Supported Frameworks

Deep Learning Containers (DLC) provides SOCI index for the following images both in private registry and public gallery: PyTorch Training (x86, arm64), Tensorflow Training, vLLM (x86, arm64), and SGLang.

Sample URIs

  • ECR Private Registry (requires AWS account to login): 

    763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.9-cpu-py312-ubuntu22.04-sagemaker-v1-soci

  • ECR Public Gallery (no login required): 

    public.ecr.aws/deep-learning-containers/pytorch-training:2.9-cpu-py312-ubuntu22.04-sagemaker-v1-soci

DLC SOCI Tag Introduction

For different tracking purposes, DLC images have several tags for one single image. Taking PyTorch training image as an example, it has six tags. Tags without version or with only major version will be overridden by each patch release and always point to the latest release image, while tags with minor version and timestamp will only attach to one specific image.

Tags attached to specific image: - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2-v1.2-2025-12-15-22-03-41 - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2-v1.2

Tags always attached to latest image: - 2.9-gpu-py312-ec2 - 2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1 - 2.9.0-gpu-py312-ec2 - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2

Usage Guidelines: - To use a specific image, use tags with timestamp and minor version - To always get the latest image with CVE patches, use tags without timestamp

SOCI-Enabled Tags:

For SOCI index enabled images, DLC adds -soci as a tag suffix. For the above image examples, the SOCI tags are:

Tags attached to specific image: - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2-v1.2-2025-12-15-22-03-41-soci - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2-v1.2-soci

Tags always attached to latest image: - 2.9-gpu-py312-ec2-soci - 2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci - 2.9.0-gpu-py312-ec2-soci - 2.9.0-gpu-py312-cu130-ubuntu22.04-ec2-soci

Supported Versions

For the latest supported version releases, please refer to DLC public gallery to check the tags.

Getting Started with SOCI Index

Environment

  • Instance Type: c5.24xlarge
  • AMI: Deep Learning Base OSS Nvidia Driver GPU AMI (Amazon Linux 2023)
  • At the time of writing, we are using AMI date 20250701

Common Setup: Install and Configure SOCI Snapshotter

Both Finch and nerdctl use the same SOCI snapshotter. Follow these steps to set it up:

Prerequisites

1. containerd (>= 1.4)

Required to run the SOCI snapshotter. To verify your containerd version:

containerd --version
# Expected output:
# containerd github.com/containerd/containerd/v2 2.0.5 fb4c30d4ede3531652d86197bf3fc9515e5276d9

2. fuse

Used for mounting without root access.

sudo yum install fuse

Install the SOCI Snapshotter

The SOCI project produces 2 binaries: - soci: The CLI tool used to build/manage SOCI indices - soci-snapshotter-grpc: The daemon (a containerd snapshotter plugin) used for lazy loading

Note: Each soci-snapshotter binary is compiled with a specific glibc version. Check your system glibc version for compatibility.

To check glibc version:

ldd --version
# Expected output:
# ldd (GNU libc) 2.34
# Copyright (C) 2021 Free Software Foundation, Inc.
# This is free software; see the source for copying conditions. There is NO
# warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# Written by Roland McGrath and Ulrich Drepper.

To install soci-snapshotter:

version="0.12.1"
wget https://github.com/awslabs/soci-snapshotter/releases/download/v${version}/soci-snapshotter-${version}-linux-amd64.tar.gz
sudo tar -C /usr/local/bin -xvf soci-snapshotter-${version}-linux-amd64.tar.gz soci soci-snapshotter-grpc

# Verify installation
soci --version
# Expected output:
# soci version v0.12.1 c140af2f22fffade79af74ddcc1c29388d763051

Configure SOCI Snapshotter

Step 1: Start Snapshotter in Background

sudo mkdir -p /run/soci-snapshotter-grpc

# Start the SOCI snapshotter in background
sudo /usr/local/bin/soci-snapshotter-grpc &

Expected output:

{"level":"info","msg":"starting soci-snapshotter-grpc","revision":"c140af2f22fffade79af74ddcc1c29388d763051","time":"2025-12-16T23:54:48.995428161Z","version":"v0.12.1"}
{"emitMetricPeriod":10000000000,"fetchPeriod":500000000,"level":"info","maxQueueSize":100,"msg":"constructing background fetcher","silencePeriod":30000000000,"time":"2025-12-16T23:54:49.007876736Z"}
{"address":"/run/soci-snapshotter-grpc/soci-snapshotter-grpc.sock","level":"info","msg":"soci-snapshotter-grpc successfully started","time":"2025-12-16T23:54:49.011089880Z"}

Verify the snapshotter is running:

ps aux | grep soci-snapshotter

Expected output:

root       12809  0.0  0.0 234584  8084 pts/1    S    23:54   0:00 sudo /usr/local/bin/soci-snapshotter-grpc
root       12812  0.0  0.0 234584  2516 pts/2    Ss+  23:54   0:00 sudo /usr/local/bin/soci-snapshotter-grpc
root       12813  0.0  0.0 2317432 42584 pts/2   Sl   23:54   0:00 /usr/local/bin/soci-snapshotter-grpc
ec2-user   12875  0.0  0.0 222324  2124 pts/1    S+   23:55   0:00 grep --color=auto soci-snapshotter

Step 2: Configure containerd to Use SOCI Plugin

Modify /etc/containerd/config.toml:

sudo vim /etc/containerd/config.toml

Ensure the config includes the following section:

[proxy_plugins]
  [proxy_plugins.soci]
    type = "snapshot"
    address = "/run/soci-snapshotter-grpc/soci-snapshotter-grpc.sock"

Choose Your Container Client

After setting up the SOCI snapshotter, choose one of the following container clients:

Finch is an open source client for container development that provides a simpler, streamlined experience. Finch uses the same SOCI snapshotter configured above.

Advantages of Finch: - Simpler command-line interface - Single package installation via dnf - CNI plugins pre-configured - Unlike nerdctl, Finch includes built-in network configuration, so you don't need to use --network host or manually configure CNI plugins - Ideal for users who want a streamlined experience

Install Finch

Finch is packaged in the standard Amazon Linux repositories, making installation straightforward:

sudo dnf install runfinch-finch

Verify Finch is installed correctly:

sudo finch run public.ecr.aws/finch/hello-finch:latest

Expected output:

.....                                      

                            @@@@@@@@@@@@@@@@@@@                                 
                        @@@@@@@@@@@@    @@@@@@@@@@@                             
                      @@@@@@@                  @@@@@@@                          
                    @@@@@@                        @@@@@@                        
                  @@@@@@                            @@@@@                       
                 @@@@@                      @@@#     @@@@@@@@@                  
                @@@@@                     @@   @@@       @@@@@@@@@@             
                @@@@%                     @     @@            @@@@@@@@@@@       
                @@@@                                               @@@@@@@@     
                @@@@                                         @@@@@@@@@@@&       
                @@@@@                                  &@@@@@@@@@@@             
                 @@@@@                               @@@@@@@@                   
                  @@@@@                            @@@@@(                       
                   @@@@@@                        @@@@@@                         
                     @@@@@@@                  @@@@@@@                           
                        @@@@@@@@@@@@@@@@@@@@@@@@@@                              
                            @@@@@@@@@@@@@@@@@@


Hello from Finch!

Visit us @ github.com/runfinch

Pull SOCI Images with Finch

Pull a SOCI-enabled DLC image using Finch:

sudo finch pull --snapshotter soci public.ecr.aws/deep-learning-containers/pytorch-training:2.8-gpu-py312-cu129-ubuntu22.04-ec2-v1-soci

Expected output:

config-sha256:a4f4e7578325b0651818c5605b87f9eef2ba5a54dcfa896dabe448a6c4c468be:    exists         |++++++++++++++++++++++++++++++++++++++| 
elapsed: 12.3s

Run Containers with Finch

Run an interactive container with SOCI lazy loading:

sudo finch run --snapshotter soci -it --rm public.ecr.aws/deep-learning-containers/pytorch-training:2.8-gpu-py312-cu129-ubuntu22.04-ec2-v1-soci /bin/bash

Note: You must use the --snapshotter soci flag with Finch to enable SOCI lazy loading. Without this flag, Finch will download the entire image.

For more information about Finch, visit the Finch documentation.

Option 2: Using nerdctl (Advanced Use Cases)

Provides more control and flexibility for advanced use cases.

Important: nerdctl requires CNI (Container Network Interface) plugin configuration for container networking. Without CNI configuration, you must use the --network host flag when running containers. All examples in this guide use --network host for simplicity.

Install nerdctl

nerdctl (>= v1.6.0) is required to interact with containerd/registry. Note: SOCI will not work with rootless nerdctl.

To install nerdctl:

wget https://github.com/containerd/nerdctl/releases/download/v2.1.5/nerdctl-2.1.5-linux-amd64.tar.gz
sudo tar -C /usr/local/bin -xzf nerdctl-2.1.5-linux-amd64.tar.gz
sudo chmod +x /usr/local/bin/nerdctl

# Verify installation
nerdctl --version
# Expected output:
# nerdctl version 2.1.5

Verify nerdctl is Using SOCI

sudo nerdctl system info

Look for soci in the Storage plugins:

Client:
 Namespace:     default
 Debug Mode:    false

Server:
 Server Version: 2.0.5
 Storage Driver: overlayfs
 Logging Driver: json-file
 Cgroup Driver: systemd
 Cgroup Version: 2
 Plugins:
  Log:     fluentd journald json-file none syslog
  Storage: native overlayfs soci
 Security Options:
  seccomp
   Profile: builtin
  cgroupns
 Kernel Version:   6.1.141-155.222.amzn2023.x86_64
 Operating System: Amazon Linux 2023.7.20250623
 OSType:           linux
 Architecture:     x86_64
 CPUs:             96
 Total Memory:     184.9GiB
 Name:             ip-172-31-53-55.us-west-2.compute.internal
 ID:               cafd1e17-3c20-4557-af0d-425ea0ef25bc

Pull SOCI Images with nerdctl

sudo nerdctl pull --snapshotter soci public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci

Output:

public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci: resolved       |++++++++++++++++++++++++++++++++++++++| 
index-sha256:fbacb61760f570e2f8df6eacd65a6285c8699405566fdb73711390e9d81db2b8:    done           |++++++++++++++++++++++++++++++++++++++| 
manifest-sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22:     done           |++++++++++++++++++++++++++++++++++++++| 
config-sha256:d007250deb6300c72286af12cf0510cf69b0c7227e4b4d50783a03ac8d917275:       done           |++++++++++++++++++++++++++++++++++++++| 
elapsed: 8.9 s     total:  37.9 K (4.3 KiB/s)

Verify the image:

sudo nerdctl images

Output:

REPOSITORY                                                  TAG                                            IMAGE ID        CREATED          PLATFORM       SIZE    BLOB SIZE
public.ecr.aws/deep-learning-containers/pytorch-training    2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci    fbacb61760f5    5 minutes ago    linux/amd64    0B      8.792GB

FUSE mount output:

{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Getxattr = 13","time":"2025-12-17T00:06:33.005689642Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Open = 0","time":"2025-12-17T00:06:33.005771355Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Readlink = 0","time":"2025-12-17T00:06:33.005780603Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: whiteout.Getattr = 0","time":"2025-12-17T00:06:33.005788862Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Listxattr = 0","time":"2025-12-17T00:06:33.005798239Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Lookup = 0","time":"2025-12-17T00:06:33.005805635Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Readdir = 0","time":"2025-12-17T00:06:33.005813816Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: file.Read = 0","time":"2025-12-17T00:06:33.005824207Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: file.Getattr = 0","time":"2025-12-17T00:06:33.005831686Z"}
{"level":"info","msg":"fuse operation count for image sha256:ca3e4389bb850cf3f43128b98897ac4522e20f354452a41139cfe7f45431da22: node.Getattr = 13","time":"2025-12-17T00:06:33.005839399Z"}

Run Containers with nerdctl

Run an interactive container with SOCI lazy loading:

sudo nerdctl run --snapshotter soci -it --rm --network host public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci /bin/bash

Note: You must use the --snapshotter soci flag with nerdctl to enable SOCI lazy loading. Without this flag, nerdctl will download the entire image. Additionally, since nerdctl requires CNI configuration, the --network host flag is used in this example.

Performance Comparison: SOCI vs Docker

Docker Pull Benchmark

time docker pull public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1

Output:

real    4m26.405s
user    0m0.129s
sys     0m0.127s

docker images

Output:

REPOSITORY                                                 TAG                                           IMAGE ID       CREATED        SIZE
public.ecr.aws/deep-learning-containers/pytorch-training   2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1        d007250deb63   32 hours ago   17.6GB

Analysis: The image size is 17.6GB. Using Docker to pull the full image takes 4m26.405s, while SOCI only pulls the index and necessary layers, taking just 8.9s(using nerdctl). The remaining layers are pulled during runtime as needed.

Container Runtime Comparison

Docker Runtime

time docker run public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1 'python -c "import torch; print(torch.__version__)"'

Output:

bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2.9.0+cu130

real    0m4.308s
user    0m0.010s
sys     0m0.023s

SOCI Runtime

time sudo nerdctl run --snapshotter soci --rm --network host public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci 'python -c "import torch; print(torch.__version__)"'

Output:

public.ecr.aws/deep-learning-containers/pytorch-training:2.9-gpu-py312-cu130-ubuntu22.04-ec2-v1-soci: resolved       |++++++++++++++++++++++++++++++++++++++| 
manifest-sha256:10f1280343bfe3e9acb43bbcf2ac78e0bf636df6181c1dbbfa6e84ac285d0948:    done           |++++++++++++++++++++++++++++++++++++++| 
config-sha256:d007250deb6300c72286af12cf0510cf69b0c7227e4b4d50783a03ac8d917275:      exists         |++++++++++++++++++++++++++++++++++++++| 
elapsed: 0.3 s     total:  7.3 Ki (24.3 KiB/s)                                      
bash: cannot set terminal process group (-1): Inappropriate ioctl for device
bash: no job control in this shell
2.9.0+cu130

real    0m22.785s
user    0m0.020s
sys     0m0.031s

Key Performance Benefits

  • Faster Initial Pull: SOCI reduces initial pull time from 4m26s to 8.9s (~97% reduction)
  • Reduced Bandwidth: Only pulls necessary layers during initial pull
  • Lazy Loading: Additional layers are fetched on-demand during runtime
  • Storage Efficiency: Reduces initial storage footprint while maintaining full functionality

References

For more information about SOCI snapshotter and related tools, please refer to the following resources: