About Advantech Container Catalog (ACC)

**Advantech Container Catalog** is a comprehensive collection of ready-to-use, containerized software packages designed to accelerate the development and deployment of Edge AI applications. By offering pre-integrated solutions optimized for embedded hardware, it simplifies the challenges often faced with software and hardware compatibility, especially in GPU/NPU-accelerated environments.
Key benefits of the Container Catalog include: | **Feature / Benefit** | **Description** | |----------------------------------------------------|--------------------------------------------------------------------------------| | Accelerated Edge AI Development | Ready-to-use containerized solutions for fast prototyping and deployment | | Hardware Compatibility Solved | Eliminates embedded hardware and AI software package incompatibility | | GPU/NPU Access Ready | Supports passthrough for efficient hardware acceleration | | Model Conversion & Optimization | Built-in AI model quantization and format conversion support | | Optimized for CV & LLM Applications | Pre-optimized containers for computer vision and large language models | | Open Ecosystem | 3rd-party developers can integrate new apps to expand the platform |

--- ## Container Overview **CUDA^®-enabled x86 Container** is a base image that provides a pre-configured NVIDIA CUDA® Toolkit development environment with NVIDIA GPU passthrough capabilities, designed for industrial AI and machine learning applications. This solution simplifies the deployment of GPU-accelerated workloads in edge computing environments while ensuring consistent, reproducible results across development and production environments. --- ## Key Features Optimized for AI development on NVIDIA GPU, this container provides flexible NVIDIA CUDA® Toolkit/cuDNN configurations, full GPU passthrough, and essential tools pre-installed for rapid deployment. | Feature | Content | |-----------|----------------| |**Automatic CUDA® Detection**|Automatically detects installed NVIDIA CUDA® Toolkit version from your system| |**Flexible Configuration**| Supports command-line configuration of NVIDIA CUDA® Toolkit and cuDNN versions| |**Multi-Version Support**| Compatible with NVIDIA CUDA® Toolkit versions from 11.8 to 12.4 and corresponding cuDNN libraries| |**Complete GPU Passthrough**|Utilizes all available NVIDIA GPUs with full compute capabilities| |**Optimized Memory Management**| Configured with 8GB shared memory for high-performance GPU operations| --- ## Application Support The Advantech **CUDA®-enabled x86 Container** container is tailored for high-performance AI and ML workloads. It supports a wide range of frameworks and libraries optimized for GPU acceleration in edge and industrial environments: | Application | Content | |-----------|---------------| |**Deep Learning Frameworks**|TensorFlow, PyTorch, JAX, MXNet| |**Computer Vision Libraries**|OpenCV, NVIDIA TensorRT| |**HPC Applications**|CUDA®-accelerated scientific computing| | **Data Science**|NumPy, SciPy, scikit-learn with GPU acceleration| |**NLP Processing**|BERT, GPT, and transformer models| |**Industrial Applications**|Edge AI, Computer Vision, Predictive Maintenance| --- ## Host Device Prerequisites Users should ensure that the selected Advantech host device meets the specifications below and has the required software packages installed before running this container image. | Item | Specification | |-----------|---------------| | Compatible Hardware | Advantech x86 Edge Devices (tested on AIR-520) - refer to [Compatible hardware](https://catalog.advantech.com/en-us/containers/x-edgesync-base-container/compatible-hardware) | |NVIDIA GPU | Host system with NVIDIA GPU(s) (tested on RTX 6000 Ada Generation) | |NVIDIA driver|550.120 or later, compatible with NVIDIA CUDA® Toolkit 11.8+| |Docker & Docker Compose|both versions > 2.0| |Recommended Device OS |Linux-based operating system (Ubuntu 20.04+)| |NVIDIA Container Toolkit|[Installing the NVIDIA Container Toolkit ](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)| |SUSI IoT Installation| SUSI drivers installed on host system [SUSI IoT Installation Guide](https://github.com/ADVANTECH-Corp/SUSI)| ### Validated Hardware Platform The **CUDA®-enabled x86 Container** has been thoroughly tested and validated on Advantech [AIR-520](https://www.advantech.com/zh-tw/products/5bfbf78e-c85b-43d2-83d9-7c12eabc6ef8/air-520/mod_9e703e06-0ce1-42ac-8fcd-9af05c1689f7) with detailed hardware specification below: | Item | Specification | |-----------|---------------| | **CPU** | AMD EPYC 7543P 32-Core Processor (64 logical cores) | |**Memory**|251GB RAM (246GB available)| |**GPU**|2x NVIDIA RTX 6000 Ada Generation (48GB VRAM each)| |**Operating System**| Ubuntu 22.04.5 LTS (Jammy Jellyfish)| |**Kernel Version**|6.8.0-59-generic| |**NVIDIA Driver**|550.144.03| | **NVIDIA CUDA® Toolkit Version**|12.2| |**cuDNN Version**|8.9.7| --- ## Container Environment Overview ### Pre-installed Development Tools on Container Image The **CUDA®-enabled x86 Container** container comes with these essential development tools pre-installed: | Type | Components | |-----------|---------------| | Build Tools | gcc/g++, make, cmake, build-essential | | Version Control | git | |Python Environmen|Python 3 with pip| | Utilities | wget, curl, vim, unzip | | System Tools | ca-certificates, gnupg2, lsb-release, software-properties-common | ### GPU Passthrough Capabilities The Advantech L2-01 container supports full GPU passthrough for: - NVIDIA RTX/Quadro series (tested with RTX 6000 Ada Generation) - NVIDIA Tesla/A-series accelerators - NVIDIA GeForce RTX/GTX series - Multi-GPU configurations with automatic load balancing - Support for NVIDIA CUDA® Toolkit compute capability 8.x to 9.x devices The container can handle: - Up to 8 GPUs simultaneously - Up to 96GB VRAM per GPU - Mixed GPU types in the same system - Dynamic GPU allocation >### Container Quick Start Guide >For container quick start, including docker-compose file, and more, please refer to [Advantech Container Repository](https://github.com/Advantech-Containers/X86-GPU_Passthrough-EdgeSync-SDK) ### Test Results The diagnostic report confirms full functionality of all GPU components on this container, showcaseing GPU access passthroguh is fully enabled: ``` ====== FINAL REPORT ====== ✅ ALL CHECKS PASSED: Your NVIDIA CUDA® Toolkit environment appears to be properly configured ┌─────────────────────────────────────────────────────┐ │ NVIDIA CUDA® Toolkit Environment Summary │ ├───────────────────────┬─────────────────────────────┤ │ Overall Status │ PASS │ ├───────────────────────┼─────────────────────────────┤ │ NVIDIA Driver │ 550.144.03 │ │ NVIDIA CUDA® Toolkit Version │ 12.2 │ │ cuDNN Version │ 8.9.7.* │ │ GPU Count │ 2 │ ├───────────────────────┼─────────────────────────────┤ │ Driver Status │ ✓ Passed │ │ NVIDIA CUDA® Toolkit Toolkit Status │ ✓ Passed │ │ cuDNN Status │ ✓ Passed │ │ NVIDIA CUDA® Toolkit Test Status │ ✓ Passed │ └───────────────────────┴─────────────────────────────┘ ``` ### GPU Performance Validation Detail of detected GPUs showing full hardware access and acceleration capabilities: ``` Device 0: NVIDIA RTX 6000 Ada Generation Compute capability: 8.9 Total global memory: 47.50 GB Multiprocessors: 142 Device 1: NVIDIA RTX 6000 Ada Generation Compute capability: 8.9 Total global memory: 47.50 GB Multiprocessors: 142 ``` ### NVIDIA Driver Performance The container has passed all driver functionality tests with full access to the underlying NVIDIA hardware: ``` Wed May 7 02:32:35 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.144.03 Driver Version: 550.144.03 NVIDIA CUDA® Toolkit Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA RTX 6000 Ada Gene... Off | 00000000:01:00.0 Off | Off | | 33% 62C P8 41W / 300W | 12MiB / 49140MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA RTX 6000 Ada Gene... Off | 00000000:81:00.0 On | Off | | 34% 62C P8 34W / 300W | 334MiB / 49140MiB | 4% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ ``` Copyright © Advantech Corporation. All rights reserved.

Model name			Available tag
AIR-510 4U Edge AI HPC powered by Intel® Raptor Lake S CPU and NVIDIA Certified with RTX™ 6000 Ada Generation	x64	Nvidia	edgesync.azurecr.io/advantech/x-nvidia-gpu-passthrough
AIR-520 4U Edge AI HPC powered by AMD EPYC 7003 series processor and NVIDIA Certified with RTX™ 6000 Ada Generation	x64	Nvidia	edgesync.azurecr.io/advantech/x-nvidia-gpu-passthrough
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