openEuler AI Software Stack

Feature Description

• openEuler supports TensorFlow, PyTorch, and MindSpore frameworks and software development kits (SDKs) of major computing architectures, such as Compute Architecture for Neural Networks (CANN) and Compute Unified Architecture (CUDA), to make it easy to develop and run AI applications.
• Environment setup is further simplified by containerizing software stacks. openEuler provides three types of container images:
  Figure 1 Container images
  
  • SDK images: Use openEuler as the base image and install the SDK of a computing architecture, for example, Ascend CANN and NVIDIA CUDA.
  • AI framework images: Use the SDK image as the base and install AI framework software, such as PyTorch and TensorFlow. You can use an AI framework image to quickly build a distributed AI framework, such as Ray.
  • Model application images: Provide a complete set of toolchains and model applications.

For details, see openEuler AI Container Image User Guide.

Application Scenarios

openEuler uses AI container images to simplify deployment of runtime environments. You can select the container image that best suits your requirements and complete the deployment in a few simple steps.

• SDK images: You can develop and debug Ascend CANN or NVIDIA CUDA applications using an SDK image, which provides a compute acceleration toolkit and a development environment. These containers offer an easy way to perform high-performance computing (HPC) tasks, such as large-scale data processing and parallel computing.
• AI framework images: This type of containers is designed to support AI model development, training, and inference.
• Model application images: Such an image contains a complete AI software stack and purpose-built models for model inference and fine-tuning.