AI Integration in Business: A Case Study of NetApp ONTAP AI in AI_LAB

• 基础设施即服务 (IaaS) - 混合云
• 基础设施即服务 (IaaS) - 公共云

• 建筑物
• 教育

• 人力资源
• 质量保证

• 临床图像分析
• 智能包装

• 云规划/设计/实施服务
• 培训

在过去几年中，随着公司利用连接到亚马逊网络服务 (AWS)、微软 Azure 和谷歌云平台 (GCP)，验证 TSC 系统和公共云之间的数据和服务集成的机会呈指数级增长。人工智能/深度学习验证项目的数量也显着增加。

对混合云和人工智能/深度学习验证的需求不断增长，促使 CTC 创建这种新的验证环境。

人工智能专用验证环境AI_LAB采用NetApp与NVIDIA联合开发的集成系统NetApp ONTAP AI。

伊藤忠技术解决方案

ITOCHU Techno-Solutions使用 NetApp ONTAP AI 集成 AI 系统构建了大规模的 AI 验证环境。该公司正在开发一个使用 Docker 容器的镜像库，并希望实现一个可以快速响应各种验证请求的高级 AI 验证环境。

采用由 NetApp 和 NVIDIA 联合开发的 NetApp ONTAP AI

NetApp ONTAP AI 集成系统集成了 NVIDIA DGX-1 AI，集成了 8 个 Tesla V100 和 NetApp AFF A800 高端全闪存阵列，形成了针对 AI/深度学习应用进行优化的参考架构。

NetApp 的人工智能集成系统“NetApp ONTAP AI”已被用作核心系统。在进行了包括云集成在内的各种人工智能/深度学习验证测试之后，它现在为客户公司将人工智能用于业务做出了重大贡献。

• The adoption of NetApp ONTAP AI in AI_LAB has significantly contributed to the utilization of AI for business by client companies. The system's integration with public cloud services allows customers to easily build an AI/deep learning data pipeline, enabling not just verification of cooperative processing using GPUs on the cloud side and hierarchical management of data that incorporates cloud storage, but also verification of the return on investment of on-premises and cloud AI environments. Furthermore, the system's high level of compatibility with container environments, such as the provision of NetApp Trident, a tool that makes it easy to create the “persistent volumes” that are essential for handling applications and databases in a container environment, allows for the movement of applications that incorporate the learning model into the cloud and run them there. This has resulted in an environment in which the customer can use the necessary data and computational resources immediately on demand, maximizing the time customers spend on verification and increasing the efficiency of AI_LAB resource utilization.

• Throughput of up to 25 GB/s achieved in performance verification of NetApp ONTAP AI
• Latency of 500 microseconds or less while constantly maintaining utilization rate of at least 95% on all 32 GPUs of the DGX-1 cluster
• AI_LAB is always fully booked at least 3 months in advance, indicating high demand and utilization