考虑使用深度学习框架，它是一个界面、库或工具，允许用户更轻松、更快速地构建深度学习模型，而无需深入了解底层算法的细节。深度学习框架为使用一组预构建和优化的组件定义模型提供了一种清晰简洁的方法。
三种流行的技术包括以下技术：

PyTorch

PyTorch 主要由 Facebook 的 AI 研究实验室开发，是一个基于 Torch 库的开源机器学习库。它用于计算机视觉和自然语言处理等应用程序，是在修改后的 BSD 许可下发布的免费开源软件。

TensorFlow

使用 TensorFlow 用户友好的基于 Keras 的 API 实现快速原型设计、研究和生产，这些 API 用于定义和训练神经网络。

咖啡

Caffe 提供了一个富有表现力的架构，允许用户在没有硬编码的情况下定义和配置模型和优化。用户可以设置单个标志在 GPU 机器上训练模型，然后部署到商品集群或移动设备。

基于硬件的加速器工具包

AI 加速器工具包可从硬件供应商处获得，专门用于在其平台上加速人工智能应用程序，例如机器学习和计算机视觉。

英特尔 OpenVINO

英特尔的开放视觉推理和神经网络优化 (OpenVINO) 工具包旨在帮助开发人员在英特尔平台上构建强大的计算机视觉应用程序。OpenVINO 还可以加快深度学习模型的推理速度。

英伟达 CUDA

CUDA 工具包为嵌入式系统、数据中心、云平台和基于 NVIDIA 计算统一设备架构 (CUDA) 的超级计算机上的 GPU 加速应用程序提供高性能并行计算。



 

但同样重要的是，还要考虑将实施应用程序的物理位置。部署在户外或恶劣环境中的工业应用，例如智慧城市、石油和天然气、采矿、电力或户外巡逻机器人应用，应具有较宽的工作温度范围和适当的散热机制，以确保在酷热或严寒天气条件下的可靠性。

某些应用还需要特定行业的认证或批准，例如无风扇设计、防爆结构和抗振性。由于许多实际应用程序部署在空间有限的机柜中并受到尺寸限制，因此小型边缘计算机。

此外，远程站点中高度分布式的工业应用可能还需要通过可靠的蜂窝或 Wi-Fi 连接进行通信。例如，具有集成蜂窝 LTE 连接的工业边缘计算机无需额外的蜂窝网关，并节省宝贵的机柜空间和部署成本。另一个考虑因素是，可能还需要支持双 SIM 卡的冗余无线连接，以确保在一个蜂窝网络信号较弱或出现故障时可以传输数据。

要了解现实世界的工业应用如何启用 AIoT 边缘计算并从中受益。

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Consider using a deep learning framework, which is an interface, library, or tool that allows users to build deep learning models more easily and quickly, without getting into the details of the underlying algorithms. Deep learning frameworks provide a clear and concise way for defining models using a collection of pre-built and optimized components.
The three most popular include the following technologies:

PyTorch

Primarily developed by Facebook’s AI Research Lab, PyTorch is an open source machine learning library based on the Torch library. It is used for applications such as computer vision and natural language processing, and is a free and open-source software released under the Modified BSD license.

TensorFlow

Enable fast prototyping, research, and production with TensorFlow’s user-friendly Keras- based APIs, which are used to define and train neural networks.

Caffe

Caffe provides an expressive architecture that allows users to define and configure models and optimizations without hard-coding. Users can set a single flag to train the model on a GPU machine, and then deploy to commodity clusters or mobile devices.

Hardware-based accelerator toolkits

AI accelerator toolkits are available from hardware vendors and are specially designed to accelerate artificial intelligence applications, such as machine learning and computer vision, on their platforms.

Intel OpenVINO

The Open Visual Inference and Neural Network Optimization (OpenVINO) toolkit from Intel is designed to help developers build robust computer vision applications on Intel platforms. OpenVINO also enables faster inference for deep learning models.

NVIDIA CUDA

The CUDA Toolkit enables high-performance parallel computing for GPU-accelerated applications on embedded systems, data centers, cloud platforms, and supercomputers built on the Compute Unified Device Architecture (CUDA) from NVIDIA.

Environmental considerations

Last but not least, also consider the physical location of where the application will be implemented. Industrial applications deployed outdoors or in harsh environments such as smart city, oil and gas, mining, power, or outdoor patrol robot applications should have a wide operating temperature range and appropriate heat dissipation mechanisms to ensure reliability in blistering hot or freezing cold weather conditions.

Certain applications also require industry-specific certifications or approvals, such as fanless design, explosion proof construction, and vibration resistance. And since many real-world applications are deployed in space-limited cabinets and subject to size limitations, small form factor edge computers are preferred.

Moreover, highly distributed industrial applications in remote sites may also require communications over a reliable cellular or Wi-Fi connection. For instance, an industrial edge computer with integrated cellular LTE connectivity eliminates the need for an additional cellular gateway and saves valuable cabinet space and deployment costs. Another consideration is that redundant wireless connectivity with dual SIM support may also be needed to ensure that data can be transferred if one cellular network signal is weak or goes down.