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An Implementation of Face Recognition with Deep Learning based on a Container-Orchestration Platform

机译:基于容器编排平台的深度学习人脸识别实现

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As a lightweight alternative to a virtual machine, a container runs applications only with the necessary environmental variables, libraries, etc. Moreover, many more containers can be run on the same computer compared to traditional VMs, which take up a lot of computing resources. Currently, Docker container and Kubernetes (K8s), which is a container-orchestration platform, are very popular tools. In addition, K8s is a high availability (HA) system with many features that can provide containers to implement more applications. In this project, a face recognition application is implemented with deep learning on Kubeflow, which is a machine learning platform running on K8s. Also, the deep learning method output features instead of classifications. This method computes the distance between two images with Triplet loss function and Euclidean distance. K8s runs on the server as a private cloud, on which our face recognition application runs.
机译:作为虚拟机的轻量级替代方案,容器仅使用必要的环境变量,库等来运行应用程序。此外,与传统的VM相比,容器可以在同一台计算机上运行更多的容器,而传统的VM占用大量的计算资源。当前,Docker容器和Kubernetes(K8s)是一个容器编排平台,是非常流行的工具。此外,K8s是具有许多功能的高可用性(HA)系统,这些功能可以提供容器来实现更多应用程序。在此项目中,人脸识别应用程序通过在Kubeflow上进行深度学习而实现,Kubeflow是在K8s上运行的机器学习平台。此外,深度学习方法输出特征而不是分类。该方法计算具有三重态损失函数和欧几里得距离的两个图像之间的距离。 K8s作为私有云在服务器上运行,我们的面部识别应用程序在该私有云上运行。

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