Ruolo
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oneclickstack
Descrizione
A fully configured runtime environment installation: features of professional web hosting services or “platform as a service” PaaS (web server, database, programming languages and libraries).
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The runtime environment constructor for the machine learning and deep learning tutorials and courses.
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Web application environment with Linux, Apache HTTPD, PostgreSQL SQL databases server and PHP. PHP works as the mod_php
Apache module.
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A pre-configured and ready-to-use Node.js 7 web stack with Nginx.
nodejs:7.9.0, min-nginx:1.11.2, selfmanagement_preset, development_preset, express_js_blank:1
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A pre-configured and ready-to-use Node.js 7 web stack with Nginx.
nodejs:7.9.0, min-nginx:1.11.2, selfmanagement_preset, development_preset, express_js_blank:1
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A pre-configured and optimized for better performance LEMP environment for web-applications with the next generation of PHP version 7. It is similiar to the LAMP stack, where Apache is replaced with the lightweight yet powerful Nginx, and PHP works in `php-fpm` mode.
min-nginx:1.11.2, mariadb-mysqld:10.1.22, php:7.1.4, selfmanagement_preset, phpmyadmin:4.7.0, memcached:1.4.36, redis:3.2.8
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A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, and the Python programming language. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack is designed for short and long-running high-performance tasks, and can be easily integrated into continuous integration and deployment workflows. It is built with the Intel MKL and MKL-DNN libraries and optimized for running on CPU.
tensorflow:1.8.0, python:3.6.3, development_preset:1
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A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, and the Python programming language. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack is designed for short and long-running high-performance tasks, and can be easily integrated into continuous integration and deployment workflows. It is built with the Intel MKL and MKL-DNN libraries and optimized for running on CPU.
tensorflow:1.8.0, python:2.7.14, development_preset:1