Ruolo
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).
The runtime environment constructor for the machine learning and deep learning tutorials and courses.
Web application environment with Linux, Apache HTTPD, PostgreSQL SQL databases server and PHP. PHP works as the mod_php Apache module.
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
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
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
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
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
