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En este Trabajo Final de Máster se pretende desplegar y aprovisionar de forma desatendida y distribuida un conjunto de nodos que lleven a cabo el entrenamiento de un modelo de deep learning (DL). Para este fin, se utilizará el framework de DL Tensorflow, junto con la herramienta Infrastructure Manager que permite el despliegue de infraestructuras complejas de forma programática. Se requiere que la infraestructura contemple: la gestión de los datos de entrenamiento, el entrenamiento del modelo con estos datos y la persistencia del modelo entrenado resultante. Se utilizarán plataformas de Cloud público como Amazon Web Services y, en la medida de lo posible, dispositivos de cómputo específico como GPGPUs., In this Master¿s thesis we focus on deploying and provisioning in an unattended and distributed way a set of nodes that conduct the training of a deep learning model. To this end, the deep learning framework TensorFlow will be used, as well as the Infrastructure Manager that allows us to deploy complex infrastructures programmatically. The nal infrastructure should consider: data handling, model training using this data, and the persistence of the trained model. For this purpose, it will be used public Cloud platforms such as Amazon Web Services (AWS) and General-Purpose Computing on Graphics Processing Units (GPGPU).

Magentix2 is an agent platform for open Multiagent Systems. Its main objective is to bring agent technology to real domains: business, industry, logistics, e-commerce, health-care, etc. Magentix2 platform is proposed as a continuation of the first Magentix platform. The final goal is to extend the functionalities of Magentix, providing new services and tools to allow the secure and optimized management of open Multiagent Systems. Nowadays, Magentix2 provides support at three levels: - Organization level, technologies and techniques related to agent societies. - Interaction level, technologies and techniques related to communications between agents. - Agent level, technologies and techniques related to individual agents (such as reasoningand learning). Thus, Magentix2 platform uses technologies with the necessary capacity to cope with the dynamism of the system topology and with flexible interactions, which are both natural consequences of the distributed and autonomous nature of its components. In this sense, the platform has been extended in order to support flexible interaction protocols and conversations, indirect communication and interactions among agent organizations. Moreover, other important aspects cover by the Magentix2 project are the security issues.