DEEP as a Service (DEEPaaS) is a REST API that is focused on providing access to machine learning models. By using DEEPaaS users can easily run a REST API in fron of their model, thus accesing its functionality via HTTP calls. Quickstart: The best way to quickly try the DEEPaaS API is trough: make run. This command will install a virtualenv (in the `virtualenv` directory) with DEEPaaS and all its dependencies and will run the DEEPaaS REST API, listening on `127.0.0.1:5000`. If you browse to `http://127.0.0.1:5000` you will get the swagger documentation page. Develop mode: If you want to run the code in develop mode (i.e. `pip install -e`), you can issue the following command before: make develop., DEEP as a Service (DEEPaaS) is a REST API that is focused on providing access to machine learning models. By using DEEPaaS users can easily run a REST API in fron of their model, thus accesing its functionality via HTTP calls., Proyecto: Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (DEEP-HybridDataCloud)., Peer reviewed

Apache License, Version 2.0, This is a plug-and-play tool to train and evaluate an image classifier on a custom dataset using deep neural networks., DEEP Hybrid-Datacloud, GA 777435., Peer reviewed

Apache License 2.0, Agencia Financiadora: EC. Acuerdo de subvención: H2020 777435, No

Apache License 2.0, EC: H2020 777435., Peer reviewed

Apache License 2.0, EC: H2020 777435, Peer reviewed

Apache License 2.0, EC: H2020 777435, Peer reviewed

Apache 2.0, EC: H2020 777435, Peer reviewed

Apache 2.0, EC: H2020 777435, Peer reviewed

This work provides a tool to train and evaluate an image regression model. The code is prepared for liquid volume estimation of glass containers as part of the BrainGut_WineUp project. It was adapted from the original image classification module developed in the DEEP Hybrid-DataCloud project., Miriam Cobo acknowledges support from Consejo Superior de Investigaciones Científicas (CSIC) and Instituto de Física de Cantabria (IFCA) via the “Proyecto Branyas” project., Peer reviewed