CABRAS, AlessandroAlessandroCABRAS2024-03-212024-03-212024http://hdl.handle.net/20.500.12386/35013https://doi.org/10.20371/INAF/TechRep/296This project proposes the configuration of a locally optimized development environment for Artifi- cial Intelligence projects, leveraging containers and Jupyter notebooks. While services like Google Colab offer quick and convenient access to pre-configured cloud resources for machine learning, subscription costs and resource limitations may be restrictive for some projects. To overcome these challenges, the creation of a locally executable environment similar to Colab is suggested, but deployable on-premise on a local server. This approach allows for full hardware customization, including GPU selection, and eliminates subscription cost constraints. In the following chapters, the necessary steps to configure this local environment will be outlined, starting from hardware selection and proceeding with the installation of required dependencies and environment setup. By following these guidelines, users will be able to establish a local machine learning develop- ment environment that provides greater control and flexibility, while retaining the convenience and familiarity associated with Google Colab.ELETTRONICOenTechnical reportFIS/05 - ASTRONOMIA E ASTROFISICA