[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121066-en":3,"doc-seo-121066-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121066,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",6,"Technology","Creating a Docker Environment for Jupyter Notebook-Based Machine Learning Projects - local on-premise setup guide","This project proposes configuration of a locally optimized development environment for Artificial Intelligence projects by combining containers with Jupyter notebooks. While Google Colab provides fast cloud access, subscription costs and resource limits can restrict certain workloads. The guide advocates building an on-premise, locally executable alternative that preserves Colab-style usability while enabling full hardware customization. Hardware selection, dependency installation, and environment setup steps are presented to help users gain control, flexibility, and GPU choices.","| Number | 296 |\n| --- | --- |\n| Publication Year | 2024 |\n| Acceptance in OA@INAF | 2024-03-21T14:57:53Z |\n| Title | Creating a Docker Environment for Jupyter Notebook-Based Machine Learning Projects |\n| Authors | CABRAS, Alessandro |\n| Affiliation of first author | O.A. Cagliari |\n| Handle | [http://hdl.handle.net/20.500.12386/35013](http://hdl.handle.net/20.500.12386/35013) ;\u003Cbr>[https://doi.org/10.20371/INAF/TechRep/296](https://doi.org/10.20371/INAF/TechRep/296) |\n\nCreating a Docker Environment for Jupyter Notebook-Based Machine Learning Projects  \nAuthors  \nALESSANDRO CABRAS 1  \n1 INAF-Osservatorio Astronomico di Cagliari  \nReviewers  \nANTONIO PODDIGHE  \nANTONIETTA ANGELA RITA FARA  \nContents  \nAbstract ii  \nList of Acronyms iv  \nList of Figures vi  \n1 Introduction 1  \n1.1 Choosing the Right Hardware for Machine Learning Environments ......... 2  \n1.2 Nvidia GeForce RTX 4090: A low budget solution .................. 3  \n2 Popular tools for ML Development 7  \n2.0.1 Jupyter Notebooks ................................ 7  \n2.0.2 Google Colab ................................... 8  \n3 Setting Up a Local Environment 11  \n3.1 Docker configuration ................................... 11  \n3.2 Link Google Drive as volume .............................. 13  \n3.3 Automatize the process ................................. 14  \nConclusions 16  \nBibliography 19  \nAbstract  \nThis project proposes the configuration of a locally optimized development environment for Artificial 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 development environment that provides greater control and flexibility, while retaining the convenience and familiarity associated with Google Colab.  \nList of Acronyms  \nAI Artificial Intelligence  \nGPU Graphics Processing Unit  \nECC Error-Correcting Code  \nHPC High Power Computing  \nCUDA Compute Unified Device Architecture  \nPCIe Peripheral Component Interconnect express  \nOAC Osservatorio Astronomico di Cagliari  \nTPU Tensor Processing Units  \nList of Figures  \n1.1 Two GPU NVIDIA RTX 4090, installed in a Supermicro server at the Osservatorio Astronomico di Cagliari (OAC) .............................. 4  \n1.2 ZOTAC GAMING GeForce RTX 4090 Trinity graphics card [1] ........... 4  \n1.3 One of the graphs extracted from the web comparing Graphics Processing Unit (GPU) performance in the market relative to their price. [2] ............. 5  \n2.1 Descriptive diagram of Google Colab, from user authentication to cloud resource allocation.......................................... 8","cbCaitgrfjda0lxD","https://ap.wps.com/l/cbCaitgrfjda0lxD","pdf",4766212,1,30,"English","en",105,"# Introduction\n## Choosing the Right Hardware for Machine Learning Environments\n## Nvidia GeForce RTX 4090: A low budget solution\n# Popular tools for ML Development\n## Jupyter Notebooks\n## Google Colab\n# Setting Up a Local Environment\n## Docker configuration\n## Link Google Drive as volume\n## Automatize the process\n# Conclusions\n# Bibliography","[{\"question\":\"Why build a local Docker environment instead of using Google Colab?\",\"answer\":\"Google Colab can be limited by subscription costs and constrained resources. 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