If I already use notebooks locally, why should I use JupyterLab on a VM with a GPU? How do I manage dependencies (pip/conda), data, and permissions without breaking my environment?
Running JupyterLab on a virtual machine (VM) with a GPU, particularly in cloud environments such as Google Cloud, offers several significant advantages for deep learning workflows compared to using local notebook environments. Understanding these advantages, alongside strategies for effective dependency, data, and permissions management, is critical for robust, scalable, and reproducible machine learning development. 1.
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Deep learning VM Images
How does one install Anaconda?
Installing Anaconda is a strategic step for professionals and students working with machine learning, data science, and artificial intelligence, especially when leveraging Google Cloud’s machine learning offerings. Anaconda is a widely adopted Python distribution that simplifies package management, environment isolation, and dependency resolution. This comprehensive explanation covers the installation process, the rationale for using Anaconda,