Setting up a minimalist Python environment from scratch. How To Use Miniconda With Python and JupyterLab.
We’re excited to announce that the Azure SDK for Python (Conda) Preview packages are now available in the Microsoft channel.
If Anaconda (conda) and Jupyter Notebook (Jupyter Lab) are set up the right way the combination of them can become the perfect team, where you are able to easily switch between Deep Learning conda environments. Please find out the following article
Build a Docker image running a multi-kernel Jupyter notebook server in fifteen minutes. Docker and Docker-Compose are great utilities that support the microservice paradigm by allowing efficient containerization. Within the python ecosystem the package manager Conda also allows some kind of containerization that is limited to python packages.
Guide to conda and conda-forge .Publishing Your Python Package on conda and conda-forge
Quick tutorial for packaging Python project using Anaconda with Docker
This article discusses two approaches for managing JupyterLab-based data science projects using Conda (+pip): a “system-wide” approach where Conda (+pip) are used to manage a single JupyterLab installation that is shared across all projects, and a “project-based” approach where Conda (+pip) are used to manage.
Creating a Portable Python Environment from Imports. Python environments provide sandboxes in which packages can be added. Conda helps us deal with the requirements and dependencies of those packages. Occasionally we find ourselves working in a constrai
“Best practices” for managing CUDA dependencies on your next data science project. Transitioning your data science projects from CPU to GPU can seem like a daunting task.
In this article, I will walk you through the process that I go through when I need to use Conda to install packages when working in Google Colab.