jupyter conda

[anneng]

http://webcache.googleusercontent.com/search?q=cache:wK_gyEsIr0QJ:https://towardsdatascience.com/managing-jupyterlab-based-data-science-projects-using-conda-pip-cd2ee8521705&hl=en&gl=am&strip=1&vwsrc=0

Examples of “project-based” JupyterLab installs
As promised here are a few examples of the “project-based” approach that you can use as inspiration for your next data science project.

JupyterLab + Scikit Learn + Dask: Environment for CPU-based data science projects that combines JupyterLab with Scikit-learn and Dask (and friends!). Includes some common JupyterLab extensions.
JupyterLab + PyTorch: Standard environment for GPU-accelerated deep learning with JupyterLab and PyTorch. Includes GPU and deep learning specific JupyterLab extensions such as jupyterlab-nvdashboard and jupyterlab-tensorboard.
JupyterLab + NVIDIA RAPIDS + BlazingSQL + Dask: More complex environment for GPU-accelerated machine learning with JupyterLab, NVIDIA RAPIDS, BlazingSQL, and Dask (and many friends!). Includes some common JupyterLab extensions as well as some GPU-specific ones such as jupyterlab-nvdashboard.