#Install jupyter notebook miniconda install#

# Create virtual environment for ML conda create -n ML # Install machine learning / data science related packages conda install numpy scipy pandas matplotlib seaborn bokeh jupyter jupyterlab scikit-learn Launch Jupyter notebook in the container Open # jupyter notebook jupyter notebook -port 8888 -ip = 0.0.0.0 -allow-root # For jupyter lab, do the following jupyter lab -port 8888 -ip = 0.0.0.0 -allow-root Since I put it in the command prompt in the container with the previous command, install the package (create a virtual environment if necessary). With jupyter notebook, files can be uploaded and downloaded into the container via a browser, so it may not be necessary. Here, for the time being -vThe shared folder is mounted with the option. v: Share the host folder (here / home / docker / miniconda) with / home in the container.First ' mkdir -p docker/minicondaIf you put a file (analysis data, etc.) in the folder created in ', you can share it with the container. p : Connect the port of the host and the container: You can open the jupyter notebook from the browser it: Connect the command prompt of the container and the host name : Name to give to the container to be made You can get the Miniconda 3 (python 3.7 based Miniconda) image from your terminal with the following command: # docker Create a place to put files shared with the container mkdir -p docker / miniconda # Get the official image of miniconda3 docker pull continuumio / miniconda3 # Start the miniconda container docker run -name miniconda3 -it -p 8888 : 8888 -v / home / / docker / miniconda: / home continuumio / miniconda3 / bin / bash The official Docker image (Anaconda 2 or 3, Miniconda 2 or 3) is below. Install Ubuntu Desktop 18.04 LTS on external SSD and multiboot Get Miniconda image / Create container

Install jupyter notebook miniconda