Create a folder inside your project’s folder where we’ll store all our Jupyter Noteboos with source code of our projects: mkdir notebooksĪnd start the container with the following command: docker run -it -p 8888:8888 -p 6006:6006 \ Now you have a working container and it’s time to start it. # Store notebooks in this mounted directoryĪs you can see, we’re installing just only libgtk2.0 for OpenCV support and all the other components like Terraform, Pandas, Scikit-learn, Matplotlib, Keras and others using conda package manager.
ANACONDA INSTALL MATPLOTLIB INSTALL
opt/conda/bin/conda install numpy pandas scikit-learn matplotlib seaborn pyyaml h5py keras -y & \ opt/conda/bin/conda install -channel opencv3 -y & \ opt/conda/bin/conda install jupyter -y & \ opt/conda/bin/conda install anaconda-client & \ opt/conda/bin/conda install python=3.6 & \ RUN /opt/conda/bin/conda update -n base -c defaults conda & \ RUN apt-get update & apt-get install -y libgtk2.0-dev & \ Here how it looks like: FROM continuumio/anaconda3 I’m using the same sources, but changing Dockerfile. It was much faster, then to compile OpenCV 3 for Ubuntu 16.04. When I started playing with ML in 2018 Anaconda was a super fast and easiest way to create Docker container for ML experiments. This process takes ~7 minutes to build the container of 3.11 Gb in size.
ANACONDA INSTALL MATPLOTLIB HOW TO
And in this article I’ll show you how to do it much faster using Anaconda official Docker Image. Well, I spent whole day preparing new image build. Suddenly, I understood, that I’ve missed OpenCV for Docker image and video manipulations. Last time we’ve created Docker container with Jupiter, Keras, Tensorflow, Pandas, Sklearn and Matplotlib. Recently we published an article Quick And Simple Introduction to Kubernetes Helm Charts in 10 minutes, where you can find instructions on how to use Helm to deploy this container to your Kubernetes cluster. This image is quite useful if you’re developing ML models or you need a pre-configured Jupyter notebook with some of the most useful libraries. In this article, we’ll build a Docker container for Machine Learning (ML) development environment.