LABEL description=”Debugging Jupyter Notebook”WORKDIR /jup FROM continuumio/miniconda3ĭefine the metadata of the Docker file and working directory: LABEL maintainer=”Manish Tiwari ” It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages. We will be using Miniconda, a minimal lightweight installer for Anaconda. You can think of the image as the file having instructions to include everything that is required to run our application in the containers. Now we will create the Dockerfile needed to create the Docker image of our required environment. However, if you feel you need to revisit then please refer to the Docker documentation. Explaining how docker works is out of the scope of this article. I assume that you are familiar with basic Docker commands and terminologies. Dockerize Jupyter with the Visual Debugger enabled In this way, we no longer need to worry about the OS and other environment-specific dependencies as everything is packaged in one single independent entity that can run anywhere and everywhere. You’ll run your tests on Debian and production is on Red Hat and all sorts of weird things happen.”Ĭontainer solves this problem by bundling the environment needed to run the application, the dependencies, binaries, all the necessary configurations and the application itself into one package. Or you’ll rely on the behavior of a certain version of an SSL library and another one will be installed. “You’re going to test using Python 2.7, and then it’s going to run on Python 3 in production and something weird will happen. Problems arise when the supporting software environment is not identical, says Docker creator Solomon Hykes. It’s why they’re the technological foundation for the cloud-native approach to app delivery. Why Containerize?Ĭontainers enable smoother development across multiple environments. Now if you run the Jupyter Lab, you should be able to see 2 additional icons, 1 each in the console and notebook sections for the xeus-python kernel. In the future, there may be many other kernels having support for this protocol. In the backend as of now, only xeus-python has support for Jupyter debugging protocol. Jupyter labextension install Installing kernel xeus-python : In future releases, Jupyter may include this extension by default. The JupyterLab uses nodejs to install extensions, so we need to install nodejs as well in order to install the frontend debugger extension. Installing JupyterLab extension for enabling the frontend debugging: Installation:Īssuming that you are already using JupterLab you just need to install JupyterLab debugger extension for the frontend debugging and any kernel supporting the Jupyter debugging protocol at the backend. Prerequisites:īasic understanding of debugging in any programming languageīasic understanding of Docker. In this article, we will be going through the steps needed for setting up the visual debugger in the existing JupyterLab environment and will also dockerize the JupyterLab environment with the visual debugger enabled by default. This feature was much awaited by the data science community which is finally released now.įor a brief overview of how the visual debugger looks in action, please refer below screencast: Screencast by Jeremy, on Github However, the only concern was the missing visual debugging ability due to which people usually had to switch to other available classical IDEs which offer a better debugging and code refactoring ability. The Data Science community is relied heavily on Jupyter Notebooks due to its ability to easily communicate and share the outcomes in an interactive way. Though it is the first release it supports all the basic debugging requirements needed to debug and inspect variables, etc. Jupyter recently announced its first-ever public release of the much-awaited visual debugger.
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