VSCode Notebook, Nteract and Streamlit Problems ¶ Note that version 4.14.3 of plotly or earlier needed two extensions ( jupyterlab-plotly and plotlywidget) to be installed manually running, and that plotlywidget requires to be installed: To summarize: if you use JupyterLab with multiple python environments, the extensions must be installed in the "server" environment, and the plotly python library must be installed in each "processing" environment that you intend to use. To check if this is the problem, you can look at the active extension list through your browser via the JupyterLab Extension Manager, which will always list the extensions in the "server" environment. If you accidentally installed the extensions (and run the command above) in one of the additional python environments ("processing" environments), then it is possible for the command above to list the correct extensions but for them to not be available in the JupyterLab front-end you have loaded in your browser. If you have installed additional python environments (or kernels) to use with JupyterLab, or if you are using a centrally hosted JupyterLab installation, you need to make sure that the extensions are installed in the python environment used to launch JupyterLab (the "server" environment). $ jupyter labextension install jupyterlab-plotly You can run the following commands in a terminal to fully remove plotly before installing again: It's often worthwhile to uninstall with both methods before following the Getting Started instructions from scratch with one or the other. It's very important that you not have a file named plotly.py in the same directory as the Python script you're running, and this includes not naming the script itself plotly.py, otherwise importing plotly can fail with mysterious error messages.īeyond this, most import problems or AttributeErrors can be traced back to having multiple versions of plotly installed, for example once with conda and once with pip. Read on for details about troubleshooting plotly in these environments. In general you must also have the correct version of the underlying Plotly.js rendering engine installed, and the way to do that depends on the environment in which you are rendering figures: Dash, Jupyter Lab or Classic Notebook, VSCode etc. This documentation (under ) is compatible with plotly version 4.x but not with version 3.x, for which the documentation is available under. 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.In order to follow the examples in this documentation site, you should have the latest version of plotly installed (5.x), as detailed in the Getting Started guide. 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. 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.ĭockerize Jupyter with the Visual Debugger enabled 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. 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.Ĭontainers enable smoother development across multiple environments.
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