1/23/2024 0 Comments Jupyterlab dependenciesIn this way, all the dependencies information required to repeat the environment are shipped with the notebook. You can use this extension for each of your notebook to guarantee they have the correct dependencies files required for reproducibility and shareability. This plugin is provided from this extension. If you have too many kernels, you can remove them directly from the JupyterLab menu under Kernel Section. If you want to see a practical example on the use of overlays and how to create them from your notebook, you can check this tutorial. If you want to know more about the use of overlays, have a look micropipenv. The dependencies stored in the notebook metadata can be also stored into overlays folder using the kernel name by default. The virtual environment created and assigned to the kernel to be used for your notebook according to your dependencies requirements can be checked using the following command from a terminal:Ĭat ~/.local/share/thoth/kernels/ Overlays directory Once lock file is created using any of available resolution engines, the dependencies will be installed in the virtualenv using NOTE: Those parameters are autodiscovered by the extension and assigned to your environment, you can customize them if you are interested. You can select the runtime environment to be used for the recommendation selecting: more performance), therefore we include these information in the notebook metadata so that other can find out what runtime environment has been used to run a certain notebook. ![]() In general different runtime environment will provide different effect on you application (e.g. Thoth resolution engine is able to provide an optimized software stack based on the runtime environment you are using (several inputs are used, if you want to know more, have a look here here). You can find more information and updates here. You can choose the type of recommendation that better fits your needs: Using the Thoth resolution engine you can request an optimized software stack that satisfies your requirements. NOTE: Currently this extension supports only Python kernels. NOTE: Thoth is used by default and Pipenv can be triggered with flags or run as backup automatically. There are currently two resolution engines available in the extension: jupyterlab-requirements UI accessible through Manage Dependencies button that appears in the notebook when it is opened in JupyteLab.horus CLI from terminal or integrated in pipelines. ![]() %horus magic commands directly in your notebook's cells.There are 3 ways to interact with this extension:
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