![]() Installing packages is only half the problem, being able to isolate project dependencies is the other half. Not so many years ago getting a decent development setup on Windows was a total nightmare, and Anaconda solved that problem. Many libraries such as numpy have dependencies that go much beyond what the default Python installer can take care of. This removes a lot of duplication (programs shipping dependencies) and makes the system easier to maintain (a single source of truth for versions and requirements).Īnaconda brought the idea of a package manager to windows, focusing on scientific/engineering Python developers. A package manager is a tool that allows to install programs AND their dependencies. ![]() The next steps should be executed from your new terminal, except where otherwise specified.Ĭreate a new Python virtual environment by running the following command.What is Anaconda and the conda package managerĪnaconda is a full-fledged package manager that works on most operating systems.įor people coming from a Linux background, this is not novelty, but for Mac and Windows users, package managers are unknown. Setting Up a Virtual Environment Using Condaįollow these steps from your Jupyter service to create a Python virtual environment using Conda:Ĭreate a new terminal by selecting the New Launcher option ( + icon) from the top action toolbar in the left sidebar, and then selecting Terminal from the main work area. This means that PIP installations persist even after restarting the Jupyter pod.Īny new Conda environment that is created or cloned will be fully located in the data mount directory of the running user, specifically at /User/.conda/. ![]() However, when these Conda environments are activated, the PIP_PREFIX and PYTHONPATH environment variables are automatically set to the data mount directory of the running user: /User/.pythonlibs/. They are reset when the Jupyter service is restarted. While the platform provides several preconfigured Conda environments that are included in the Jupyter pod, it's important to note that any Conda installations to these environments are not persistent. ![]()
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