This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
With an increasing number of different projects, involving different machine learning models, it's becoming difficult to manage different package versions across workflows. Currently, the Python tool has a single virtual environment, so we need to develop models in different projects always using the same Python and package versions as the Python tool venv. While this doesn't bother the code itself too much, it becomes a problem as soon as we store and load pickled models, which are sensitive to even minor changes in packages.
This is even more so a problem when we are working on the Alteryx server, where different teams might use different packages. Currently, there is only the server admin who can install packages on the server and there can only be one version per package.
So, a more robust venv management in the Python tool would be much appreciated!