Hi @jax79sg ,
Whenever you create a new python code environment in DSS it's placed in
so to upgrade pip you'd need to run
DATA_DIR/code-envs/python/ENV_NAME/bin/pip install --upgrade pip
Are there any plans from Dataiku to deal with this issue in a more universal way?
As background, I think that I have something like 14 DSS Python code environments. With at least 2 Design_MANAGED the rest PLUGIN_MANAGED. And then another small handful of R code environments.
What are the positive and potentially negative ramifications of going to each of these directories and running?
Do I have the chance to break a plug-in doing this?
It's unlikely that upgrading pip will cause issues with the plugins.
However, changing versions of the libraries in those environments is more risky. For this reason plugins come with it's own requirements.txt that contains a list of library versions that will work with a given version of the plugin.
Is there a particular reason why you'd want upgrade pip in all of your environments?
Good to know. With my IT operations hat on we like to keep utilities up to date to generally avoid bugs an vulnerabilities.
I’m taking from your comment that may not be a good idea in this case. That’s why I’m extending this conversation to get a bit of clarity.
Related, I think I’ve seen errors recently when working with older existing code environments in dss where pip has thrown out an error and specifically called out the need to update pip. I don’t remember if this error caused the build of the environment not to complete successfully.
So if I am remembering correctly then I guess this would be about making sure that rebuilds run smoothly. I’m not in a place to test at the moment.
Has anyone else seen things around the version of pip related to dss?