| Python runtime deployment is a major pain point for us (CS department at a university). On the most tightly managed lab machines, which are all in lockstep on a fixed configuration (latest Ubuntu LTS with a updated image pushed annually), we can provide a consistent Python setup (e.g. Python 3.10 and a fixed set of C-based modules like psycopg2). However, our staff and PhD desktops and laptops are more diverse - with the OS often only being upgraded when the distro is going out of support, they could be running n, n-1 or n-2. That, most likely, means three different Python versions. We could use pyenv to let people install their own preferred version. Installing with pyenv requires building from source (slow on some of our oldest machines). This also means installing the Python build deps, which is fine for our departmental machines but not possible on the HPC cluster (operated by a different business unit) or the Physics shared servers. It's also less than ideal for our servers where students deploy their projects (where we want to minimise the packages installed, like the build-essentials meta package). It's also a massive stumbling block for less experienced students with their own laptops which could be running any distro, of any age. Many CS101 or engineering/business/humanities students taking a programming class, who have never programmed before, would really struggle. So, classes might tend towards teaching lowest common denominator Python (i.e. the oldest conceivable version a student might have installed on their machine). Sure, we have in-person and remote lab machines students can use - but it's not always convenient (especially for the data science / ML students running Jupyter notebooks with their own GPU). There are workarounds, but they all have serious downsides. Compared with Node.js and Go, where users can just download the appropriate package and unzip/untar the runtime or compiler version of their choice, deploying the Python runtime has enormous friction (especially for less experienced users). This has the bonus of simplifying deployments elsewhere in our infrastructure (CI/CD, containers, etc). And while we all complain about node_modules, Python venvs not being trivially relocatable is another huge frustration. We've used Anaconda, but that comes with its own issues (most recently, finding that it ships with its own gio/gvfs binaries and libraries which fail to mount our CIFS DFS shares - causing confusion for users running `gio mount` from within a conda environment). |