Earlier quoted context omitted.
I remember when Perl was more popular generally than Python (late 90's and early 00's), and Perl espoused the mantra of (There is more than one way to do it: TIMTOWTDI). As a tongue-in-cheek reaction, Python espoused "TOOWTDI", or There's Only One Way To Do It :) reference: https://wiki.python.org/moin/TOOWTDI The problem is, when it comes to the Python package management ecosystem, there are SO MANY ways to do it. A…
I'm relatively new to python. I use venv, pip and requirements.txt. It's dead simple. What am I missing?
However, it was pushy. It would put its own header/linker paths in front of the system paths (this is how it made "environments," its wannabe containers), which tended to create inadvertent cross dependencies if you didn't understand or didn't remember that the semantics of a conda environment extended beyond python. These dependencies could get baked into binaries and break far down the road, or they could get sucked in as a transitive dependency and trip over the shoelaces of a different build of the same software installed outside conda. Unfortunate. However, around 2019, the problems started growing beyond mere foot-guns. Conda uses a full SAT solver to provide a highly featured versioning system, and this worked great until the big conda channels grew to the point that it started getting really slow. Installing packages went from taking seconds to minutes to hours to forever. They tried caching, they tried fragmenting channels, but it was all very not-seamless.
Eventually, people started migrating back to pip. It turns out that over the last decade distro repositories had gotten their shit together and now Docker existed to sweep up the last few use cases, so nobody needed conda's "poor-man's docker plus curated 3rd party repos" anymore. Now pip is the tool that Just Works, and it Just Works without any of conda's baggage. Virtualenv environments don't hook your system quite as aggressively, pip never stalls when resolving its version plans, and Docker can be used to reproducibly experiment and find the happy path.
Be glad that you missed out on pre-conda pip and the conda arc.