51–60 of 185 posts
The big obstacle is that for while we would have multiple execution environments. It’s not like we could flip a switch and all Dockerfiles are using PyPy.
Plus I don’t think AWS Lambda supports it.
If I could go back in time, we would use it from the beginning.
That said, if I do ever run into a situation where I need my code to perform better, PyPy is high on my list of things to try. It’s nice to know it’s an option.
I use it at work for a script that parses and analyzes some log files in an unusual format. Wrote a naive parser with a parsing combinator library. It was too slow to be usable with CPython. Tried PyPy and got a 50x speed increase (yes, 50 times faster). Very happy with the results, actually =)
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Yes 1000x What is it with them which makes them feel entitled to have special "dist-packages" vs "site-packages" as is the default? This drives me nuts, when I have a bunch of native packages I want to bundle in our in-house python deployment. CentOS and Ubuntu are vanilla, and only Debian (mind-boggingly) deviates from the well-trodden path. I still haven't figured out how to beat this dragon. All suggestions welcom…
I usually make a venv in ~/.venv and then activate it at the top of any python project. Makes it much easier to deal with dependencies when they're all in one place.
if you need extra dependencies that pip can not do well in the .venv case, Conda can help with its own and similar site-based installation.
I don't know how it is different in the python installation case between ubuntu and debian, they seem the same to me.
Earlier quoted context omitted.
Yes 1000x What is it with them which makes them feel entitled to have special "dist-packages" vs "site-packages" as is the default? This drives me nuts, when I have a bunch of native packages I want to bundle in our in-house python deployment. CentOS and Ubuntu are vanilla, and only Debian (mind-boggingly) deviates from the well-trodden path. I still haven't figured out how to beat this dragon. All suggestions welcom…
I usually make a venv in ~/.venv and then activate it at the top of any python project. Makes it much easier to deal with dependencies when they're all in one place.
Python is fun to work with (except classes…), but its just sooo slow. Pypy can be a life saver.
[1] https://blog.transitapp.com/how-we-shrank-our-trip-planner-t... [2] https://blog.transitapp.com/how-we-built-the-worlds-pretties...
If it was relatively up to date with Python3 I'd use it, but as it lags behind considerably I avoid it, even for personal work.
Python 3.10 is too old for your work?
There is still the lag though, Python 3.10 was out for quite a while before PyPy supported 3.10.
Hey, you might want to delete the link to https://mesapy.org/rpython-by-example in https://doc.pypy.org/en/latest/architecture.html as it is pointing to a resource that people are unable to access.