As modules are still not GA, no. Going forward, modules from the Hub will be available for open source Redis, Redis Cloud and Redis Labs Enterprise Cluster.
Briefly looking at the paper, it seems what he calls the "theory of the program" is akin to what Fred Brooks called "conceptual integrity". Am I wrong?
I think that's right. The terms 'design' and 'model' are often used for this nowadays. Naur's point is that a program is a shared mental construct that lives in the minds of the people who build it. The source code is not the program. It's the canonical written representation, but a lossy one.
The programmer is unable to completely and unambiguously articulate the "design" in source code and documentation. Yes, the source code can be improved with with longer names of variables and functions in addition to liberal code comments. And documentation can be expanded to include chapters on "architectural overview" and "technical motivations" to help fill the gaps but it will inevitably be incomplete.
We've transitioned our local/dev/prod instances to use conda on Heroku, and couldn't be happier. It was a tiny bit of work to get it set up, but now everything is consistent, and we can set up new local environments in seconds.
So I have been considering this. does conda track pypi or does it lag it? I have been concerned about moving over my requirements.text for a webapp with lots of dependencies
It's also pretty straightforward to set up your own Conda package tree. Nice for packaging your app for deployment or making sure you have very precise dependencies.
I think deployment is a solved problem with docker. Its libraries like blas,etc that are a huge pain. I'm not sure why static linked bumpy is not possible - even anaconda could not achieve it.
If you've ever tried to dive into the NumPy build process you'd see why. It's unbelievably complicated... not that they really could do it better given that they are compiling about a billion scientific libraries and support alternatives and optimizations (like MKL).
Yes - unfortunately I have and I failed miserably.
These days I'm trying to see if there's a docker build that can build a great numpy (with all optimizations). Interestingly there are even docker images to call cuda APIs from python.
We have to use a mix of pypi and conda since quite a few of our dependencies are not in conda. We have a script which checks conda first, then falls back to pypi, all from one requirements.txt
