I've generally had the same experience, although that particular toolchain only solves part of the deployment problem. The other caveat is that you will eventua

I've generally had the same experience, although that particular toolchain only solves part of the deployment problem. The other caveat is that you will eventually run into a situation where, say, pip doesn't quite make it to the finish line, and you're left wondering why. If my memory serves me, the last time this happened to me was while I tried to install a project that used ipython (=zmq+readline+pyglets+..+..+) and needed a numpy that was properly linked to the intel MKL. It failed at the 'pip level' but I didn't arrive at a solution wasn't clear until I dove much deeper (python build, MKL build, ZMQ build, setuptools interactions, autoreconf incantations, etc.) There are definitely times when it pays to understand things like the differences betweening pipping and easy_installing. However, when dealing with projects that are closer to pure python, and/or less complicated, workon proj && pip install < requirements.txt has almost never failed me. More and more I find myself moving to Ansible. So good.