Bits of py

Unsurprisingly I use some very popular Scientific Python packages like Numpy, Scipy and Scikit Learn. These packages don't get on that well with virtualenv and pip as they take a lot of external dependencies to build. These dependencies can be optional libraries like libblas and libatlas which if present will make Numpy run faster, or required dependencies like a fortran compiler. Back in the good old days you wouldn't pin all your dependency versions down and you'd end up with a precarious mix of apt-get installed and pip installed packages. Working with other developers, especially on different operating system update schedules could be a pain. It was time to update your project when it breaks because of a dependency upgraded by the operating system. Does virtualenv fully solve this? No, not when you have hard requirements on the binaries that must be installed at a system level. Docker being at a lower level gives you much more control without adding too much e

I’ve been branching out doing some 3D modelling at work recently and wanted to share the rendering pipeline I settled on. I began on my laptop Dell XPS 15 (XPS15-6846sLV) , GPU rendering worked straight away from Windows 7, but took some setting up with Archlinux. This setup was pretty good for modeling, but I quickly realized that I needed some more serious heat dissipation for rendering. I then moved to my workstation which has much more CPU power and a dedicated NVS 310 graphics card, not too shabby but sample renders were taking too long. I borrowed a work colleague's GeForce GTX TITAN with 12GB GDDR5 ram - unsurprisingly this beast sped things up considerably. I still had to render more frames than would make sense on one machine - especially in the time I had to work with; so I set up Blender on Amazon Web Services to take advantage of the EC2 instances with GPUs (g2.2xlarge). Taking advantage of favourable spot prices in North Virginia one can run GPU instances for betw