As Deep learning is becoming more and more popular, there is an ongoing debate on whether it’s possible to create Deep Learning applications with a Free Software license. See for example this discussion on the debian-devel mailing list.
The argument we often see is that:
It’s impossible to study the inner workings of a Deep Learning software (for example, an image classifier or a text generator) or improve it, because one cannot understand how it’s going to make predictions only by looking at the weights of the Deep Learning model Training a Deep Learning model requires a specialized and expensive hardware that runs non-Free software But the first statement misses the point of Deep Learning programs.
This procedure has been tested on Fedora 28, on a HP laptop with this graphical card: NVIDIA Corporation GP107M GeForce GTX 1050 Mobile (rev a1).
The commands have to be run as the root user. This tutorial assumes the nvidia driver is already working.
Install pip dnf install python3-pip Install Cuda We install cuda 9.0 as it is the latest version supported by tensorflow at the time of writing.
If we have to choose between a convenient system and a secure one, we often pick the former rather than the latter. The reason is mainly psychological. Several scientific studies have shown that we prefer instant gratification over delayed gratification, because that’s how our brains are wired. We are surrounded by instant gratification, our day-to-day actions like our hobbies, usage of social media, got us hooked on having a quick feedback.
We should not think that programming is complicated. It is often the easiest part of an IT project, because one simply needs to communicate with a computer. The communication between human beings is far more complicated. Computers are the most predictable things in the universe. Humans are unpredictable by nature. They can lie, change mood or theirs opinions multiple times, decline an offer because they had a bad day or due to the weather, or the horoscope, you name it.