I've been heavily refactoring code of my machine learning project. Now I can just watch how GPUs are fully loaded, while the CPUs are not that much. Can't wait for first validation results. It's a slightly modified model which takes images of different sizes. I don't know yet wether it gives better results, but it definitely heats up the room.
Then I relaised, that resising images on the fly might be the main reason of the slow down. This idea came to me on Friday afternoon. The end of the day was intense, but resizing images once and staving the sizes I need improved the speed. Still, the GPU is not 100% busy. Now I'm looking for Monday, to see the results!
At that point TemsorFlow was complaining that the CPU supports instructions that the binary was not built with. Before building TF from source, I decided to update cuda and other Nvidia software. After a dance following outdated tutorials, I got it done. Speed improved, but still, while CPUs were at 100%, GPUs were barely loaded.
As an experiment to use Spotify less often, I'll give somafm.com a try as the default radio in my car.
A big plus is that I won't listen to the same music over and over again. The only worry I have is that I'll pay for the data about what I'm paying right now for Spotify.
It's also a good time to donate to somafm!
Computer science, computational linguistics, running, swimming, photography.
A beta setup of a Mastodon instance primary for family and friends.