In a previous blog, I talked a bit about the hazards of coding to an implementation and not a specification, based on 1980s home computers. While the specifics and peculiarities of that case is hopefully confined to old hardware, the lessons are still worth contemplating. There is a modern variant of this phenomenon that is based on open-source software, and that I must admit to feeling a bit annoyed by. Fundamentally, the question is this: when figuring out how to use an API – should you look at the documentation or the implementation?
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Windows 10 Reboot Loop – CUDA & Alienware
Late last year I was trying to do some machine learning work on my brand new Alienware 15 R4 gaming laptop. I had bought the laptop in order to have something portable with sufficient performance to actually do convolutional neural network (CNN) training and inference “on the road”. The GTX 1060 in the laptop is just as powerful as my home desktop machine, and should run Tensorflow and Keras well. I had the setup working on the desktop already, and copied the code over to the laptop. When trying to run the code the first time, I got some rather strange errors that I finally figured out meant that I was missing the CUDA toolkit. I downloaded CUDA version 10, installed, and the machine rebooted into the Windows 10 automatic repair mode.
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