I want to do machine learning without the cloud, which as we learn previously, is awful.
So, let’s buy a Razer Blade 2016.
I don’t want to do anything fancy here, just process a few gigabytes of MP3 data. My data is stored in the AARNET owncloud server. It’s all quite simple, but the algorithm is just too slow without a GPU and I don’t have a GPU machine I can leave running. I’ve developed it in keras v1.2.2, which depends on tensorflow 1.0.
So installing CUDA etc on the laptop is straightforward. Making it run is not.
Daniel Teichmann’s Bumblebee instructions.
NVVIDIA’s howto points out that you will need to care about Bumblebee to survive Ubuntu.
Confusing, because most sources seem to think you want to have NVIDIA graphics; but what if you merely want NVIDIA computing via CUDA, and don’t care about graphics?
In stark contrast to NVIDIA, they claim:
There is sometimes confusion about CUDA. You don’t need Bumblebee to run CUDA. Follow the How-to to get CUDA working under Ubuntu.
Lies! without Bumblebee the NVIDIA gets switched off.
Their second point is interesting though
There is however a new feature (--no-xorg option for optirun) in Bumblebee 3.2, which makes it possible to run CUDA / OpenCL applications that does not need the graphics rendering capabilities.
Bumblebee is its own small world. There is a walkthrough for a Razer Blade. It has a debugging page, which you will need.
sudo modprobe nvidia-uvm nvidia primusrun python -i jobs/job_fit_spectrogram.py primusrun nvidia-smi