In a terminal use nvidia-smi, it should not find a gpu (unless you have multiple…) and give some error message In the xserver desktop go to nvidia-settings-gui and change the PRIME Profile to “Intel (Power Saving Mode)” In a terminal use nvidia-smi to check the gpu is recognized correctly, note the memory consumption of up to 400MB just for the xserver GUI. This method works on all machines that have an onboard Intel Graphics chip (which is nearly every computer that uses Intel CPUs), you can use the onboard for the gui and leave 100% of the gpu for calculations which is my intended mode. If you want to have a graphical GUI but still use your nvidia gpu only for DL, that is also possible and the way I have set up my machines (Laptop w/ GPU and Desktop w/ GPU). Maximize GPU Memory availability for Deep Learning but still have GUI (Ubuntu 18.04) And good you post it here, might help other people.Īlong those lines, maybe someone finds this useful too (maybe even you):

Yes okay, that is a “different topic” but glad you solved it. Jeremy has explained that in one of the early videos (part 1, or workshop for part 1 or lesson one of machine learning for coders.) Something like this: How do I tunnel and browse the server webpage on my laptop? The easiest way to do that is probably by using an ssh tunnel and “portforwarding” on your mac. In case you want to then run jupyter notebook and access this from the macbook via the browser, you have to set that up too. This is for ubuntu 16.04 but it should transfer easily In order to use your machine in “server mode” from your macbook, it is not really important whether a desktop is installed or running on your “server” but rather that the right tools for remote access are installed.īasically it means you have to install/enable ssh on the server and then use ssh/scp/sft (or GUI-tools based on that) to access the server, like you would an aws instance.

I am not sure I fully understand your question.

Ubuntu system monitor headless