![]() ![]() While the setup of the eGPU on Windows is literally plug & play, there is much more to do on Ubuntu 17.10. No official support and only very few resources for most eGPU systems on Linux.High GPU prices due to crypto-currency mining.Of course, there were two big bullet points which made me hesitate some more days: And that’s why I thought: well, then do it yourself! Most or even all of them are focused on pure gaming. There was basically no benchmark available regarding eGPU setups for Machine Learning. However, this is a dual-GPU and you only get access to one of them, so the performance is actually worse than it looks like on most benchmarks.Īfter checking out some of these cloud platforms, I was still curious about how an eGPU performs with TensorFlow. The free version comes with 10 hours of GPU access on a Nvidia Tesla K80. Last but not least, I checked out FloydHub, which actually worked quite well. Unfortunately, the offer was limited to a 8-core CPU instance, while no GPU instance was available. Next, I checkout out the 300$ free credit on Google compute engine. This means that you have to pay for a full hour, even when you just run a simple example for 1 minute. I did not try out a GPU-enabled instance on AWS, because the use a billing based on a hourly rate. That’s why I tried to get access to a high-performance graphics card in order to be able to train non-trivial networks and so some more serious research.Īt first, I had a look at some offers in the cloud. And it’s well known that taking advantage of a GPU boosts training time by a huge margin. But everything faded into obscurity because I almost lost full interest into gaming the last years.īut this changed, since I’m spending a lot of time in deep learning since about two years. The symbiosis of having a light weight laptop at university or on the go, but still having a desktop like power horse when having some spare-time at home sounded like a dream. Other than my laptop, I have a desktop with an NVIDIA 970, which works very smoothly under 18.04, including EGPU and cuda.I remember when I read about eGPUs for the first time. So in summary, Nouveau makes my laptop with EGPU freeze installation of nvidia-390 from the bionic repo does not load the driver properly the installation of nvidia-390 through the run file seems alright but gives me the login loop. Trangely, most people don't see the wayland option when they have installed NVIDIA driver, but I can see both the wayland and xorg options in all circumstances. This time nvidia-smi showed that the driver was loaded correctly, but I had the login loop for the xorg. Finally, I installed NVIDIA-390 via the run file. Moreovoer, nvidia-smi showed that the driver was not loaded. I could logged in via xorg, but the nouveau was loaded insteaf of NVIDIA. However, when I connected the GPU dock to the laptop, the error PKCS#7 appeared continuously. The installation seemed fine, and did not report any error. Secondly, I installed the NVIDIA-390 driver directly through the Bionic repo. Firstly, when I used nouveau, the desktop simply froze after I logged in. ![]() However, with 18.04, things have been difficult. With 16.04, everything is working smoothly: I connect the GPU dock to the laptop via the thunderbolt 3 connector and use the prime-select tool to select NVIDIA. I have a razer core at home and an Omen Accelerator in my office and I have a GTX 970 and a GTX 1080ti. I don't travel much, so I use EGPU to gain more GPU power all the time. I have been using 16.04, and installed 18.04 recently. ![]()
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