Install a comprehensive Python and GPU powered deep learning suite. The NVIDIA RTX tensor cores can accelerate deep learning tasks by up to 30x (3,000%) versus other popular developer laptops. This solution only applies to the Kubuntu Focus M1 and M2. The Focus XE does not have the required Nvidia hardware.
The Focus XE ships with OpenGL and Vulkan libraries and usually just works for gaming. The Focus M2 ships with CUDA, TensorRT, cuDNN, OpenGL, and Vulkan libraries installed. This enables sophisticated compute and gaming. However, adding repositories or packages can alter and break the libraries. You may check and reset them as shown in the code block.
Run the app from
App Menu > Kubuntu Focus > Anaconda GPU AI Suite Installer, or open a terminal and enter
kfocus-001-conda. This installs an
Anaconda suite that is professionally vetted, comprehensive, and GPU accelerated. You will be prompted to optionally install the following environments:
kfocus-001-conda will not overwrite or delete data unless you tell it to do so. You may run it again to add environments as needed. For example, you may install only
TensorFlow at first and then add
PyTorch later. Run the AI suite at the end to compare to the results shown in the Benchmarks section.
When you wish to build on these Conda environments, we recommend using Pycharm. You may install this with the JetBrains Toolbox installed on all Kubuntu Focus laptops.
When running Deep Learning workloads, you may notice that the software reports less available RAM than the GPU actually has. Part of this is because of driver overhead, but also because the GPU uses some memory for your display. One can instead use the Intel GPU for the display and use the NVIDIA GPU for only Deep Learning or other computing tasks. To do this, use the GPU widget in the tool tray or
NVIDIA Settings > Prime Profiles to select
NVIDIA On-Demand and reboot. This mode, also called Hybrid Graphics Mode, does not significantly improve battery life, so we recommend using
NVIDIA (Performance mode) or
Intel (Power Saving Mode) as it suits your needs at other times.
You may run comprehensive TensorFlow benchmarks at the end of the
kfocus-001-conda script. Please compare to the official results. All tests are current as of March 2021.
|RTX 2080 Super||RTX 2070||RTX 2060||Titan X||Titan XP W10||Titan XP Ubuntu|
 Kubuntu Focus 2.2Kg, 20mm thick
 Unconstrained Desktop GPU
Q: Starting NVIDIA Visual Profiler results in an error. How can I fix this?
A: One must install Java 8 and then call
nvvp with the correct path.
Q: I booted my Kubuntu Focus, and it never got past the splash screen. How do I fix this?
NVIDIA Settings and other applications may ask you to save an
xorg.conf file. Doing so may disable your internal display from functioning with a GUI upon logout or restart, and you will not be able to sign-in. If this happens, we recommend you remove this file:
sudo /bin/rm /etc/X11/xorg.conf, type your password when prompted.
sudo systemctl restart sddm && exit.
You should be able to sign-in normally.
This is a partial revision history. See the
git repository for all entries.
2021-10-10 5728326eReformat to 2-column
2021-09-22 dc862884Update link and headline colors
2021-08-23 681261b4Review and update codeblocks
2021-08-19 b994dd79Add keywords for hybrid graphics mode
2021-03-07 e3c1285dRevise conda launch guide, update styles
2020-09-28 5d089610Update to M2 scores
2020-06-10 c4ed9299Restructure layout
2020-05-25 581c6523Expand benchmarks
2020-05-20 d00295d7Add Library check
2020-05-18 8c4b5b82Add GPU library fix
2020-05-15 0684cd5cAdd benchmark
2020-02-18 a4d437c5Add conda script
2020-01-31 70b4aa40First publication
We try hard to provide a useful solution validated by professionals. However, we cannot anticipate every situation, and therefore cannot guarantee this procedure will work for your needs. Always backup your data and test the solution to determine the correct procedure for you.
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