Install a comprehensive Python and GPU powered deep learning suite. The Nvidia RTX tensor cores can accelerate deep learning tasks by up to 25x (2,500%) versus other popular developer laptops.
kfocus-001-condaPlease read the disclaimer before proceeding. This solution is updated regularly. Please write authorship with suggestions or requests.
The Focus ships with CUDA, TensorRT, cuDNN, OpenGL, and Vulkan libraries pre-installed. This enables sophisticated compute and gaming out of the box. However, adding repositories or packages can alter and break the libraries. You may check and reset them as shown below:
kfocus-001-condaUse kfocus-001-conda to install an Anaconda suite. This is professionally vetted, comprehensive, and GPU accelerated. Open a terminal and enter the following:
Follow the prompts. You will be asked to install the following environments:
(GPU) Jupyter Notebook(GPU) MXNet(GPU) PyTorch(GPU) TensorFlow(GPU) TheanoThe script 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, start the Nvidia Settings app and then select Prime Profiles > Nvidia On-Demand and reboot. This 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 November 2020.
| RTX 2080 Super[1] | RTX 2070[1] | RTX 2060[1] | Titan X[2] | Titan XP W10[2] | Titan XP Ubuntu[2] | |
|---|---|---|---|---|---|---|
| Inference | 9,626 | 8,881 | 7,785 | 7,443 | 9,741 | 11,948 |
| Training | 9,767 | 9,295 | 8,671 | 7,908 | 10,375 | 12,922 |
| AI Score | 19,393 | 18,176 | 16,878 | 15,351 | 20,089 | 24,870 |
[1] Kubuntu Focus 2.2Kg, 20mm thick
[2] 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.
This is a partial revision history. See the git repository for all entries.
2020-09-28 5d089610 Update to M2 scores2020-06-10 c4ed9299 Restructure layout2020-05-25 581c6523 Expand benchmarks2020-05-20 d00295d7 Add Library check2020-05-18 8c4b5b82 Add GPU library fix2020-05-15 0684cd5c Add benchmark2020-02-18 a4d437c5 Add conda script2020-01-31 70b4aa40 Initial documentWe 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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