Category Archives: Machine Learning

Posts about tools and experiments regarding “machine learning” in a Linux environment

Keras 3/TF vs. PyTorch – small model performance tests on a Nvidia 4060 TI

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There are many PROs and CONs regarding the choice of a Machine Learning [ML] framework for private studies on a Linux Workstations. Two mainly used frameworks are PyTorch and a Keras/Tensorflow combination. One aspect for productive work with ML models certainly is performance. And as I personally do not have TPUs or other advanced chips available, but just a consumer Nvidia 4060 TI graphics card, performance and optimal GPU usage are of major interest – even for the training of relatively small models.

With this post I just want to point out that the question of performance advantages of some framework on a CUDA controlled graphics card can not be answered in a unique way. Even for small neural network [NN] models the performance may depend on a variety of relevant settings, on jit-/xla-compilation and the chosen precision level of your training or inference runs.

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Machine Learning on PCs – Use mixed precision and look out for super-convergence to save energy

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People doing Machine Learning [ML] experiments on their own Linux PCs or laptops know that the numerical training runs put a heavy load on the graphics cards and consume a lot of energy as a direct consequence. Especially in a hot summer like we have it in Germany right now, cooling of your systems may become a problem. And as energy has a high price tag here, any method to reduce the load and/or power consumption is welcome.

But I think that caring about energy consumption is a topic which we as a Linux and ML enthusiasts should keep in mind in general. Some big tech companies will probably not do it – as long as their money machinery works and as some heads follow fantasies about building small nuclear power plants for their big AI data centers. But we Opensource people would like to see more AI- and ML-services independent of the monopolists and their infrastructure, anyway. Not only for reasons of data and privacy protection.

As soon as we, however, proclaim and work for a development that favors local and resource optimized installations of AI and ML tools both for private people and companies, we have to care about side effects: We have to bring the energy consumption down for these many local installations substantially in parallel. Otherwise, centralized solutions may have a better energy efficiency than decentralized solutions.

For me as a retired person in Germany the general financial pressure is high enough to enforce a careful use of my private resources. With this post I want to draw your attention to two points which may help you, too, to save energy during your ML-experiments. (In addition to or aside of standard measures like saving certain model states during training runs to get better starting points for new runs.)

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Opensuse Leap 15.5 – installation of CUDA 12.3 for Machine Learning

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Working with Machine Learning and Deep Neural Networks not only requires GPU drivers, but in case of Nvidia GPUs also the installation of CUDA and cuDNN. This process is always a bit tricky as additional environment variables have to be set for IPython-based Jupyterlab or classic Jupyter Notebook. On an Opensuse system one must in addition take care of the right settings in /etc/alternatives.

I have described the necessary steps in a post at “machine-learning.anracom.com“.

I hope this helps people who want to use Leap 15.5 for Machine Learning with Nvidia GPUs, Keras/Tensorflow 2 and Jupyterlab.

Important addendum 01/27/2024:
Although the combination of CUDA 12.3, cuDNN 8.9.7, Tensorflow 2.15 and Nvidia drivers 545.29.06 works regarding AI-models, there is another major problem:
Nvidia’s driver 545.29.06 is buggy – at least for Leap 15.5, KDE/Plasma with multiple screens. The bug affects Suspend-to-RAM. Suspend-to-RAM seems to work in the suspend phase, and the system also comes up afterward in a seemingly proper state of your KDE/Plasma interface (on your screens).

However, the problems begin when you want to change to another virtual screen via Ctrl-Alt-Fx. You wait and wait and wait … The same for changing the run-level or systemd target state or when you want to shut the system down. This makes Suspend-to-RAM with driver 545.29.06 impossible to use.

Recommendation:
If you have a working older Nvidia driver (e.g. a stable 535 version) do not change to 545.29.06. Unfortunately, it is a mess on a multiscreen Leap 15.5 system to return to an older driver version. The Nvidia community repository does not offer you a choice. (Why by the way ????). Downloading an older proprietary driver from Nvidia and trying to install it afterward on a console terminal (after having stopped X11 or Wayland) did not work in my case – the screens displaying the terminal changed their resolution and froze afterward. So, you may have to completely uninstall the present driver 545 completely, go back to standard VGA and then try to install an older driver via Nvidias install mechanism. As I said: It is a mess …