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

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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Opensuse Leap 15.5 – installation of CUDA 12.3 for Machine Learning

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 …

 

Blender – even on old laptops a graphics card increases rendering performance

My present experiments with Blender on my old laptop take considerable time to render- especially animations. So, I got interested in whether rendering on the laptop’s old Nvidia card, a GT 645M, would make a difference in comparison to rendering on the available 8 hyperthreaded cores of the CPU. The laptop’s CPU is an old one, too, namely an i7-3632QM. The laptop’s operative system is Opensuse Leap 15.3. The system uses Optimus technology. To switch between the Nvidia card and the Intel graphics I invoke Suse’s Prime Select application on KDE.

I got a factor of 2 up to 5.2 faster rendering on the GPU in comparison to the CPU. The difference depends on multiple factors. The number of CPU cores used is an important one.

How to activate GPU rendering in Blender?

Basically three things are required: (1) A working recent Nvidia driver (with compute components) for your graphics card. (2) A certain setting in Blender’s preferences. (3) A setting for the Cycles renderer.

Regarding the CUDA toolkit I quote from Blender’s documentation

Normally users do not need to install the CUDA toolkit as Blender comes with precompiled kernels.

With respect to required Blender settings one has to choose a CUDA capable device via the menu point “Preferences >> System”:

You may also select both the GPU and the CPU. Then rendering will be done both on the GPU and the CPU. My graphics card unfortunately only understands a low level of CUDA instructions. The Nvidia driver I used is of version 470.103.01, installed via Opensuse’s Nvidia community repository:

In addition, you must set an option for the Cycles renderer:

With all these settings I got a factor of 2 up to > 6 faster rendering on the GPU in comparison to a CPU with multiple cores.

The difference in performance, of course, depends on

  • the number of threads used on the CPU with 8 (hyperthreaded) cores available to the Linux OS
  • tiling – more precisely the “tile size” – in case of the GPU and the CPU

All other render options with the exception of “Fast G” were kept constant during the experiments.

Scene Setup

To give the Blender’s Cylces renderer something to do I set up a scene with the following elements:

  • a mountain-like landscape (via the A.N.T Landscape Add-On) with a sub-dividion of 256 to 128 – plus subdivision modifier (Catmull-Clark, render level 2, limit surface quality 3) – plus simple procedural texture with some noise and bumps
  • a plane with an “ocean” modifier (no repetition, waves + noisy bump texture for the normal to simulate waves)
  • a world with a sky texture of the Nishita type ( blue sky by much oxygen, some dust and a sun just above the horizon)

The scene looked like

The central red rectangle marks the camera perspective and the area to be rendered. With 80 samples and a resolution of 1200×600 we get:

The hardest part for the renderer is the reflection on the water (Ocean with wave and texture). Also the “landscape” requires some time. The Nishita world (i.e. the sky with the sun), however, is rendered pretty fast.

Required time for rendering on multiple CPU cores

I used 40 samples to render – no denoising, progressive multi-jitter, 0 minimum bounces.
Other settings can be found here:


The number of threads, the tile size and the use of the Fast CI approximation were varied.
The resolution was chosen to be 1200×600 px.

All data below were measured on a flatpak installation of Blender 3.1.2 on Opensuse Leap 15.3.

tile size threads Fast GI time
64 2 no 82.24
128 2 no 81.13
256 2 no 81.01
32 4 no 45.63
64 4 no 43.73
128 4 no 43.47
256 4 no 43.21
512 4 no 44.06
128 8 no 31.25
256 8 no 31.04
256 8 yes 26.52
512 8 no 31.22

A tile size of 256×256 seems to provide an optimum regarding rendering performance. In my experience this depends heavily on the scene and the chosen image resolution.

“Fast GI” gives you a slight, but noticeable improvement. The differences in the rendered picture could only be seen in relatively tiny details of my special test case. It may be different for other scenes and illumination.

Note: With 8 CPU cores activated my laptop was stressed regarding CPU temperature: It went up to 81° Celsius.

Required time for rendering on the mobile GPU

Below are the time consumption data for rendering on the mobile Nvidia GPU 645M:

tile size Fast GI time
64 no 18.3
128 no 16.47
256 no 15.56
512 no 15.41
1024 no 15.39
1200 no 15.21
1200 yes 12.80

Bigger tile sizes improve the GPU rendering performance! This may be different for rendering on a CPU, especially for small scenes. There you have to find an optimum for the tile size. Again, we see an effect of Fast GI.

Note: The temperature of the mobile graphics card never rose above 58° Celsius. I measured this whilst rendering a much bigger image of 4800×2400 px. I therefore think that the temperature stress Blender rendering exerts on the GPU is relatively smaller in comparison to the heat stress on a CPU.

Required time for rendering both on the CUDA capable mobile GPU and the CPU

As the CPU is CUDA capable one can activate CUDA based rendering on the CPU in addition to the GPU in the “preferences” settings. With 4 CPU cores this brings you down to around 11 secs, with 8 cores down to 10 secs.

tile size threads Fast GI time
64 4 no 11.01
128 8 no 10.08

Conclusion

Even on an old laptop with Optimus technology it is worthwhile to use a CUDA capable Nvidia graphics card for Cycles based rendering in Blender experiments. The rise in temperature was relatively low in my case. The gain in performance may range from a factor 2 to 5 depending on how many CPU cores you can invoke without overheating your laptop.

Ceterum censeo: The worst living fascist and war criminal today, who must be isolated, denazified and imprisoned, is the Putler.