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Dev tools / equipment

TF 2.16, Keras 3, alternatives for deprecated ImageDataGenerator

These days I started again to work with ResNets and images. To improve accuracy e.g. for classification projects the test on evaluation datasets is the only thing that counts regarding accuracy. One should use some form of data augmentation, best statistically during training, to prevent overfitting of a model. In the past I have often used Keras’ ImageDataGenerator. ImageDatagenerator is “deprecated” in Keras 3 DataImageGenerator was an easy to use tool. With Keras 3 it is now classified “deprecated”. I do not regard this as… Read More »TF 2.16, Keras 3, alternatives for deprecated ImageDataGenerator

Setting NUMA node to 0 for Nvidia cards on standard Linux PCs

People working on Linux PCs with Tensorflow 2 [TF2] and CUDA may be confronted with warnings complaining a lack of an assignment of their Nvidia graphics card to a NUMA node. This is somewhat enervating as depending on the TF2 version a default entry of “-1” for the NUMA on consumer systems may clatter some of your Jupyter notebook cells with warnings. In this post I first will briefly turn to the question what NUMA is good for on sever systems with multiple CPUs and… Read More »Setting NUMA node to 0 for Nvidia cards on standard Linux PCs

Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning

Machine Learning on a Linux system is no fun without a GPU and its parallel processing capabilities. On a system with a Nvidia card you need basic Nvidia drivers and additional libraries for optimal support of Deep Neural Networks and Linear Algebra operations on the GPU sub-processors. E.g., Keras and Tensorflow 2 [TF2] use CUDA and cuDNN-libraries on your Nvidia GPU. Basic information can be found here: This means that you must not only perform an installation of (proprietary) Nvidia drivers, but also of CUDA… Read More »Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning

Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads

As you read this post you are probably interested in Machine Learning [ML] and hopefully in Linux systems as a ML-platform as well. This post series wants to guide you over a bridge between the standard tool-set of Python3 notebooks in Jupyterlab for the control of ML-algorithms and graphical Qt-applications on your Linux desktop. The objective is to become more independent of some limitations of the browser based Jupyterlab notebooks. One aspect is the use of graphical Qt-based control elements (as e.g. buttons, etc.) in… Read More »Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads