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Jupyterlab, matplotlib, dynamic plots – II – external Qt-windows and figure updates from foreground jobs

The work on this post series has been delayed a bit. One of my objectives was to use background jobs to directly redraw or to at least trigger a redrawing of Matplotlib figures with backends like Qt5Agg. By using background jobs I wanted to circumvent a blocking of code execution in other further Juypter notebook cells. This would to e.g. perform data analysis tasks in the foreground whilst long running Python jobs are executed in the background (e.g. jobs for training a ML-algorithm). This challenge… Read More »Jupyterlab, matplotlib, dynamic plots – II – external Qt-windows and figure updates from foreground jobs

Jupyterlab, Python3, asyncio – asynchronous tasks in a notebook background thread

Jupyterlab and IPython are always good for some surprises. Things that work in a standard Python task in Eclipse or at the prompt of a Linux shell may not work in a Python notebook within a Jupyterlab environment. One example where things behave a bit differently in Jupyterlab are asynchronous tasks. This post is about starting and stopping asynchronous tasks via the Python3 package “asyncio” in a Jupyterlab notebook. In addition we do not want to block the usage of further notebook cells despite long… Read More »Jupyterlab, Python3, asyncio – asynchronous tasks in a notebook background thread

Jupyterlab, matplotlib, dynamic plots – I – relevant backends

When we work with Deep Neural Networks on limited HW-resources we must get an overview over CPU- and VRAM-consumption, training progress, change of metrical variables of our network models, etc. Most of us will probably want to see the development of our system- and model-related variables in a graphical way. All of this requires dynamic plots, which are updated periodically and thus display monitored data live. As non-professionals we probably use Python code in standalone Jupyter notebooks or (multiple) Python notebooks in a common Jupyterlab… Read More »Jupyterlab, matplotlib, dynamic plots – I – relevant backends

ResNet basics – II – ResNet V2 architecture

In the 1st post of this series ResNet basics – I – problems of standard CNNs I gave an overview over building blocks of standard Convolutional Neural Networks [CNN]. I also briefly discussed some problems that come up when we try to build really deep networks with multiple stacks of convolutional layers [e.g. Conv1D- or Conv2D-layers of the Keras framework]. In this 2nd post I discuss the core elements of so called deep Residual Networks [ResNets]. ResNets have been published in multiple versions. The versions… Read More »ResNet basics – II – ResNet V2 architecture