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Dev tools / equipment

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

Preliminary test of a Nvidia RTX 4060 TI 16GB with neural networks

Recently I had the opportunity to test a Nvidia RTX 4060 TI (vendor: MSI, model:Ventus ) on my Linux system against a Geforce GTX 960. For private consumers as me who are not interested in gaming, but in Machine Learning [ML] this type of card can be interesting. I name three reasons: Some of the readers of this blog may miss a criterion like “performance“. The reason is that I regard the VRAM criterion as more important as raw GPU power. I have commented on… Read More »Preliminary test of a Nvidia RTX 4060 TI 16GB with neural networks