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Jupyterlab – resolve error messages regarding iopub_data_rate_limit

When I worked with some ML-based text evaluation I had run across an output limit rate on the notebooks output rate: iopub_data_rate_limit : (bytes/sec) Maximum rate at which stream output can be sent on iopub before they are limited. I had changed it by a factor of 10 from the standard value in a file ~/.jupyter/jupyter_notebook_config.py in my home-directory on my Linux system. When starting Jupyterlab I get error messages of the kind: ‘iopub_data_rate_limit’ has moved from NotebookApp to ServerApp. This config will be passed… Read More »Jupyterlab – resolve error messages regarding iopub_data_rate_limit

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