Memory Usage # def memory(): with open('/proc/meminfo', 'r') as mem: ret = {} tmp = 0 for i in mem: sline = i.split() if str(sline[0]) == 'MemTotal:': ret['total'] = int(sline[1]) elif str(sline[0]) in ('MemFree:', 'Buffers:', 'Cached:'): tmp += int(sline[1]) ret['free'] = tmp ret['used'] = int(ret['total']) - int(ret['free']) return ret No Hang Up # nohup jupyter notebook --no-browser > notebook.log 2>&1 & Workaround: no cells output # se = time. ...
1 pip uninstall plotly 2 jupyter labextension uninstall @jupyterlab/plotly-extension 3 jupyter labextension uninstall jupyterlab-plotly 4 jupyter labextension uninstall plotlywidget 5 jupyter labextension update --all 6 pip install plotly==5.17.0 7 pip install "jupyterlab>=3" "ipywidgets>=7.6" 8 pip install jupyter-dash 9 jupyter labextension list Useful Links # What is Right extension for Plotly in JupyterLab? https://stackoverflow.com/questions/62604893/what-is-right-extension-for-plotly-in-jupyterlab https://jupyter-docker-stacks.readthedocs.io/en/latest/ https://github.com/jupyter/docker-stacks https://github.com/plotly/plotly.py
Thanks to the Jupyter community, it’s now much easier to run PySpark on Jupyter using Docker. There are two ways you can do this : 1. the “direct” way and 2. the customized way. The “direct” way # verify your local settings are aligned with the pre-requisites to run this container, grosso modo make sure docker is installed, of course ! You have to have about 4 GB of free space pull image from docker hub https://hub. ...