Hi there,

I'm using a Python 3.6 kernel in my Jupyter Notebook running on 2 CPUs and 8GB of RAM. At a certain point when I execute the cells the kernel always dies. I'm aware this might be due to memory shortage, so I freed the memory of all unneeded dataframes and maps, etc. I'm then able to get past that point, however eventually the kernel dies again a couple of cells later at a for my understanding fairly easy computation, where I'm just dropping two columns from a dataframe, renaming another and counting NA values in one column.

The whole damn thing ran a couple of days before with no issues (I didn't even had to free any memory) but I'm just not able to execute it since yesterday.

Any suggestions on how to fix this? Did anyone had a similar problem? Please reply.

Thanks.

Sebastian

------------------------------
Sebastian
------------------------------

#WatsonStudio

Hey Sebastian, a best practice we always advise is when you're not actively working in a notebook, click File > Stop Kernel to free up resources.  If you try to reconnect to the kernel and immediately run a code cell (or if the kernel reconnection happened during code execution), the notebook doesn't reconnect to the kernel and no output is displayed for the code cell. You need to manually reconnect to the kernel by clicking Kernel > Reconnect. When the kernel is ready, you can try running the code cell again.

For further information about known issues visit here: https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/known-issues.html?context=analytics


#WatsonStudio