If you're a heavy user of the research environment, you've probably seen it "hang" or "crash" at some point. You might even have done something as simple as open all of the lecture notebooks at once! When this "hang" happens it can be incredibly frustrating. We're giving you a new tool to help you understand and prevent most research "hang" problems.
This week we launched a new memory usage meter in the research environment. You now see this meter in the navigation of individual notebooks, and on your notebook list screen. It reflects your research memory usage across all notebooks. This will help you better understand when memory is your limiting factor - and when your research notebook is getting close to "hanging."
If you are running into memory limitations, the first thing to do is shut down any notebooks you aren't using. You can also restart a notebook to clear out the memory of that notebook and start over (run > restart in the notebook navigation).
Memory is a complicated thing to manage in the best of scenarios. Working within Python adds layers of abstraction which can make it hard to manage your memory as effectively as one would like. The goal of this meter is better help you understand when memory is an issue.
The attached notebook is a quick example to help show how the memory meter works. Clone it and execute the cells one at a time.