Data storage and memory

As you produce more notebooks and environments in Anaconda Notebooks, you may begin to run out of storage and find that processes slow down. This topic explains how data storage and memory usage work in Anaconda Notebooks and provides instructions to resolve these issues.

Anaconda Notebooks provides varying levels of cloud storage and CPU “high-compute” seconds based on your Anaconda tier level.

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Data storage

You can monitor your available cloud storage space with the Disk Usage meter at the top of the screen. If you’re maxing out your storage space, the most likely culprits are custom environments, which are all the environments you see in the Disk Manager. To gain more storage, delete these environments (and optionally download them for later use) or upgrade your subscription.

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Managing disk usage

You can view and manage all your files by clicking anywhere on the Disk Usage meter to open the Disk Manager:

1. Clear Cache: Anaconda recommends clearing your cache on a regular basis to save on space.
2. Reset…: Restores all selected items to their default state. Select Download items before deleting to ensure you don’t lose valuable work.
3. Download: Downloads selected files. Consider downloading valuable files before deleting.
4. Delete: Deletes selected files.
5. File Name: Hovering over the File Name column header exposes a filter tool to further sort through your files. Clicking File Name orders (and reverses the order of) files alphabetically.
6. Size: Clicking Size orders (and reverses the order of) files by size.

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Removing custom environments

1. In a terminal within Anaconda Notebooks, run conda env list and see if there are any environments NOT in /opt/conda.

2. If there are, you can remove those unwanted environments in the Disk Manager as shown in the previous section, or by running:

   # Replace <ENV_NAME> with the environment name
   conda env remove --name <ENV_NAME>
   

3. Further, clear out the cache and other artifacts by clicking Clear Cache in the Disk Manager as shown in the previous section, or by running:

   conda clean --all
   pip cache purge
   rm -rf /tmp/*
   

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Memory

If your notebook is running slowly, you may have exceeded your CPU usage limit for the day. You will still be able to work when this happens, but the performance will be affected (for example, loading a .csv file with Pandas may take 10 seconds instead of half a second). The limit resets daily.

Anaconda Notebooks limits processes to 6GB of RAM per kernel. Exceeding this limit terminates your process, at which point you will need to restart your kernel. If you need to run larger processes, please contact us at [email protected].

To see current progress towards your daily quota, refer to the CPU Usage widget at the top of Anaconda Notebooks.

To better manage your CPU usage, regularly check the Running Terminals and Kernels tab in the left sidebar and shut down unnecessary kernels when you no longer need them.