Below you’ll find frequently asked questions, performance tips, and other topics that don’t really fit in to the rest of the SatPy documentation.

If you have any other questions that aren’t answered here feel free to make an issue on GitHub or talk to us on the Slack team or mailing list. See the contributing documentation for more information.

Why is SatPy slow on my powerful machine?

SatPy depends heavily on the dask library for its performance. However, on some systems dask’s default settings can actually hurt performance. By default dask will create a “worker” for each logical core on your system. In most systems you have twice as many logical cores (also known as threaded cores) as physical cores. Managing and communicating with all of these workers can slow down dask, especially when they aren’t all being used by most SatPy calculations. One option is to limit the number of workers by doing the following at the top of your python code:

import dask
from multiprocessing.pool import ThreadPool
# all other SatPy imports and code

This will limit dask to using 8 workers. Typically numbers between 4 and 8 are good starting points.

Similarly, if you have many workers processing large chunks of data you may be using much more memory than you expect. If you limit the number of workers and the size of the data chunks being processed by each worker you can reduce the overall memory usage. Default chunk size can be configured in SatPy by setting the following environment variable:


This could also be set inside python using os.environ, but must be set before SatPy is imported. This value defaults to 4096, meaning each chunk of data will be 4096 rows by 4096 columns. In the future setting this value will change to be easier to set in python.

How do I avoid memory errors?

If your environment is using many dask workers, it may be using more memory than it needs to be using. See the “Why is SatPy slow on my powerful machine?” question above for more information on changing SatPy’s memory usage.