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.
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 dask.config.set(pool=ThreadPool(8)) # all other Satpy imports and code
This will limit dask to using 8 workers. Typically numbers between 4 and 8 are good starting points. Number of workers can also be set from an environment variable before running the python script, so code modification isn’t necessary:
DASK_NUM_WORKERS=4 python myscript.py
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.
Many of the underlying Python libraries use math libraries like BLAS and LAPACK written in C or FORTRAN, and they are often compiled to be multithreaded. If necessary, it is possible to force the number of threads they use by setting an environment variable:
OMP_NUM_THREADS=2 python myscript.py
The above questions handle two different stages of parallellization: Dask workers and math library threading.
The number of Dask workers affect how many separate tasks are started, effectively telling how many chunks of the data are processed at the same time. The more workers are in use, the higher also the memory usage will be.
The number of threads determine how much parallel computations are run for the chunk handled by each worker. This has minimal effect on memory usage.
The optimal setup is often a mix of these two settings, for example
DASK_NUM_WORKERS=2 OMP_NUM_THREADS=4 python myscript.py
would create two workers, and each of them would process their chunk of data using 4 threads when calling the underlying math libraries.
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.
Sometimes GDAL-based products, like geotiffs, can be much larger than expected.
This can be caused by GDAL’s internal memory caching conflicting with dask’s
chunking of the data arrays. Modern versions of GDAL default to using 5% of
available memory for holding on to data before compressing it and writing it
to disk. On more powerful systems (~128GB of memory) this is usually not a
problem. However, on low memory systems this may mean that GDAL is only
compressing a small amount of data before writing it to disk. This results
in poor compression and large overhead from the many small compressed areas.
One solution is to increase the chunk size used by dask but this can result
in poor performance during computation. Another solution is to increase
GDAL_CACHEMAX, an environment variable that GDAL uses. This defaults to
"5%", but can be increased:
For more information see GDAL’s documentation.
The GDAL library’s GeoTIFF driver has a lot of options for changing how your
GeoTIFF is formatted and written. One of the most important ones when it comes
to writing GeoTIFFs is using multiple threads to compress your data. By
default Satpy will use DEFLATE compression which can be slower to compress
than other options out there, but faster to read. GDAL gives us the option to
control the number of threads used during compression by specifying the
num_threads option. This option defaults to
1, but it is recommended
to set this to at least the same number of dask workers you use. Do this by
num_threads to your save_dataset or save_datasets call:
scn.save_datasets(base_dir='/tmp', tiled=True, num_threads=8)
Here we’re also using the tiled option to store our data as “tiles” instead of “stripes” which is another way to get more efficient compression of our GeoTIFF image.
See the GDAL GeoTIFF documentation for more information on the creation options available including other compression choices.