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.

How can I speed up creation of composites that need resampling?

Satpy performs some initial image generation on the fly, but for composites that need resampling (like the true_color composite for GOES/ABI) the data must be resampled to a common grid before the final image can be produced, as the input channels are at differing spatial resolutions. In such cases, you may see a substantial performance improvement by passing generate=False when you load your composite:

scn = Scene(filenames=filenames, reader='abi_l1b')
scn.load(['true_color'], generate=False)
scn_res = scn.resample(...)

By default, generate=True which means that Satpy will create as many composites as it can with the available data. In some cases this could mean a lot of intermediate products (ex. rayleigh corrected data using dynamically generated angles for each band resolution) that will then need to be resampled. By setting generate=False, Satpy will only load the necessary dependencies from the reader, but not attempt generating any composites or applying any modifiers. In these cases this can save a lot of time and memory as only one resolution of the input data have to be processed. Note that this option has no effect when only loading data directly from readers (ex. IR/visible bands directly from the files) and where no composites or modifiers are used. Also note that in cases where most of your composite inputs are already at the same resolution and you are only generating a limited number of composites, generate=False may actually hurt performance.

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
# 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 using the following around your code:

with dask.config.set("array.chunk-size": "32MiB"):
  # your code here

For more information about chunk sizes in Satpy, please refer to the Data Chunks section in Overview.


The PYTROLL_CHUNK_SIZE variable is pending deprecation, so the above-mentioned dask configuration parameter should be used instead.

Why multiple CPUs are used even with one worker?

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

What is the difference between number of workers and number of threads?

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.

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.

Reducing GDAL output size?

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:

export GDAL_CACHEMAX="15%"

For more information see GDAL’s documentation.

How do I use multi-threaded compression when writing GeoTIFFs?

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 adding num_threads to your save_dataset or save_datasets call:

scn.save_datasets(base_dir='/tmp', num_threads=8)

Satpy also stores our data as “tiles” instead of “stripes” which is another way to get more efficient compression of our GeoTIFF image. You can disable this with tiled=False.

See the GDAL GeoTIFF documentation for more information on the creation options available including other compression choices.