speed up tile download with concurrent processing #182
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drewbo
suggested changes
Aug 28, 2020
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Awesome and glad it will speed things up considerably. A few notes:
- the thread count should be configurable via CLI, let's add it as a flag (not in the config.json since it isn't data related). Note that threads != CPU cores, so you can try like ~50 and see if you get faster downloads (my guess is somewhere between 20-50 is the fastest)
- because we don't use the result of the function anywhere, we don't need to use
.as_completedor.result. I'd recommend using shutdown or wait and making sure that the image function itself logs failures
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Re: supertile child download: that function will already be happening inside the |
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the download time for ~400 tiles with thread count of 50 is ~4 seconds! |
drewbo
reviewed
Aug 28, 2020
label_maker/images.py
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| image_function(tile, imagery, tiles_dir, kwargs) | ||
| t = time.perf_counter() | ||
| with concurrent.futures.ThreadPoolExecutor(max_workers=threadcount) as executor: | ||
| {executor.submit(image_function, tile, imagery, tiles_dir, kwargs): tile for tile in tiles} |
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this can be a list comprehension instead of dict
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@martham93 sweet; looks better. Last two things: (1) make the default threads ~10 and then people can up it if they want and (2) either remove the time elapsed or print it with nicer formatting |
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awesome thanks @drewbo all of these should be addressed now |
drewbo
approved these changes
Aug 31, 2020
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to address issue #12
cc @drewbo