Implement streaming annotations #51
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… object Minimizing information passed to allow for future buffering of trace.stats without storing the full trace.
Based on asyncio, the different steps of running predictions on streams are now implemented as coroutines. Therefore they can be run in parallel. This improves both CPU and GPU utilization. In addition, batching is now possible across stations, leading to performance improvements when annotating many small input fragments. When annotating long traces with point-prediction models, the new implementation also reduces memory consumption, as not all waveform fragments are created at once.
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Hey thanks for implementing this! I agree with publishing to 0.1.8 as the user interface hasn't changed - makes sense. I'll take a look and get back to you. |
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I've run across datasets and waveforms (similar to the unit test cases you implement but just for seismic examples with various models). All looks good. Merging now. |
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This commit implements streaming processing for WaveformModels. This has several advantages:
As this only modifies the backend, but does not introduce any changes in the user interface, I'd suggest to publish it as 0.1.8 (instead of 0.2.0), even though it's technically not a bugfix. What do you think @jawooll ? In addition, because it's a somewhat more complicated change, I'd ask you to have a look at it.
Closes #40