We introduce a cache to avoid computing find_necessary_steps during each inference call#10
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We introduce a cache to avoid computing find_necessary_steps during each inference call#10
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…ing bug does not occur
tobibaum
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Feb 22, 2018
tobibaum
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tobibaum
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looks good! just the little typo in the test.
| inputs_keys = tuple(sorted(inputs.keys())) | ||
| cache_key = (inputs_keys, outputs) | ||
| if cache_key in self._necessary_steps_cache: | ||
| return self._necessary_steps_cache[cache_key] |
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i had no idea you could have a tuple of tuples as a key. seems to work though.
test/test_graphkit.py
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| pipeline = compose(name="pipeline", merge=True)( | ||
| operation(name="op_a", needs=['a', 'b'], provides='c')(op_a), | ||
| operation(name="op_b", needs=['c', 'b'], provides='d')(op_b), | ||
| operation(name="op_c", needs=['a', 'b'], provides='e')(op_b), |
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Ah thanks, good catch
Codecov Report
@@ Coverage Diff @@
## master #10 +/- ##
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+ Coverage 77.21% 77.8% +0.59%
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Files 5 5
Lines 338 347 +9
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+ Hits 261 270 +9
Misses 77 77
Continue to review full report at Codecov.
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Okay updated the test and verified that no errors occur |
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👍 |
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@tobibaum
I've introduced a cache to avoid computing find_necessary_steps multiple times during each inference call. This has 2 benefits: 1) it reduces computation time of the compute call 2) it avoids a subtle multi-threading bug in networkx when accessing the graph from a high number of threads.
The following test should fail without these changes:
https://github.com/yahoo/graphkit/compare/multithreading_fix?expand=1#diff-eb68f4dabe94fb4dfdc148f0ef8961f8R283
Thanks for reviewing!