{"push_id":2561072363,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"b8e6ec354c9a47f2613518307d4201457d1547a2","before":"7286063673c2f3ee583bbd3933e8a46ac9dacd6d","commits":[{"sha":"b8e6ec354c9a47f2613518307d4201457d1547a2","author":{"name":"SamRagusa","email":"b1c1d8736f20db3fb6c1c66bb1455ed43909f0d8@SamRagusa.com"},"message":"Performance Improvements / Bug Fix\n\n-Changed the ChEstimator's ANN inference pipeline.  Among other improvements it now receives the ep square as a uint8 instead of the uint64 bitboard (a similar approach will be applied to castling rights, and the kings very soon)\n-Implemented a few tricks for the PriorityBins class's insert_nodes_and_get_next_batch method to sometimes avoid computing things like which bin each node belongs to\n-Now setting a board's pseudo legal moves while they're being found, instead of using generators (mainly because JIT compilation was taking way too long)\n-Implemented legal move checking for NumPy structured scalar board representations (as opposed to JitClass), and refactored how it is computed\n-Fixed an error occurring when a board had only one possible move, and it was discovered using the transposition table\n-Joining the board evaluation thread when it's supposed to be completed (I'm still not sure what was causing the error which required it to be joined earlier, it was very likely something involved with how TensorFlow was handling the two sessions)\n-Removed almost all use of BoardState JitClass (except for one usage soon to be refactored out)\n-Various minor refactoring\n-Very minor code cleaning","distinct":true,"url":"https://api.github.com/repos/SamRagusa/Batch-First/commits/b8e6ec354c9a47f2613518307d4201457d1547a2"}]}
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