Comments (2)
Thanks! It looks like this is not necessarily immediately relevant to this project, as it seems to be about how to improve how training scales with very large batches, which comes up in the situation when you have a very large amount of parallel compute on the neural net training side across many GPUs/TPUs and therefore need large batches to be able to optimally use the hardware, as opposed to when training is only on one or a few GPUs and batch sizes cannot be so large anyways. For Go, most of the compute cost is on the self-play side for data generation, which is also already highly parallelizable.
Nonetheless, it is interesting - thanks!
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Related Issues (20)
- Interface for a custom algorithm to play as a bot HOT 8
- Katago model preference for Go beginner HOT 16
- Try Gemma.cpp to explain the meaning of each move HOT 3
- error CL_OUT_OF_RESOURCES when launching ./katago benchmark
- Can't compile for OpenCL 1.2 HOT 3
- FATAL ERROR: Failed test assert: buf.result->policyProbs[buf.result->getPos(Location::ofString("E16",board),board)] >= 0.95 HOT 2
- ERROR: task loop loop thread failed: Got nonfinite for policy sum HOT 11
- Scoring bug HOT 2
- Unable to compile - error: invalid conversion from ‘float**’ to ‘void**’
- Pytorch load model from tensorflow .ckpt and play: Mismatch and Performance Issue with b18c384nbt model_pytorch and Checkpoint File HOT 2
- How is distributed training implemented in KataGo? HOT 1
- A question in the "match" module HOT 4
- Loading the latest 28b engine failed with 8 4090 cards HOT 2
- A question about moves not being buffed by LCB HOT 2
- match: Wrong useNHWC/FP16 settings are given to bot if there are bot dedups HOT 2
- difference in winrate and scorelead between MoveInfos and RootInfo HOT 1
- Does the latest katago networks no longer work with Katrain, Lizzie, nor any other GUI? HOT 3
- Winrate Help HOT 2
- Uncaught exception when genconfig HOT 3
- 举报OmnipotentEntity在discord群任意踢人 HOT 22
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