Problem with training about leela-zero HOT 6 CLOSED

Try

gzip train.0
parse.py train

Parse I think will check for the number and expect the .gz on its own.

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Next problem I have

name: GeForce GTX 760 major: 3 minor: 0 memoryClockRate(GHz): 1.0845
pciBusID: 0000:01:00.0
totalMemory: 1.95GiB freeMemory: 1.92GiB

and I get this :

2017-11-14 21:29:51.688804: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 227.25MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.

2017-11-14 21:29:51.692699: W tensorflow/stream_executor/cuda/cuda_dnn.cc:2223]

2017-11-14 21:29:51.693036: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 227.25MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.

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I had to halve the BATCH_SIZE in training/tf/parse.py (set it to 128) to avoid (I think?) that error on my GTX 950. I may have had to lower per_process_gpu_memory_fraction a little, too.

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Thanks it works now! even if 224.638 pos/s I guess is not that good

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I may have had to lower per_process_gpu_memory_fraction a little, too.

You can probably use larger batches if you set it higher, not lower. TensorFlow defaults to using 100% of GPU RAM but this is annoying if you want to run leelaz at the same time, so I changed the default to 75%.

If you lower the batch size, you should lower the learning rate in MomentumOptimizer a bit, by a factor of sqrt() the factor you lowered the batch size with.

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@marcocalignano Is this issue ready to close? :)

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