178 lines
9.4 KiB
INI
178 lines
9.4 KiB
INI
# Example config for C++ (non-python) gtp bot
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# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg
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# Logs------------------------------------------------------------------------------------
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# Where to output log?
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logFile = gtp.log
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# Controls the number of moves after the first move in a variation.
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# analysisPVLen = 15
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# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE).
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reportAnalysisWinratesAs = BLACK
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# Bot behavior---------------------------------------------------------------------------------------
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# Handicap -------------
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# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game.
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# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may
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# set up a handicap games using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands.
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# However, it may also lead to incorrect undersanding of komi if whiteBonusPerHandicapStone = 1 and a server does NOT
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# have such a practice.
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# Defaults to true! Uncomment and set to false to disable this behavior.
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# assumeMultipleStartingBlackMovesAreHandicap = true
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# Passing and cleanup -------------
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# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules.
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# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly.
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# Defaults to true! Uncomment and set to false to disable this.
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conservativePass = true
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# When using territory scoring, self-play games continue beyond two passes with special cleanup
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# rules that may be confusing for human players. This option prevents the special cleanup phases from being
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# reachable when using the bot for GTP play.
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# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup.
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# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules
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# documented at https://lightvector.github.io/KataGo/rules.html
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# preventCleanupPhase = true
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# Search limits-----------------------------------------------------------------------------------
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# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much
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maxVisits = 500
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# If provided, cap search time at this many seconds
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# maxTime = 60
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# Number of threads to use in each search in parallel for any SINGLE position.
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# NOTE: Analysis engine can specify number of POSITIONS to be able to search in parallel via command line argument
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# so this number does not necessarily need to be larger than 1, although you can still set it larger if you prefer
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# to analyze fewer positions in parallel but spend more threads on each position.
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# Generally, having more threads on a single position will worsen the quality of search slightly, holding fixed the
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# number of visits, and thread contention will reduce efficiency, so cross-position parallelization is preferable
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# to numSearchThreads, but numSearchThreads is preferable if you want to reduce latency, and have individual
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# searches complete faster by doing fewer of them at a time.
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numSearchThreads = 2
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# GPU Settings-------------------------------------------------------------------------------
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# Maximum number of positions to send to GPU at once.
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nnMaxBatchSize = 128
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# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree.
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nnCacheSizePowerOfTwo = 23
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# Size of mutex pool for nnCache is 2 ** this
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nnMutexPoolSizePowerOfTwo = 17
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# Randomize board orientation when running neural net evals?
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nnRandomize = true
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# How many threads should there be to feed positions to the neural net?
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# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments
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# that specify which threads should use which GPUs.
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# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread.
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# If you're tuning single-GPU performance, use numSearchThreads instead.
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numNNServerThreadsPerModel = 1
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# CUDA GPU settings--------------------------------------
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# These only apply when using CUDA as the backend for inference.
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# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
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# Default behavior tries to guess the 'best' GPU or device
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# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
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# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
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# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
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# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
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# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
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# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
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# Uncomment these on NVIDIA devices with FP16 tensor cores for probably a speedup, at the cost of introducing some precision loss in the nn calculation.
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# cudaUseFP16 = true
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# cudaUseNHWC = true
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# OpenCL GPU settings--------------------------------------
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# These only apply when using OpenCL as the backend for inference.
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# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg)
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# Default behavior tries to guess the 'best' GPU or device
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# You will want to uncomment and adjust one or more of these lines to take advantage of a multi-gpu machine
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# openclDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model
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# openclDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model
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# openclDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model
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# openclDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0
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# openclDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1
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# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size
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# openclReTunePerBoardSize = true
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# Root move selection and biases------------------------------------------------------------------------------
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# Not all of these parameters are applicable to analysis, some are only used for actual play
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# Temperature for the early game, randomize between chosen moves with this temperature
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chosenMoveTemperatureEarly = 0.5
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# Decay temperature for the early game by 0.5 every this many moves, scaled with board size.
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chosenMoveTemperatureHalflife = 19
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# At the end of search after the early game, randomize between chosen moves with this temperature
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chosenMoveTemperature = 0.10
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# Subtract this many visits from each move prior to applying chosenMoveTemperature
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# (unless all moves have too few visits) to downweight unlikely moves
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chosenMoveSubtract = 0
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# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above
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chosenMovePrune = 1
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# Use dirichlet noise for the root node policy?
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rootNoiseEnabled = false
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# Dirichlet noise alpha is set to this divided by number of legal moves. 10.83 produces an alpha of 0.03 on an empty 19x19 board.
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rootDirichletNoiseTotalConcentration = 10.83
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# Proportion of root policy that is noise
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rootDirichletNoiseWeight = 0.25
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# Number of symmetries to sample (WITH replacement) and average at the root
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rootNumSymmetriesToSample = 1
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# Using LCB for move selection?
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useLcbForSelection = true
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# How many stdevs a move needs to be better than another for LCB selection
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lcbStdevs = 5.0
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# Only use LCB override when a move has this proportion of visits as the top move
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minVisitPropForLCB = 0.15
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# Internal params------------------------------------------------------------------------------
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# Scales the utility of winning/losing
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winLossUtilityFactor = 1.0
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# Scales the utility for trying to maximize score
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staticScoreUtilityFactor = 0.10
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dynamicScoreUtilityFactor = 0.30
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# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount.
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dynamicScoreCenterZeroWeight = 0.20
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dynamicScoreCenterScale = 0.75
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# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1)
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noResultUtilityForWhite = 0.0
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# The number of wins that a draw counts as, for white. (0 to 1)
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drawEquivalentWinsForWhite = 0.5
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# Exploration constant for mcts
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# adjusted from 0.9 / 0.6 to be more exploratory
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cpuctExploration = 2
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cpuctExplorationLog = 0.9
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# FPU reduction constant for mcts
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fpuReductionMax = 0.2
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rootFpuReductionMax = 0.1
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# Use parent average value for fpu base point instead of point value net estimate
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fpuUseParentAverage = true
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# Amount to apply a downweighting of children with very bad values relative to good ones
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valueWeightExponent = 0.5
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# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame,
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# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of
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# points but a bit more unfriendly to humans.
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rootEndingBonusPoints = 0.5
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# Make the bot prune useless moves that are just prolonging the game to avoid losing yet
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rootPruneUselessMoves = true
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# How big to make the mutex pool for search synchronization
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mutexPoolSize = 2048
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# How many virtual losses to add when a thread descends through a node
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numVirtualLossesPerThread = 1
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