306 lines
16 KiB
INI
306 lines
16 KiB
INI
# Example config for C++ (non-python) gtp bot
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# RUNNING ON AN ONLINE SERVER OR IN A REAL TOURNAMENT OR MATCH:
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# If you plan to do so, you may want to read through the "Rules" section
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# below carefully for proper handling of komi and handicap games and end-of-game cleanup
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# and various other details.
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# NOTES ABOUT PERFORMANCE AND MEMORY USAGE:
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# You will likely want to tune one or more the following:
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#
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# numSearchThreads:
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# The number of CPU threads to use. If your GPU is powerful, it can actually be much higher than
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# the number of cores on your processor because you will need many threads to feed large enough
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# batches to make good use of the GPU.
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#
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# The "./katago benchmark" command can help you tune this parameter, as well as to test out the effect
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# of changes to any of the other parameters below!
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#
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# nnMaxBatchSize:
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# The maximum GPU batch size. Should often be at least as large as numSearchThreads.
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# Larger won't do anything, but also won't hurt except use a little bit more GPU memory.
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# Smaller can be fine if you have more than one GPU, since the GPUs will be sharing the work
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# of servicing the CPU threads.
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#
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# cudaUseFP16 and cudaUseNHWC:
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# These have a good chance of improving peformance at larger threads/batch sizes if
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# you are using the CUDA implementation with an NVIDIA GPU with FP16 tensor cores.
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#
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# nnCacheSizePowerOfTwo:
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# This controls the NN Cache size, which is the primary RAM/memory use.
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# Increase this if you don't mind the memory use and want better performance for searches with
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# tens of thousands of visits or more. Decrease this if you want to limit memory usage.
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#
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# If you're someone who is happy to do a bit of math - each neural net entry takes very
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# approximately 1.5KB, except when using whole-board ownership/territory visualizations, each
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# entry will take very approximately 3KB. The number of entries is (2 ** nnCacheSizePowerOfTwo),
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# for example 2 ** 18 = 262144.
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#
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# OTHER NOTES:
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# If you have more than one GPU, take a look at "OpenCL GPU settings" or "CUDA GPU settings" below.
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#
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# If using OpenCL, you will want to verify that KataGo is picking up the correct device!
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# (e.g. some systems may have both an Intel CPU OpenCL and GPU OpenCL, if KataGo appears to pick
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# the wrong one, you correct this by specifying "openclGpuToUse" below).
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#
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# You may also want to adjust "maxVisits", "ponderingEnabled", "resignThreshold", and possibly
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# other parameters depending on your intended usage.
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# Logs------------------------------------------------------------------------------------
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# Where to output log?
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logFile = gtp.log
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# Logging options
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logAllGTPCommunication = true
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logSearchInfo = true
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logToStderr = false
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# KataGo will display some info to stderr on GTP startup
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# Uncomment this to suppress that and remain silent
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# startupPrintMessageToStderr = false
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# Chat some stuff to stderr, for use in things like malkovich chat to OGS.
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# ogsChatToStderr = true
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# Configure the maximum length of analysis printed out by lz-analyze and other places.
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# Controls the number of moves after the first move in a variation.
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# analysisPVLen = 9
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# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE).
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# Default is SIDETOMOVE, which is what tools that use LZ probably also expect
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# reportAnalysisWinratesAs = SIDETOMOVE
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# Default rules------------------------------------------------------------------------------------
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# See https://lightvector.github.io/KataGo/rules.html for a description of the rules.
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# These rules are defaults and can be changed mid-run by several custom GTP commands.
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# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md for those commands.
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koRule = SIMPLE # Simple ko rules (triple ko = no result)
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# koRule = POSITIONAL # Positional superko
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# koRule = SITUATIONAL # Situational superko
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# scoringRule = AREA # Area scoring
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scoringRule = TERRITORY # Territory scoring (uses a sort of special computer-friendly territory ruleset)
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# taxRule = NONE # All surrounded empty points are scored
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taxRule = SEKI # Eyes in seki do NOT count as points
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# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live
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multiStoneSuicideLegal = false #Is multiple-stone suicide legal? (Single-stone suicide is always illegal).
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hasButton = false # Set to true when area scoring to award 0.5 points to the first pass.
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whiteHandicapBonus = 0 # In handicap games, give white no compensation for black's handicap stones (Tromp-taylor, NZ, JP)
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# whiteHandicapBonus = N-1 # In handicap games, give white N-1 points for black's handicap stones (AGA)
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# whiteHandicapBonus = N # In handicap games, give white N points for black's handicap stones (Chinese)
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# Bot behavior---------------------------------------------------------------------------------------
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# Resignation -------------
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# Resignation occurs if for at least resignConsecTurns in a row,
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# the winLossUtility (which is on a [-1,1] scale) is below resignThreshold.
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allowResignation = true
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resignThreshold = -0.999
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resignConsecTurns = 3
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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 understanding of komi if whiteHandicapBonus is used 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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# Makes katago dynamically adjust to play more aggressively in handicap games based on the handicap and the current state of the game.
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# Comment to disable this and make KataGo play the same always.
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dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.04
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# Instead of setting dynamicPlayoutDoublingAdvantageCapPerOppLead, you can uncomment these and set this to a value from -2.0 to 2.0
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# to set KataGo's aggression to a FIXED level.
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# Negative makes KataGo behave as if it is much weaker than the opponent, preferring to play defensively
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# Positive makes KataGo behave as if it is much stronger than the opponent, prefering to play aggressively or even overplay slightly.
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# playoutDoublingAdvantage = 0.0
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# Controls which side dynamicPlayoutDoublingAdvantageCapPerOppLead or playoutDoublingAdvantage applies to.
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playoutDoublingAdvantagePla = WHITE
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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 playing under territory scoring, encourage the bot to fill dame before passing.
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# This is NOT absolutely guaranteed to work, and possibly in some rare pathological situations will make the bot
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# play a bad move, losing points. However, it also acts as a safeguard against things like a situation when the opponent must
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# eventually make a protective move and lose 1 point, where the bot might otherwise assume that the score would be counted as such,
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# yet without filling the dame to force the opponent to actually make the move.
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# Defaults to true! Uncomment and set to false to disable this.
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# fillDameBeforePass = 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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# If provided, limit maximum number of root visits per search to this much. (With tree reuse, visits do count earlier search)
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maxVisits = 2500
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# If provided, limit maximum number of new playouts per search to this much. (With tree reuse, playouts do not count earlier search)
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# maxPlayouts = 300
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# If provided, cap search time at this many seconds (search will still try to follow GTP time controls)
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# maxTime = 60
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# Ponder on the opponent's turn?
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ponderingEnabled = false
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# Same limits but for ponder searches if pondering is enabled
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# maxVisitsPondering = 1000
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# maxPlayoutsPondering = 1000
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# maxTimePondering = 60
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# Number of seconds to buffer for lag for GTP time controls
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lagBuffer = 1.0
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# Number of threads to use in search
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numSearchThreads = 4
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# Play a little faster if the opponent is passing, for friendliness
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searchFactorAfterOnePass = 0.50
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searchFactorAfterTwoPass = 0.25
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# Play a little faster if super-winning, for friendliess
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searchFactorWhenWinning = 0.40
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searchFactorWhenWinningThreshold = 0.95
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# GPU Settings-------------------------------------------------------------------------------
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# Maximum number of positions to send to GPU at once. Note that you will also need to increase numSearchThreads
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# to make use of this, as every thread in KataGo is synchronous, so with 1 thread max batch will only be 1 anyways.
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nnMaxBatchSize = 16
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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 = 20
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# Size of mutex pool for nnCache is 2 ** this
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nnMutexPoolSizePowerOfTwo = 16
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# Randomize board orientation when running neural net evals?
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nnRandomize = true
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# If provided, force usage of a specific seed for nnRandomize instead of randomizing
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# nnRandSeed = abcdefg
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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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# If provided, force usage of a specific seed for various things in the search instead of randomizing
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# searchRandSeed = hijklmn
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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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cpuctExploration = 0.9
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cpuctExplorationLog = 0.6
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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 = 8192
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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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