# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis"

# Example config for C++ (non-python) analysis engine

# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg
# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW!

# Logs------------------------------------------------------------------------------------

# Where to output log?
logDir = analysis_logs  # Each run of KataGo will log to a separate file in this dir
# logDirDated = analysis_logs  # Use this instead of logDir to also write separate dated subdirs
# logFile = analysis.log  # Use this instead of logDir to just specify a single file directly
# logToStderr = true      # Echo everything output to log file to stderr as well
# logAllRequests = false  # Log all input lines received to the analysis engine.
# logAllResponses = false # Log all lines output to stdout from the analysis engine.
# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings
# logSearchInfo = false   # Log debug info for every search performed

# Report a warning whenever a query contains a field that is unused. Helps to guard against
# typos when writing code that queries the analysis engine.
# warnUnusedFields = true

# Analysis------------------------------------------------------------------------------------

# Controls the number of moves after the first move in a variation.
# analysisPVLen = 15

# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE).
reportAnalysisWinratesAs = BLACK

# Larger values will make KataGo explore the top move(s) less deeply and accurately,
# but explore and give evaluations to a greater variety of moves.
# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves.
# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis.
# If you are intending to use the analysis engine to also play games and you want to maximize playing strength,
# set this to 0.0 either in this config or in the overrides.
# wideRootNoise = 0.04

# Try to limit the effect of possible bad or bogus move sequences in the
# history leading to this position from affecting KataGo's move predictions.
# ignorePreRootHistory = true

# Bot behavior---------------------------------------------------------------------------------------

# Handicap -------------

# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game.
# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may
# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands.
# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT
# have such a practice.
# Defaults to true! Uncomment and set to false to disable this behavior.
# assumeMultipleStartingBlackMovesAreHandicap = true

# Passing and cleanup -------------

# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules.
# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly.
# Defaults to true! Uncomment and set to false to disable this.
# conservativePass = true

# When using territory scoring, self-play games continue beyond two passes with special cleanup
# rules that may be confusing for human players. This option prevents the special cleanup phases from being
# reachable when using the bot for GTP play.
# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup.
# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules
# documented at https://lightvector.github.io/KataGo/rules.html
# preventCleanupPhase = true

# Experimental feature - useEvalCache is false by default in v1.16.4,
# may become true by default in later versions.
# If set to true, will remember the evaluations of any node that got
# at least evalCacheMinVisits many visits, and on future searches
# that encounter that exact position, until KataGo is restarted or
# the cache is cleared, can bias future searches based on that eval.
# In particular, if you are interactively analyzing and help KataGo solve
# a blind spot by playing down a variation with it, it can remember and
# become more capable of solving that variation from earlier as well.
# useEvalCache = false
# evalCacheMinVisits = 100

# Search limits-----------------------------------------------------------------------------------

# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much
maxVisits = 500
# If provided, cap search time at this many seconds
# maxTime = 60

# Search threads, batching, GPUs--------------------------------------------------------------------------

# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want
# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall
# peak throughput on queries to be lower.
numAnalysisThreads = 2
numSearchThreadsPerAnalysisThread = 16

# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize
# total throughput and also the evaluation quality of all the queries and you never care about the response latency
# of the individual queries, only the throughput as a whole.
# numAnalysisThreads = 32
# numSearchThreadsPerAnalysisThread = 1

# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you
# have a very weak GPU, and play with the balance between them depending on your use case.
# Read the explanation below to understand how to set these parameters:

# EXPLANATION:
# numAnalysisThreads:                 the number of POSITIONS to be able to search in parallel.
# numSearchThreadsPerAnalysisThread:  the number of threads to use in the tree search for EACH position.
# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread')

# Therefore, the total number of search threads that may be active at a given time could be as large as the product:
# numAnalysisThreads * numSearchThreadsPerAnalysisThread

# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each
# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower
# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration,
# although the cost is minor for only 2,4,8 threads.

# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make
# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small,
# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput
# and strength of the search.

# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions
# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two
# queries at a time!

# Therefore:
# * If you plan to use the analysis engine only for batch processing large numbers of positions,
#   it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads.
# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities
#   than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual
#   analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use
#   a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one.

# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread)
# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256.
# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case.

# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your
# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration.

# -------------

# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that:
# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads)
# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them
# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory,
# too large a number may fail if the GPU doesn't have enough memory.
nnMaxBatchSize = 64

# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to
# run multiple instances, with some instances only handling smaller boards). It should improve performance.
# It may also mean you can use more threads profitably.
# maxBoardXSizeForNNBuffer = 19
# maxBoardYSizeForNNBuffer = 19

# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size
# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs.
# requireMaxBoardSize = true

# TO USE MULTIPLE GPUS:
# Uncomment and set this to the number of GPUs you have and/or would like to use...
# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below.
# numNNServerThreadsPerModel = 1

# Other General GPU Settings-------------------------------------------------------------------------------

# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree.
nnCacheSizePowerOfTwo = 23
# Size of mutex pool for nnCache is 2 ** this
nnMutexPoolSizePowerOfTwo = 17
# Randomize board orientation when running neural net evals?
nnRandomize = true


# TENSORRT GPU settings--------------------------------------
# These only apply when using the TENSORRT version of KataGo.

# Transformer trunks use NHWC by default, which is normally faster. Set this to false only after benchmarking
# the particular GPU and TensorRT version if you think NCHW is faster.
# trtTransformerNHWC = true

# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0
# trtDeviceToUse = 0

# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above):
# trtDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# trtDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1

# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above):
# trtDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# trtDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1
# trtDeviceToUseThread2 = 2  # change this if the third GPU you want to use turns out to be not device 2

# You can probably guess the pattern if you have four, five, etc. GPUs.


# CUDA-specific GPU settings--------------------------------------
# These only apply when using the CUDA version of KataGo.

# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0
# cudaDeviceToUse = 0

# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above):
# cudaDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# cudaDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1

# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above):
# cudaDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# cudaDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1
# cudaDeviceToUseThread2 = 2  # change this if the third GPU you want to use turns out to be not device 2

# You can probably guess the pattern if you have four, five, etc. GPUs.

# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you
# want to try to force a particular behavior though you can uncomment these lines and change them
# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using
# FP16 but you think it should.
# cudaUseFP16 = auto
# cudaUseNHWC = auto


# Metal GPU settings--------------------------------------
# These only apply when using the METAL version of KataGo.

# Metal backend dispatch is configured via numNNServerThreadsPerModel and metalDeviceToUseThread<N>.
# Device index values:
#   0   = GPU only (MPSGraph) - default
#   100 = ANE only (CoreML, runs on CPU + Apple Neural Engine)
#
# Mux mode (recommended): 4 pipelined server threads (2x GPU + 2x ANE).
# Set nnMaxBatchSize to half of numSearchThreads for optimal pipelining.
#
# Example: mux mode (best throughput)
# numNNServerThreadsPerModel = 4
# metalDeviceToUseThread0 = 0
# metalDeviceToUseThread1 = 0
# metalDeviceToUseThread2 = 100
# metalDeviceToUseThread3 = 100
#
# Example: GPU-only mode (default)
# numNNServerThreadsPerModel = 1
# metalDeviceToUseThread0 = 0
#
# Example: ANE-only mode
# numNNServerThreadsPerModel = 1
# metalDeviceToUseThread0 = 100
#
# Default (no config): 1 server thread, GPU-only mode (gpuIdx = 0).

# FP16 precision (default true). Currently this only affects the ANE/CoreML path:
# the GPU/MPSGraph path always runs in FP32 regardless of this flag. Setting this
# to false also disables ANE acceleration (the ANE is FP16-only hardware, so
# CoreML threads fall back to CPU FP32) - useful for deterministic reference
# inference but much slower than the GPU path.
# metalUseFP16 = true


# ROCm GPU settings--------------------------------------
# These only apply when using the ROCm version of KataGo.

# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0
# rocmDeviceToUse = 0

# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above):
# rocmDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# rocmDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1

# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above):
# rocmDeviceToUseThread0 = 0  # change this if the first GPU you want to use turns out to be not device 0
# rocmDeviceToUseThread1 = 1  # change this if the second GPU you want to use turns out to be not device 1
# rocmDeviceToUseThread2 = 2  # change this if the third GPU you want to use turns out to be not device 2

# You can probably guess the pattern if you have four, five, etc. GPUs.

# KataGo uses FP16 by default (all AMD GPU architectures this backend supports handle FP16; it is
# only auto-disabled if the build itself was compiled without FP16 kernel support). If you want to
# force a particular behavior you can uncomment these lines and change them to "true" or "false",
# e.g. if FP16 is giving an error or too much numerical inaccuracy on your card.
# rocmUseFP16 = auto
# rocmUseNHWC = auto  # Uses NHWC tensor layout. Default: auto - NHWC with FP16 on GPUs where NHWC convolutions are faster (CDNA), else NCHW; transformers always NHWC.
# rocmUse1x1Matmul = auto  # Whether 1x1 NHWC convs run as a hipBLAS GEMM instead of a MIOpen conv. auto = GEMM when possible (MIOpen's NHWC conv are slow on some GPUs)


# ONNX Runtime backend settings--------------------------------------
# These only apply when using the ONNX version of KataGo.
# See gtp_example.cfg for longer explanations of these options.

# Which hardware to run the neural net on. This is REQUIRED - the ONNX backend will
# not run without it. KataGo's official ONNX packages are named after the provider they
# support (e.g. onnx-openvino supports openvino), and every package also supports cpu.
#   openvino - Intel GPUs and NPUs.
#   directml - any DirectX 12 GPU (Windows only).
#   cuda     - NVIDIA GPUs. KataGo's native CUDA and TensorRT backends are likely faster.
#   tensorrt - NVIDIA GPUs, likewise.
#   migraphx - AMD GPUs (Linux only). Untested - sanity-check results if you try it.
#   cpu      - runs anywhere, but is slow and does not use any GPU or NPU.
#   coreml   - macOS only, not working yet.
# onnxProvider = ...

# Device index, mainly for choosing among multiple GPUs with the cuda / tensorrt /
# migraphx providers. For the OpenVINO provider, prefer the device options below.
# onnxDeviceToUse = 0
# onnxDeviceToUseThread0 = 0
# onnxDeviceToUseThread1 = 1

# OpenVINO options (only used when onnxProvider = openvino):
# Which device to use: GPU (default), NPU, a specific device like GPU.0 or GPU.1 if you
# have several, or an OpenVINO multi-device string such as AUTO:GPU,CPU. The per-thread
# overrides allow using GPU and NPU at the same time (set numNNServerThreadsPerModel
# above to the number of threads you assign).
# onnxOpenVINODeviceType = GPU
# onnxOpenVINODeviceTypeThread0 = GPU
# onnxOpenVINODeviceTypeThread1 = NPU

# Cache compiled models in this directory (relative to where KataGo runs). Without it,
# OpenVINO recompiles the model at every startup, which can be slow.
# onnxOpenVINOCacheDir = katago_ov_cache

# GPU inference precision (FP16 default, or FP32, ACCURACY) and number of GPU execution
# streams (2 or more can substantially increase performance on a strong GPU with many
# search threads). The NPU always runs FP16 and single-stream.
# onnxOpenVINOPrecision = FP16
# onnxOpenVINONumStreams = 1

# The options below rarely need changing - see gtp_example.cfg.
# onnxPadBatch = auto
# onnxSkipScale8 = false
# onnxTransformerNHWC = true


# OpenCL-specific GPU settings--------------------------------------
# These only apply when using the OpenCL version of KataGo.

# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size
# openclReTunePerBoardSize = true

# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly.
# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess.
# openclDeviceToUse = 0

# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use.
# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when
# KataGo starts up - it should print or log all the devices it finds.
# (AND also set numNNServerThreadsPerModel above)
# openclDeviceToUseThread0 = X
# openclDeviceToUseThread1 = Y

# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use.
# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when
# KataGo starts up - it should print or log all the devices it finds.
# (AND also set numNNServerThreadsPerModel above)
# openclDeviceToUseThread0 = X
# openclDeviceToUseThread1 = Y
# openclDeviceToUseThread2 = Z

# You can probably guess the pattern if you have four, five, etc. GPUs.

# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you
# want to try to force a particular behavior though you can uncomment this lines and change it
# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable
# by rerunning the tuner with various arguments.
# openclUseFP16 = auto


# Eigen-specific settings--------------------------------------
# These only apply when using the Eigen (pure CPU) version of KataGo.

# This is the number of CPU threads for evaluating the neural net on the Eigen backend.
# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores).
# numEigenThreadsPerModel = X


# Misc Behavior --------------------

# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko.
# Uncomment and set to false to disable this.
# rootSymmetryPruning = true

# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate,
# and also to improve opening diversity versus some particular other bots that like to play it all the time.
# avoidMYTDaggerHack = false

# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board.
# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change.
# avoidRepeatedPatternUtility = 0.0

# Enable some hacks that mitigate rare instances when passing messes up deeper searches.
# enablePassingHacks = true

# Root move selection and biases------------------------------------------------------------------------------
# Uncomment and edit any of the below values to change them from their default.
# Not all of these parameters are applicable to analysis, some are only used for actual play

# Temperature for the early game, randomize between chosen moves with this temperature
# chosenMoveTemperatureEarly = 0.5
# Decay temperature for the early game by 0.5 every this many moves, scaled with board size.
# chosenMoveTemperatureHalflife = 19
# At the end of search after the early game, randomize between chosen moves with this temperature
# chosenMoveTemperature = 0.10
# Subtract this many visits from each move prior to applying chosenMoveTemperature
# (unless all moves have too few visits) to downweight unlikely moves
# chosenMoveSubtract = 0
# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above
# chosenMovePrune = 1

# Number of symmetries to sample (WITHOUT replacement) and average at the root
# rootNumSymmetriesToSample = 1

# Using LCB for move selection?
# useLcbForSelection = true
# How many stdevs a move needs to be better than another for LCB selection
# lcbStdevs = 5.0
# Only use LCB override when a move has this proportion of visits as the top move
# minVisitPropForLCB = 0.15

# Internal params------------------------------------------------------------------------------
# Uncomment and edit any of the below values to change them from their default.

# Scales the utility of winning/losing
# winLossUtilityFactor = 1.0
# Scales the utility for trying to maximize score
# staticScoreUtilityFactor = 0.10
# dynamicScoreUtilityFactor = 0.30
# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount.
# dynamicScoreCenterZeroWeight = 0.20
# dynamicScoreCenterScale = 0.75
# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1)
# noResultUtilityForWhite = 0.0
# The number of wins that a draw counts as, for white. (0 to 1)
# drawEquivalentWinsForWhite = 0.5

# Exploration constant for mcts
# cpuctExploration = 1.0
# cpuctExplorationLog = 0.45

# Parameters that control exploring more in volatile positions, exploring less in stable positions.
# cpuctUtilityStdevPrior = 0.40
# cpuctUtilityStdevPriorWeight = 2.0
# cpuctUtilityStdevScale = 0.85

# FPU reduction constant for mcts
# fpuReductionMax = 0.2
# rootFpuReductionMax = 0.1
# fpuParentWeightByVisitedPolicy = true

# Parameters that control weighting of evals based on the net's own self-reported uncertainty.
# useUncertainty = true
# uncertaintyExponent = 1.0
# uncertaintyCoeff = 0.25

# Explore using optimistic policy
# rootPolicyOptimism = 0.2
# policyOptimism = 1.0

# Amount to apply a downweighting of children with very bad values relative to good ones
# valueWeightExponent = 0.25

# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame,
# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of
# points but a bit more unfriendly to humans.
# rootEndingBonusPoints = 0.5

# Make the bot prune useless moves that are just prolonging the game to avoid losing yet
# rootPruneUselessMoves = true

# Apply bias correction based on local pattern keys
# subtreeValueBiasFactor = 0.45
# subtreeValueBiasWeightExponent = 0.85

# Use graph search rather than tree search - identify and share search for transpositions.
# useGraphSearch = true

# How much to shard the node table for search synchronization
# nodeTableShardsPowerOfTwo = 16
# How many virtual losses to add when a thread descends through a node
# numVirtualLossesPerThread = 1

# Improve the quality of evals under heavy multithreading
# useNoisePruning = true


# Avoid SGF Patterns ------------------------------------------------------------------------------
# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns
# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature.
# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose.

# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted)
# avoidSgfPatternDirs = path/to/directory/with/sgfs/
# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs.
# Within double quotes, backslashes are escape characters.
# avoidSgfPatternDirs = "path/to/directory/with/sgfs/"

# Penalize this much utility per matching move.
# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it!
# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play.
# avoidSgfPatternUtility = 0.001

# Optional - load only the newest this many files
# avoidSgfPatternMaxFiles = 20

# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones.
# avoidSgfPatternLambda = 0.90

# Optional - pay attention only to moves that were made by players with this name.
# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating
# moves that itself made in past games, not the moves that its opponents made.
# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2

# Optional - Ignore any moves in SGF files that occurred before this turn number.
# avoidSgfPatternMinTurnNumber = 0

# For more avoid patterns:
# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,...
# avoidSgf2PatternDirs = ...
# avoidSgf2PatternUtility = ...
# avoidSgf2PatternMaxFiles = ...
# avoidSgf2PatternLambda = ...
# avoidSgf2PatternAllowedNames = ...
# avoidSgf2PatternMinTurnNumber = ...