Optimize settings / smaller boardsize kata / fix error message / teac…

…her improvements

Optimize settings / smaller boardsize kata / fix error message / teacher improvements

KataGo/analysis_config.cfg

@@ -55,14 +55,14 @@ maxVisits = 500
 # number of visits, and thread contention will reduce efficiency, so cross-position parallelization is preferable
 # to numSearchThreads, but numSearchThreads is preferable if you want to reduce latency, and have individual
 # searches complete faster by doing fewer of them at a time.
-numSearchThreads = 2
+numSearchThreads = 6
 
 # GPU Settings-------------------------------------------------------------------------------
 
 # Maximum number of positions to send to GPU at once.
-nnMaxBatchSize = 128
+nnMaxBatchSize = 64
 # Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree.
-nnCacheSizePowerOfTwo = 23
+nnCacheSizePowerOfTwo = 21
 # Size of mutex pool for nnCache is 2 ** this
 nnMutexPoolSizePowerOfTwo = 17
 # Randomize board orientation when running neural net evals?

README.md

@@ -39,17 +39,25 @@ but has since grown to include a wide range of features, including:
 * Execute the command `pip install kivy_deps.glew kivy_deps.sdl2 kivy_deps.gstreamer kivy`
 * Start the app by running `python katrain.py` in the directory where you downloaded the scripts. Note that the program can be slow to initialize the first time, due to kata's gpu tuning.
 
-### Installation for Linux/Mac users
+### Installation for Linux users
 
 * This assumed you have a working Python 3.6/3.7 installation as a default. If your default is python 2, use pip3/python3. Kivy currently does not have a release for Python 3.8.
 * Git clone or download the repository.
-* `pip install kivy`
-* A binary for KataGo is included, but if you have compiled your own, point the 'engine/katago' setting to the relevant KataGo v1.3.5+ binary.
+* Run the command `pip install kivy` in the terminal.
+* A binary for KataGo is included, but if you have compiled your own, point the 'engine/katago' setting to the relevant KataGo v1.3.5-bs29+ binary.
 * Start the app by running `python katrain.py`.  Note that the program can be slow to initialize the first time, due to KataGo's GPU tuning.
-
+
+### Installation for MacOS users
+
+* Git clone or download the repository.
+* Run the command `pip install kivy` in the terminal.
+* Follow instructions [here](https://github.com/lightvector/KataGo) to compile KataGo yourself -- note that the version required is currently too new for the 'brew' method.
+* Start the app by running `python katrain.py`, change the path of the 'katago' setting to the path where you compiled it, and click 'Apply and Save'.
+
 ## Manual
 
 ### Play
+
 Under the 'play' tab you can select who is playing black and white.
 * Human is simple play with potential feedback, but without auto-undo.
 * Teach will give you instant feedback, and auto-undo bad moves to give you a second chance. 
@@ -72,6 +80,7 @@ In short, if you are a weaker player you should mostly on large dots that are re
 while stronger players can pay more attention to smaller mistakes.
 
 #### AIs
+
 Available AIs, with strength indicating an estimate for the default settings, are:
 
 * **[9p+]** **Default** is full KataGo, above professional level. 
@@ -107,7 +116,6 @@ Keyboard shortcuts are shown with **[key]**.
     * **[s]**: Equalize: Re-evaluate all currently shown next moves with the same visits as the current top move. Useful to increase confidence in the suggestions with high uncertainty.
     * **[d]**: Sweep: Evaluate all possible next moves. This can take a bit of time even though 'fast_visits' is used, but the result is nothing if not colourful.
 
-
 ## Keyboard and mouse shortcuts
 
 In addition to shortcuts mentioned above, there are:
@@ -128,7 +136,6 @@ In addition to shortcuts mentioned above, there are:
 * **[Ctrl-n]**: Load SGF from clipboard
 * **[space]**: Pass
 
-
 ## Configuration
 
 Configuration is stored in `config.json`. Most settings are now available to edit in the program, but some advanced options are not.
@@ -165,11 +172,12 @@ If you ever need to reset to the original settings, simply re-download the `conf
   * The first startup of KataGo can be slow due to GPU tuning, after that it should be much faster.
 * The program is running too slowly. How can I speed it up?
   *  Adjust the number of visits or maximum time allowed in the settings.
-* The engine crashes with out of memory errors
-  * Try setting `nnMaxBatchSize` in `KataGo/analysis_config.cfg` to something lower.
+* KataGo crashes with out of memory errors, how can I prevent this?
+  *  Try using a lower number for `nnMaxBatchSize` in `KataGo/analysis_config.cfg`, and avoid using versions compiled with large board sizes.
 
 ## Contributing
 
-* Feedback and pull requests are both very welcome.
+* Feedback and pull requests are both very welcome. I would also be happy to host translations of this manual into languages where English fluency is typically lower.
 * For suggestions and planned improvements, see the 'issues' tab on github.
-* You can also contact me on discord (Sander#3278) or [reddit](http://reddit.com/u/sanderbaduk) to give feedback, or simply show your appreciation.
+* You can also contact me on discord (Sander#3278), [KakaoTalk](https://open.kakao.com/o/gTsMJCac) or [Reddit](http://reddit.com/u/sanderbaduk) to give feedback, or simply show your appreciation.
+* Some people have also asked me how to donate. Something go-related such as a book or teaching time is highly appreciated.

bots/ai2gtp.py

@@ -75,7 +75,7 @@ def malkovich_analysis(cn):
         logger.log(f"dscore {dscore} = {cn.analysis['root']['scoreLead']} {cn.parent.analysis['root']['scoreLead']} at {move}...", OUTPUT_ERROR)
         if abs(dscore) > REPORT_SCORE_THRESHOLD and (cn.player == "B" and dscore < 0 or cn.player == "W" and dscore > 0):  # relevant mistakes
             favpl = "B" if dscore > 0 else "W"
-            msg = f"MALKOVICH:{cn.player} {cn.single_move.gtp()} caused a significant score change ({favpl} gained {abs(dscore):.1f} points)"
+            msg = f"MALKOVICH:{cn.player} {cn.move.gtp()} caused a significant score change ({favpl} gained {abs(dscore):.1f} points)"
             if cn.ai_thoughts:
                 msg += f" -> Win Rate {cn.format_win_rate()} Score {cn.format_score()} AI Thoughts: {cn.ai_thoughts}"
             else:
@@ -139,7 +139,7 @@ def malkovich_analysis(cn):
             if not pol:
                 pol = ["??"]
             print(f"DISCUSSION:OK, since you passed {MAX_PASS} times after the {bx+by}th move, I will pass as well [policy {pol[-1]:.3%}].", file=sys.stderr)
-            move = game.play(Move(None, player=game.next_player)).single_move
+            move = game.play(Move(None, player=game.next_player)).move
         else:
             move, node = ai_move(game, ai_strategy, ai_settings)
             if node is None:

bots/engine_server.py

@@ -18,7 +18,7 @@
     "max_visits": 50,
     "max_time": 1.0,
     "enable_ownership": False,
-    "threads": 8,
+    "threads": 32,
 }
 
 

bots/selfplay.py

@@ -41,7 +41,7 @@ class AI:
         "max_time": 300.0,
         "_enable_ownership": False,
     }
-    NUM_THREADS = 8
+    NUM_THREADS = 32
     IGNORE_SETTINGS_IN_TAG = {"threads", "_enable_ownership", "katago"}  # katago for switching from/to bs version
     ENGINES = []
     LOCK = threading.Lock()

bots/settings.py

@@ -1,5 +1,6 @@
 bot_strategy_names = {
     "dev": "P:Noise",
+    "dev-beta": "P:Weighted",
     "strong": "Policy",
     "influence": "P:Influence",
     "territory": "P:Territory",
@@ -11,7 +12,8 @@
 
 
 greetings = {
-    "dev": "Experimental!",
+    "dev": "Policy+Dirichlet noise.",
+    "dev-beta": "Play a policy-weighted move.",
     "strong": "Play top policy move.",
     "influence": "Play an influential style.",
     "territory": "Play a territorial style.",

bots/start_bot.py

@@ -15,7 +15,10 @@
 else:
     GTP2OGS = "node ../stable-gtp2ogs"
 BOT_SETTINGS = f" --maxconnectedgames {MAXGAMES} --maxhandicapunranked 25 --maxhandicapranked 1 --boardsizesranked 19 --boardsizesunranked all --komisranked automatic,5.5,6.5,7.5 --komisunranked all"
-
+if "beta" in bot:
+    BOT_SETTINGS += " --beta"
+else:
+    BOT_SETTINGS += ""  # --rankedonly"
 
 username = f"katrain-{bot}"
 
@@ -40,6 +43,6 @@
     GREETING += f" Settings: {settings_dump}."
 BYEMSG = "Thank you for playing. If you have any feedback, please message my admin! Play with these bots at any time by downloading KaTrain at github.com/sanderland/katrain"
 
-cmd = f'{GTP2OGS} --debug --apikey {APIKEY} --username {username} --greeting "{GREETING}" --farewell "{BYEMSG}"  {BOT_SETTINGS} --farewellscore --aichat --noclock --nopause --speeds blitz,live  --persist --minrank 25k  -- python bots/ai2gtp.py {bot} {port}'
+cmd = f'{GTP2OGS} --debug --apikey {APIKEY} --rejectnewfile ~/shutdown_bots --username {username} --greeting "{GREETING}" --farewell "{BYEMSG}"  {BOT_SETTINGS} --farewellscore --aichat --noclock --nopause --speeds blitz,live  --persist --minrank 25k  -- python bots/ai2gtp.py {bot} {port}'
 print(f"starting bot {username} using server port {port} --> {cmd}")
 os.system(cmd)

config.json

@@ -1,9 +1,9 @@
 {
     "engine": {
-        "katago": "KataGo/katago-bs",
+        "katago": "KataGo/katago",
         "model": "KataGo/models/b15-1.3.2.txt.gz",
         "config": "KataGo/analysis_config.cfg",
-        "threads": 8,
+        "threads": 16,
         "max_visits": 500,
         "fast_visits": 50,
         "max_time": 3.0,
@@ -43,7 +43,8 @@
             0
         ],
         "eval_off_show_last": 3,
-        "eval_show_ai": true
+        "eval_show_ai": true,
+        "lock_ai": false
     },
     "ai": {
         "Default": {
@@ -98,7 +99,7 @@
             "_help_right": "Lower `stddev` makes it prefer closer moves."
         },
         "P:Tenuki": {
-            "pick_override": 0.9,
+            "pick_override": 0.85,
             "stddev": 7.5,
             "pick_n": 5,
             "pick_frac": 0.7,

core/ai.py

@@ -99,11 +99,11 @@ def ai_move(game: Game, ai_mode: str, ai_settings: Dict) -> Tuple[Move, GameNode
                 ai_thoughts += f"Generated weights for {ai_mode} according to weight factor {ai_settings['line_weight']} and distance from {thr_line+1}th line. "
             elif "local" in ai_mode or "tenuki" in ai_mode:
                 var = ai_settings["stddev"] ** 2
-                if not cn.single_move or cn.single_move.coords is None:
+                if not cn.move or cn.move.coords is None:
                     weighted_coords = [(1, 1, *top_policy_move.coords)]  # if "pick" in ai_mode -> even
                     ai_thoughts += f"No previous non-pass move, faking weights to play top policy move. "
                 else:
-                    mx, my = cn.single_move.coords
+                    mx, my = cn.move.coords
                     weighted_coords = [
                         (policy_grid[y][x], math.exp(-0.5 * ((x - mx) ** 2 + (y - my) ** 2) / var), x, y) for x in range(size[0]) for y in range(size[1]) if policy_grid[y][x] > 0
                     ]

core/engine.py

@@ -43,9 +43,9 @@ def start(self):
         try:
             self.katrain.log(f"Starting KataGo with {self.command}", OUTPUT_DEBUG)
             self.katago_process = subprocess.Popen(self.command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
-        except FileNotFoundError:
+        except FileNotFoundError as e:
             self.katrain.log(
-                f"Starting kata with command '{self.command}' failed. If you are on Mac or Linux, please change the settings or configuration file (config.json) to point to the correct KataGo executable.",
+                f"Starting kata with command '{self.command}' failed with error {e}. Please make sure the 'katago' value under 'engine' in settings points to the correct KataGo executable.",
                 OUTPUT_ERROR,
             )
 

core/game.py

@@ -265,12 +265,15 @@ def analyze_extra(self, mode):
         elif mode == "sweep":
             board_size_x, board_size_y = self.board_size
             policy_grid = var_to_grid(self.current_node.policy, size=(board_size_x, board_size_y)) if self.current_node.policy else None
-            analyze_moves = [
-                Move(coords=(x, y), player=cn.next_player)
-                for x in range(board_size_x)
-                for y in range(board_size_y)
-                if (policy_grid is None and (x, y) not in stones) or policy_grid[y][x] >= 0
-            ]
+            analyze_moves = sorted(
+                [
+                    Move(coords=(x, y), player=cn.next_player)
+                    for x in range(board_size_x)
+                    for y in range(board_size_y)
+                    if (policy_grid is None and (x, y) not in stones) or policy_grid[y][x] >= 0
+                ],
+                key=lambda mv: -policy_grid[mv.coords[1]][mv.coords[0]],
+            )
             visits = engine.config["fast_visits"]
             self.katrain.controls.set_status(f"Refining analysis of entire board to {visits} visits")
             priority = -1_000_000_000
@@ -283,7 +286,7 @@ def analyze_extra(self, mode):
             cn.analyze(engine, priority, visits=visits, refine_move=move, time_limit=False)  # explicitly requested so take as long as you need
 
     def analyze_undo(self, node, train_config):
-        move = node.single_move
+        move = node.move
         if node != self.current_node or node.auto_undo is not None or not node.analysis_ready or not move:
             return
         points_lost = node.points_lost
@@ -306,5 +309,5 @@ def analyze_undo(self, node, train_config):
         node.auto_undo = undo
         if undo:
             self.undo(1)
-            self.katrain.controls.set_status(f"Undid move {move.gtp()} as it lost {points_lost:.1f} points{xmsg}")
+            self.katrain.controls.set_status(f"Undid move {move.gtp()} as it lost {points_lost:.1f} points{xmsg}. Hover over the move to see expected refutation.")
             self.katrain.update_state()

core/game_node.py

@@ -86,8 +86,14 @@ def format_win_rate(self, win_rate=None):
         if win_rate is not None:
             return f"{'B' if win_rate > 0.5 else 'W'} {max(win_rate,1-win_rate):.1%}"
 
-    def comment(self, sgf=False, teach=False, hints=False):
-        single_move = self.single_move
+    def make_pv(self, player, pv, interactive):
+        pvtext = f"{player}{' '.join(pv)}"
+        # if interactive:
+        #    pvtext = f"[u][ref={pvtext}][color=#334466]{pvtext}[/color][/ref][/u]"
+        return pvtext
+
+    def comment(self, sgf=False, teach=False, hints=False, interactive=False):
+        single_move = self.move
         if not self.parent or not single_move:  # root
             return ""
 
@@ -109,7 +115,7 @@ def comment(self, sgf=False, teach=False, hints=False):
                         text += f"Move was predicted best move.\n"
                     if sgf:
                         if previous_top_move.get("pv") and (sgf or hints):
-                            text += f"PV: {single_move.player}{' '.join(previous_top_move['pv'])}\n"
+                            text += f"PV: {self.make_pv(single_move.player,previous_top_move['pv'],interactive)}\n"
 
                 if sgf or hints or teach:
                     policy_ranking = self.parent.policy_ranking
@@ -131,15 +137,15 @@ def comment(self, sgf=False, teach=False, hints=False):
 
     @property
     def points_lost(self) -> Optional[float]:
-        single_move = self.single_move
+        single_move = self.move
         if single_move and self.parent and self.analysis_ready and self.parent.analysis_ready:
             parent_score = self.parent.score
             score = self.score
             return self.player_sign(single_move.player) * (parent_score - score)
 
     @property
     def parent_realized_points_lost(self) -> Optional[float]:
-        single_move = self.single_move
+        single_move = self.move
         if single_move and self.parent and self.parent.parent and self.analysis_ready and self.parent.parent.analysis_ready:
             parent_parent_score = self.parent.parent.score
             score = self.score