def rl_loop(): # monkeypatch the hyperparams so that we get a quickly executing network. dual_net.get_default_hyperparams = lambda **kwargs: { 'k': 1, 'fc_width': 2, 'num_shared_layers': 1, 'l2_strength': 2e-4, 'momentum': 0.9} with tempfile.TemporaryDirectory() as base_dir: model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap') selfplay_dir = os.path.join(base_dir, 'data', 'selfplay') model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap', 'worker1') gather_dir = os.path.join(base_dir, 'data', 'training_chunks') sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap') os.mkdir(os.path.join(base_dir, 'data')) print("Creating random initial weights...") dual_net.DualNetworkTrainer(model_save_file).bootstrap() print("Playing some games...") # Do two selfplay runs to test gather functionality main.selfplay( load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, readouts=10) main.selfplay( load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, readouts=10) print("Gathering game output...") main.gather(input_directory=selfplay_dir, output_directory=gather_dir) print("Training on gathered game data... (ctrl+C to quit)") main.train(gather_dir, save_file=model_save_file, num_steps=10000)
def rl_loop(): """Run the reinforcement learning loop This is meant to be more of an integration test than a realistic way to run the reinforcement learning. """ # monkeypatch the hyperparams so that we get a quickly executing network. dual_net.get_default_hyperparams = lambda **kwargs: { 'k': 8, 'fc_width': 16, 'num_shared_layers': 1, 'l2_strength': 1e-4, 'momentum': 0.9 } dual_net.TRAIN_BATCH_SIZE = 16 #monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions. preprocessing.SHUFFLE_BUFFER_SIZE = 10000 with tempfile.TemporaryDirectory() as base_dir: model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap') selfplay_dir = os.path.join(base_dir, 'data', 'selfplay') model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap') gather_dir = os.path.join(base_dir, 'data', 'training_chunks') sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap') os.mkdir(os.path.join(base_dir, 'data')) print("Creating random initial weights...") dual_net.DualNetworkTrainer(model_save_file).bootstrap() print("Playing some games...") # Do two selfplay runs to test gather functionality main.selfplay(load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) main.selfplay(load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) print("Gathering game output...") main.gather(input_directory=selfplay_dir, output_directory=gather_dir) print("Training on gathered game data... (ctrl+C to quit)") main.train(gather_dir, save_file=model_save_file, num_steps=10000, logdir="logs", verbosity=2)
def rl_loop(): # monkeypatch the hyperparams so that we get a quickly executing network. dual_net.get_default_hyperparams = lambda **kwargs: { 'k': 8, 'fc_width': 16, 'num_shared_layers': 1, 'l2_strength': 1e-4, 'momentum': 0.9} dual_net.TRAIN_BATCH_SIZE = 16 #monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions. preprocessing.SHUFFLE_BUFFER_SIZE = 10000 with tempfile.TemporaryDirectory() as base_dir: model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap') selfplay_dir = os.path.join(base_dir, 'data', 'selfplay') model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap') gather_dir = os.path.join(base_dir, 'data', 'training_chunks') sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap') os.mkdir(os.path.join(base_dir, 'data')) print("Creating random initial weights...") dual_net.DualNetworkTrainer(model_save_file).bootstrap() print("Playing some games...") # Do two selfplay runs to test gather functionality main.selfplay( load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) main.selfplay( load_file=model_save_file, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) print("Gathering game output...") main.gather(input_directory=selfplay_dir, output_directory=gather_dir) print("Training on gathered game data... (ctrl+C to quit)") main.train(gather_dir, save_file=model_save_file, num_steps=10000, logdir="logs", verbosity=2)
def gather(): print("Gathering game output...") main.gather(input_directory=SELFPLAY_DIR, output_directory=TRAINING_CHUNK_DIR)
def rl_loop(): """Run the reinforcement learning loop This is meant to be more of an integration test than a realistic way to run the reinforcement learning. """ # monkeypatch the hyperparams so that we get a quickly executing network. dual_net.get_default_hyperparams = lambda **kwargs: { 'k': 8, 'fc_width': 16, 'num_shared_layers': 1, 'l2_strength': 1e-4, 'momentum': 0.9 } dual_net.TRAIN_BATCH_SIZE = 16 dual_net.EXAMPLES_PER_GENERATION = 64 #monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions. preprocessing.SHUFFLE_BUFFER_SIZE = 1000 # with tempfile.TemporaryDirectory() as base_dir: base_dir = "/tmp/minigo" with open('/tmp/foo', 'w') as fff: working_dir = os.path.join(base_dir, 'models_in_training') model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap') next_model_save_file = os.path.join(base_dir, 'models', '000001-nextmodel') selfplay_dir = os.path.join(base_dir, 'data', 'selfplay') model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap') gather_dir = os.path.join(base_dir, 'data', 'training_chunks') holdout_dir = os.path.join(base_dir, 'data', 'holdout', '000000-bootstrap') sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap') os.makedirs(os.path.join(base_dir, 'data'), exist_ok=True) print("Creating random initial weights...") main.bootstrap(working_dir, model_save_path) for i in range(100): qmeas.start_time('main-loop') print("Playing some games...") # Do two selfplay runs to test gather functionality qmeas.start_time('main-loop-self-play') for j in range(2): main.selfplay(load_file=model_save_path, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) qmeas.stop_time('main-loop-self-play') # Do one holdout run to test validation qmeas.start_time('main-loop-self-play-holdout') main.selfplay(load_file=model_save_path, holdout_dir=holdout_dir, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=100, readouts=10) qmeas.stop_time('main-loop-self-play-holdout') print("See sgf files here?") sgf_listing = subprocess.check_output( ["ls", "-l", sgf_dir + "/full"]) print(sgf_listing.decode("utf-8")) print("Gathering game output...") qmeas.start_time('main-loop-gather') main.gather(input_directory=selfplay_dir, output_directory=gather_dir) qmeas.stop_time('main-loop-gather') print("Training on gathered game data...") qmeas.start_time('main-loop-train') main.train(working_dir, gather_dir, next_model_save_file, generation_num=1) qmeas.stop_time('main-loop-train') print("Trying validate on 'holdout' game...") qmeas.start_time('main-loop-validate') main.validate(working_dir, holdout_dir) qmeas.stop_time('main-loop-validate') print("Verifying that new checkpoint is playable...") main.selfplay(load_file=next_model_save_file, holdout_dir=holdout_dir, output_dir=model_selfplay_dir, output_sgf=sgf_dir, readouts=10) qmeas.stop_time('main-loop') qmeas._flush()
def gather(): print("Gathering game output...") main.gather(input_directory=SELFPLAY_DIR, output_directory=TRAINING_CHUNK_DIR)
def rl_loop(): """Run the reinforcement learning loop This is meant to be more of an integration test than a realistic way to run the reinforcement learning. """ # monkeypatch the hyperparams so that we get a quickly executing network. dual_net.get_default_hyperparams = lambda **kwargs: { 'k': 8, 'fc_width': 16, 'num_shared_layers': 1, 'l2_strength': 1e-4, 'momentum': 0.9} dual_net.TRAIN_BATCH_SIZE = 16 dual_net.EXAMPLES_PER_GENERATION = 64 #monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions. preprocessing.SHUFFLE_BUFFER_SIZE = 1000 with tempfile.TemporaryDirectory() as base_dir: working_dir = os.path.join(base_dir, 'models_in_training') model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap') next_model_save_file = os.path.join(base_dir, 'models', '000001-nextmodel') selfplay_dir = os.path.join(base_dir, 'data', 'selfplay') model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap') gather_dir = os.path.join(base_dir, 'data', 'training_chunks') holdout_dir = os.path.join( base_dir, 'data', 'holdout', '000000-bootstrap') sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap') os.makedirs(os.path.join(base_dir, 'data'), exist_ok=True) print("Creating random initial weights...") main.bootstrap(working_dir, model_save_path) print("Playing some games...") # Do two selfplay runs to test gather functionality main.selfplay( load_file=model_save_path, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) main.selfplay( load_file=model_save_path, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0, readouts=10) # Do one holdout run to test validation main.selfplay( load_file=model_save_path, holdout_dir=holdout_dir, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=100, readouts=10) print("See sgf files here?") sgf_listing = subprocess.check_output(["ls", "-l", sgf_dir + "/full"]) print(sgf_listing.decode("utf-8")) print("Gathering game output...") main.gather(input_directory=selfplay_dir, output_directory=gather_dir) print("Training on gathered game data...") main.train(working_dir, gather_dir, next_model_save_file, generation_num=1) print("Trying validate on 'holdout' game...") main.validate(working_dir, holdout_dir) print("Verifying that new checkpoint is playable...") main.selfplay( load_file=next_model_save_file, holdout_dir=holdout_dir, output_dir=model_selfplay_dir, output_sgf=sgf_dir, readouts=10)