def testBootstrap(data): print("\nBOOTSTRAP 1:") (treino, teste) = bootstrap(data) print("treino: " + str(len(treino)) + "\n" + str(treino)) print("teste: " + str(len(teste)) + "\n" + str(teste)) print("\nBOOTSTRAP 2:") (treino, teste) = bootstrap(data) print("treino: " + str(len(treino)) + "\n" + str(treino)) print("teste: " + str(len(teste)) + "\n" + str(teste))
help="Add New Upstream Block", metavar=("")) parser.add_argument("--ssl", help="Add New Upstream Block", type=bool, metavar=("")) args = parser.parse_args() params = { "domain": args.domain, "block": args.block, "prev_value": args.prev_value, "new_value": args.new_value, "directive": args.directive, "attribute": args.attribute, "config": f"/etc/nginx/conf.d/{args.domain}.conf", } start_time = time.time() main.bootstrap() if not args.block: exit("Block Argu is not passed") if not args.domain: exit("domain Argu is not passed") if not args.prev_value: exit("prev_value Argu is not passed") if not args.new_value: exit("new_value Argu is not passed") if not args.directive and args.block == "server": exit("directive Argu is not passed") if not args.attribute and args.block == "server": exit("attribute Argu is not passed") if not args.new_subdomain: if args.block.lower() == "server":
def bootstrap(working_dir): bootstrap_name = shipname.generate(0) bootstrap_model_path = os.path.join(fsdb.models_dir(), bootstrap_name) print("Bootstrapping with working dir {}\n Model 0 exported to {}".format( working_dir, bootstrap_model_path)) main.bootstrap(working_dir, bootstrap_model_path)
def bootstrap(): bootstrap_name = shipname.generate(0) bootstrap_model_path = os.path.join(MODELS_DIR, bootstrap_name) print("Bootstrapping model at {}".format(bootstrap_model_path)) main.bootstrap(bootstrap_model_path)
def bootstrap(): bootstrap_name = shipname.generate(0) bootstrap_model_path = os.path.join(MODELS_DIR, bootstrap_name) print("Bootstrapping with working dir {}\n Model 0 exported to {}".format( ESTIMATOR_WORKING_DIR, bootstrap_model_path)) main.bootstrap(ESTIMATOR_WORKING_DIR, bootstrap_model_path)
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 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. """ # TODO(brilee): move these all into appropriate local_flags file. # monkeypatch the hyperparams so that we get a quickly executing network. flags.FLAGS.conv_width = 8 flags.FLAGS.fc_width = 16 flags.FLAGS.trunk_layers = 1 flags.FLAGS.train_batch_size = 16 flags.FLAGS.shuffle_buffer_size = 1000 dual_net.EXAMPLES_PER_GENERATION = 64 flags.FLAGS.num_readouts = 10 with tempfile.TemporaryDirectory() as base_dir: flags.FLAGS.base_dir = base_dir working_dir = os.path.join(base_dir, 'models_in_training') flags.FLAGS.model_dir = working_dir model_save_path = os.path.join(base_dir, 'models', '000000-bootstrap') local_eb_dir = os.path.join(base_dir, 'scratch') 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) main.selfplay(load_file=model_save_path, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0) # 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) 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...") eb.make_chunk_for(output_dir=gather_dir, local_dir=local_eb_dir, game_dir=selfplay_dir, model_num=1, positions=dual_net.EXAMPLES_PER_GENERATION, threads=8, samples_per_game=200) print("Training on gathered game data...") main.train_dir(gather_dir, next_model_save_file) 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)
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. """ # TODO(brilee): move these all into appropriate local_flags file. # 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 flags.FLAGS.num_readouts = 10 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') local_eb_dir = os.path.join(base_dir, 'scratch') 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) main.selfplay(load_file=model_save_path, output_dir=model_selfplay_dir, output_sgf=sgf_dir, holdout_pct=0) # 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) 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...") eb.make_chunk_for(output_dir=gather_dir, local_dir=local_eb_dir, game_dir=selfplay_dir, model_num=1, positions=dual_net.EXAMPLES_PER_GENERATION, threads=8, samples_per_game=200) print("Training on gathered game data...") main.train_dir(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)
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)