def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption(
'backprop',
'Whether to backprop the total loss',
True)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption('num_minibatch', 'number of minibatches', 5000)
spec.addIntOption('num_episode', 'number of episodes', 10000)
spec.addIntOption('num_process', 'number of processes', 2)
spec.addBoolOption('tqdm', 'toggle tqdm visualization', False)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption('bn', 'toggles batch norm', True)
spec.addBoolOption('leaky_relu', 'toggles leaky ReLU', True)
spec.addIntOption('num_layer', 'number of layers', 39)
spec.addIntOption('dim', 'model dimension', 128)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrListOption(
'additional_labels',
'add additional labels in the batch; e.g. id, seq, last_terminal',
[])
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addFloatOption(
'discount',
'exponential discount rate',
0.99)
return spec
def get_option_spec(cls, stats_name=''):
spec = PyOptionSpec()
spec.addStrOption(
stats_name + '_stats',
'type of stat to report (rewards or winrate)',
'')
return spec
def get_option_spec(cls):
# print("\u001b[31;1m|py|\u001b[0m\u001b[37m", "MCTSPrediction::", inspect.currentframe().f_code.co_name)
spec = PyOptionSpec()
spec.addBoolOption('backprop', 'Whether to backprop the total loss',
True)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption('bn', 'toggles batch norm', True)
spec.addBoolOption('leaky_relu', 'toggles leaky ReLU', False)
spec.addFloatOption('bn_momentum',
'batch norm momentum (pytorch style)', 0.1)
spec.addIntOption('dim', 'model dimension', 128)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'loglevel',
('Global log level. Choose from '
'trace, debug, info, warning, error, critical, or off)'),
'info')
return spec
def get_option_spec(cls, name='eval'):
spec = PyOptionSpec()
spec.addStrListOption('keys_in_reply', 'keys in reply', [])
spec.addIntOption('num_minibatch', 'number of minibatches', 5000)
spec.addStrListOption('parsed_args', 'dummy option', [])
spec.merge(Stats.get_option_spec(name))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'num_block',
'number of resnet blocks',
20)
spec.merge(Block.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption('num_minibatch', 'number of minibatches', 5000)
spec.addIntOption(
'num_cooldown',
'Last #minibatches to refresh running mean/std for batchnorm '
'in addition to the training stage', 0)
spec.addIntOption('num_episode', 'number of episodes', 10000)
spec.addBoolOption('tqdm', 'toggle tqdm visualization', False)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'num_eval',
'number of games to evaluate',
500)
spec.addBoolOption(
'tqdm',
'toggle tqdm visualization',
False)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addFloatOption(
'grad_clip_norm',
'gradient norm clipping',
0.0)
spec.addStrOption(
'value_node',
'name of the value node',
'V')
return spec
def get_option_spec(cls, name='eval'):
# print("\u001b[31;1m|py|\u001b[0m\u001b[37m", "Evaluator::", inspect.currentframe().f_code.co_name)
# print("\u001b[31;1m", os.path.dirname(os.path.abspath(__file__)), " - ", os.path.basename(__file__), "\u001b[0m")
spec = PyOptionSpec()
spec.addStrListOption('keys_in_reply', 'keys in reply', [])
spec.addIntOption('num_minibatch', 'number of minibatches', 5000)
spec.addStrListOption('parsed_args', 'dummy option', '')
spec.merge(Stats.get_option_spec(name))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'sample_policy',
'choices of epsilon-greedy, multinomial, or uniform',
'epsilon-greedy')
spec.addBoolOption('store_greedy',
('if enabled, picks maximum-probability action; '
'otherwise, sample from distribution'), False)
spec.addFloatOption('epsilon', 'used in epsilon-greedy', 0.0)
spec.addStrListOption('sample_nodes', 'nodes to be sampled and saved',
['pi,a'])
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption('bn', 'toggles batch norm', True)
spec.addBoolOption('leaky_relu', 'toggles leaky ReLU', False)
spec.addIntOption('gpu', 'which gpu to use', -1)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption('a_node', 'action node', 'a')
spec.addStrOption('q_node', 'Q node', 'Q')
spec.merge(DiscountedReward.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption('value_node', 'name of the value node', 'V')
spec.addFloatOption('adv_clip',
'clip value of advantage. 0.0 means no clipping',
0.0)
spec.merge(
PyOptionSpec.fromClasses(
(PolicyGradient, DiscountedReward, ValueMatcher)))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
elf_C.setSpecELFOptions(spec.getOptionSpec())
test.setSpecTSOptions(spec.getOptionSpec())
spec.addIntOption(
'gpu',
'GPU id to use',
0)
spec.addStrOption(
'load',
'Load old model',
"")
spec.addStrListOption(
'parsed_args',
'dummy option',
[])
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption('value_node', 'name of the value node', 'V')
spec.merge(
PyOptionSpec.fromClasses(
(PolicyGradient, DiscountedReward, ValueMatcher)))
return spec
def get_option_spec(cls, name='eval'):
spec = PyOptionSpec()
spec.addStrListOption(
'keys_in_reply',
'keys in reply',
[])
spec.addIntOption(
'num_minibatch',
'number of minibatches',
5000)
spec.addStrListOption(
'parsed_args',
'dummy option',
'')
spec.merge(Stats.get_option_spec(name))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption(
'bn',
'toggles batch norm',
True)
spec.addBoolOption(
'leaky_relu',
'toggles leaky ReLU',
False)
spec.addFloatOption(
'bn_momentum',
'batch norm momentum (pytorch style)',
0.1)
spec.addIntOption(
'dim',
'model dimension',
128)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption(
'bn',
'toggles batch norm',
True)
spec.addBoolOption(
'leaky_relu',
'toggles leaky ReLU',
True)
spec.addIntOption(
'num_block',
'number of blocks',
20)
spec.addIntOption(
'dim',
'model dimension',
128)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'num_minibatch',
'number of minibatches',
5000)
spec.addIntOption(
'num_episode',
'number of episodes',
10000)
spec.addIntOption(
'num_process',
'number of processes',
2)
spec.addBoolOption(
'tqdm',
'toggle tqdm visualization',
False)
return spec
def get_option_spec(cls): spec = PyOptionSpec() spec.addIntOption( 'num_block', 'number of resnet blocks', 20) spec.merge(Block.get_option_spec()) return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'sample_policy',
'choices of epsilon-greedy, multinomial, or uniform',
'epsilon-greedy')
spec.addBoolOption(
'store_greedy',
('if enabled, picks maximum-probability action; '
'otherwise, sample from distribution'),
False)
spec.addFloatOption(
'epsilon',
'used in epsilon-greedy',
0.0)
spec.addStrListOption(
'sample_nodes',
'nodes to be sampled and saved',
['pi,a'])
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'num_minibatch',
'number of minibatches',
5000)
spec.addIntOption(
'num_cooldown',
'Last #minibatches to refresh running mean/std for batchnorm '
'in addition to the training stage',
0)
spec.addIntOption(
'num_episode',
'number of episodes',
10000)
spec.addBoolOption(
'tqdm',
'toggle tqdm visualization',
False)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'a_node',
'action node',
'a')
spec.addStrOption(
'q_node',
'Q node',
'Q')
spec.merge(DiscountedReward.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'value_node',
'name of the value node',
'V')
spec.merge(PyOptionSpec.fromClasses(
(PolicyGradient, DiscountedReward, ValueMatcher)
))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
elf.saveDefaultOptionsToArgs("", spec)
elf.saveDefaultNetOptionsToArgs("", spec)
spec.addIntOption(
'gpu',
'GPU id to use',
-1)
spec.addStrListOption(
"parsed_args",
"dummy option",
[])
return spec
def main():
print(sys.version)
print(torch.__version__)
print(torch.version.cuda)
print("Conda env: \"%s\"" % os.environ.get("CONDA_DEFAULT_ENV", ""))
option_spec = PyOptionSpec()
option_spec.merge(PyOptionSpec.fromClasses((RunGC,)))
option_map = option_spec.parse()
rungc = RunGC(option_map)
rungc.initialize()
num_batch = 10000000
rungc.wrapper.start()
for i in range(num_batch):
rungc.wrapper.run()
rungc.wrapper.stop()
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addFloatOption('discount', 'exponential discount rate', 0.99)
return spec
def on_batch(self, batch):
#print("Receive batch: ", batch.smem.info(),
# ", curr_batchsize: ", str(batch.batchsize), sep='')
#print(batch["s"])
# print("Actor: " + str(datetime.timestamp(datetime.now())))
#print("on actor")
# n = self.params["num_action"]
# print(batch.batchsize)
res = self.model(batch)
res["V"].data /= 10.0
#print("s: %f, V: %f, pi: %s" % (batch["s"].data.item(), res["V"].data.item(), str(res["pi"].data)))
return dict(V=res["V"].data, pi=res["pi"].data)
if __name__ == '__main__':
option_spec = PyOptionSpec()
option_spec.merge(PyOptionSpec.fromClasses((RunGC,)))
option_map = option_spec.parse()
rungc = RunGC(option_map)
rungc.initialize()
num_batch = 10000000
start = time.perf_counter()
rungc.wrapper.start()
for i in range(num_batch):
rungc.wrapper.run()
elapsed = time.perf_counter() - start
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'record_dir',
'directory to record in',
'./record')
spec.addStrOption(
'save_prefix',
'prefix of savefiles',
'save')
spec.addStrOption(
'save_dir',
'directory for savefiles',
os.environ.get('save', './'))
spec.addStrOption(
'latest_symlink',
'name for latest model symlink',
'latest')
spec.addIntOption(
'num_games',
'number of games',
1024)
spec.addIntOption(
'batchsize',
'batch size',
128)
return spec
def get_option_spec(cls, stats_name=''):
spec = PyOptionSpec()
spec.addStrOption(stats_name + '_stats',
'type of stat to report (rewards or winrate)', '')
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
tutorial.getPredefined(spec.getOptionSpec())
spec.addIntOption(
'client_dummy',
'Some dummy arguments',
-1)
spec.addStrOption(
'client_dummy2',
'some string dummy arguments',
'')
spec.addBoolOption(
"client_dummy3",
"Some boolean dummy arguments",
True)
spec.addIntOption(
'gpu',
'GPU id to use',
-1)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption('bn', 'toggles batch norm', True)
spec.addBoolOption('leaky_relu', 'toggles leaky ReLU', False)
spec.addFloatOption('bn_momentum',
'batch norm momentum (pytorch style)', 0.1)
spec.addIntOption('num_block', 'number of blocks', 20)
spec.addIntOption('dim', 'model dimension', 128)
spec.addBoolOption('use_data_parallel', 'TODO: fill this in', False)
spec.addBoolOption('use_data_parallel_distributed',
'TODO: fill this in', False)
spec.addIntOption('dist_rank', 'TODO: fill this in', -1)
spec.addIntOption('dist_world_size', 'TODO: fill this in', -1)
spec.addStrOption('dist_url', 'TODO: fill this in', '')
spec.addIntOption('gpu', 'which gpu to use', -1)
spec.merge(GoResNet.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addFloatOption('entropy_ratio',
'the entropy ratio we put on PolicyGradient', 0.01)
spec.addFloatOption('grad_clip_norm', 'gradient norm clipping', 0.0)
spec.addFloatOption('min_prob', 'mininal probability used in training',
1e-6)
spec.addFloatOption('ratio_clamp', 'maximum importance sampling ratio',
10.0)
spec.addStrListOption('policy_action_nodes',
'the entropy ratio we put on PolicyGradient',
['pi,a'])
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption(
'bn',
'toggles batch norm',
True)
spec.addBoolOption(
'leaky_relu',
'toggles leaky ReLU',
False)
spec.addFloatOption(
'bn_momentum',
'batch norm momentum (pytorch style)',
0.1)
spec.addIntOption(
'num_block',
'number of blocks',
20)
spec.addIntOption(
'dim',
'model dimension',
128)
spec.addBoolOption(
'use_data_parallel',
'TODO: fill this in',
False)
spec.addBoolOption(
'use_data_parallel_distributed',
'TODO: fill this in',
False)
spec.addIntOption(
'dist_rank',
'TODO: fill this in',
-1)
spec.addIntOption(
'dist_world_size',
'TODO: fill this in',
-1)
spec.addStrOption(
'dist_url',
'TODO: fill this in',
'')
spec.addIntOption(
'gpu',
'which gpu to use',
-1)
spec.merge(GoResNet.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'preload_sgf',
'TODO: fill this help message in',
'')
spec.addIntOption(
'preload_sgf_move_to',
'TODO: fill this help message in',
-1)
spec.addBoolOption(
'actor_only',
'TODO: fill this help message in',
False)
spec.addStrListOption(
'list_files',
'Provide a list of json files for offline training',
[])
spec.addIntOption(
'port',
'TODO: fill this help message in',
5556)
spec.addStrOption(
'server_addr',
'TODO: fill this help message in',
'')
spec.addStrOption(
'server_id',
'TODO: fill this help message in',
'')
spec.addIntOption(
'q_min_size',
'TODO: fill this help message in',
10)
spec.addIntOption(
'q_max_size',
'TODO: fill this help message in',
1000)
spec.addIntOption(
'num_reader',
'TODO: fill this help message in',
50)
spec.addIntOption(
'num_reset_ranking',
'TODO: fill this help message in',
5000)
spec.addIntOption(
'client_max_delay_sec',
'Maximum amount of allowed delays in sec. If the client '
'didn\'t respond after that, we think it is dead.',
1200)
spec.addBoolOption(
'verbose',
'TODO: fill this help message in',
False)
spec.addBoolOption(
'keep_prev_selfplay',
'TODO: fill this help message in',
False)
spec.addBoolOption(
'print_result',
'TODO: fill this help message in',
False)
spec.addIntOption(
'data_aug',
'specify data augumentation, 0-7, -1 mean random',
-1)
spec.addIntOption(
'ratio_pre_moves',
('how many moves to perform in each thread, before we use the '
'data to train the model'),
0)
spec.addFloatOption(
'start_ratio_pre_moves',
('how many moves to perform in each thread, before we use the '
'first sgf file to train the model'),
0.5)
spec.addIntOption(
'num_games_per_thread',
('For offline mode, it is the number of concurrent games per '
'thread, used to increase diversity of games; for selfplay mode, '
'it is the number of games played at each thread, and after that '
'we need to call restartAllGames() to resume.'),
-1)
spec.addIntOption(
'expected_num_clients',
'Expected number of clients',
-1
)
spec.addIntOption(
'num_future_actions',
'TODO: fill this help message in',
1)
spec.addIntOption(
'move_cutoff',
'Cutoff ply in replay',
-1)
spec.addStrOption(
'mode',
'TODO: fill this help message in',
'online')
spec.addBoolOption(
'black_use_policy_network_only',
'TODO: fill this help message in',
False)
spec.addBoolOption(
'white_use_policy_network_only',
'TODO: fill this help message in',
False)
spec.addIntOption(
'ply_pass_enabled',
'TODO: fill this help message in',
0)
spec.addBoolOption(
'use_mcts',
'TODO: fill this help message in',
False)
spec.addBoolOption(
'use_mcts_ai2',
'TODO: fill this help message in',
False)
spec.addFloatOption(
'white_puct',
'PUCT for white when it is > 0.0. If it is -1 then we use'
'the same puct for both side (specified by mcts_options).'
'A HACK to use different puct for different model. Should'
'be replaced by a more systematic approach.',
-1.0)
spec.addIntOption(
'white_mcts_rollout_per_batch',
'white mcts rollout per batch',
-1)
spec.addIntOption(
'white_mcts_rollout_per_thread',
'white mcts rollout per thread',
-1)
spec.addBoolOption(
'use_df_feature',
'TODO: fill this help message in',
False)
spec.addStrOption(
'dump_record_prefix',
'TODO: fill this help message in',
'')
spec.addIntOption(
'policy_distri_cutoff',
'TODO: fill this help message in',
0)
spec.addFloatOption(
'resign_thres',
'TODO: fill this help message in',
0.0)
spec.addBoolOption(
'following_pass',
'TODO: fill this help message in',
False)
spec.addIntOption(
'selfplay_timeout_usec',
'TODO: fill this help message in',
0)
spec.addIntOption(
'gpu',
'TODO: fill this help message in',
-1)
spec.addBoolOption(
'policy_distri_training_for_all',
'TODO: fill this help message in',
False)
spec.addBoolOption(
'parameter_print',
'TODO: fill this help message in',
True)
spec.addIntOption(
'batchsize',
'batch size',
128)
spec.addIntOption(
'batchsize2',
'batch size',
-1)
spec.addIntOption(
'T',
'number of timesteps',
6)
spec.addIntOption(
'selfplay_init_num',
('Initial number of selfplay games to generate before training a '
'new model'),
2000)
spec.addIntOption(
'selfplay_update_num',
('Additional number of selfplay games to generate after a model '
'is updated'),
1000)
spec.addBoolOption(
'selfplay_async',
('Whether to use async mode in selfplay'),
False)
spec.addIntOption(
'eval_num_games',
('number of evaluation to be performed to decide whether a model '
'is better than the other'),
400)
spec.addFloatOption(
'eval_winrate_thres',
'Win rate threshold for evalution',
0.55)
spec.addIntOption(
'eval_old_model',
('If specified, then we directly switch to evaluation mode '
'between the loaded model and the old model specified by this '
'switch'),
-1)
spec.addStrOption(
'eval_model_pair',
('If specified for df_selfplay.py, then the two models will be '
'evaluated on this client'),
'')
spec.addStrOption(
'comment',
'Comment for this run',
'')
spec.addBoolOption(
'cheat_eval_new_model_wins_half',
'When enabled, in evaluation mode, when the game '
'finishes, the player with the most recent model gets 100% '
'win rate half of the time.'
'This is used to test the framework',
False)
spec.addBoolOption(
'cheat_selfplay_random_result',
'When enabled, in selfplay mode the result of the game is random'
'This is used to test the framework',
False)
spec.addIntOption(
'suicide_after_n_games',
'return after n games have finished, -1 means it never ends',
-1)
spec.merge(PyOptionSpec.fromClasses((ContextArgs, MoreLabels)))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption(
'opt_method',
'optimization method (adam or sgd)',
'adam')
spec.addFloatOption(
'lr',
'learning rate',
1e-3)
spec.addFloatOption(
'adam_eps',
'Adam epsilon',
1e-3)
spec.addFloatOption(
'momentum',
'momentum parameter',
0.9)
spec.addFloatOption(
'weight_decay',
'weight decay rate',
0.0)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addStrOption('record_dir', 'directory to record in', './record')
spec.addStrOption('save_prefix', 'prefix of savefiles', 'save')
spec.addStrOption('save_dir', 'directory for savefiles',
os.environ.get('save', './'))
spec.addStrOption('latest_symlink', 'name for latest model symlink',
'latest')
spec.addIntOption('num_games', 'number of games', 1024)
spec.addIntOption('batchsize', 'batch size', 128)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addFloatOption(
'entropy_ratio',
'the entropy ratio we put on PolicyGradient',
0.01)
spec.addFloatOption(
'grad_clip_norm',
'gradient norm clipping',
0.0)
spec.addFloatOption(
'min_prob',
'mininal probability used in training',
1e-6)
spec.addFloatOption(
'ratio_clamp',
'maximum importance sampling ratio',
10.0)
spec.addStrListOption(
'policy_action_nodes',
'the entropy ratio we put on PolicyGradient',
['pi,a'])
return spec
def load_env(
envs,
num_models=None,
overrides=None,
additional_to_load=None):
"""Load envs.
Envs will be specified as environment variables. Specifically, the
environment variables ``game``, ``model_file`` and ``model`` are
required.
``additional_to_load`` is a dict with the following format:
{'variable_name': (option_spec, callable)}
For each element in ``additional_to_load``, ``load_env`` will parse
the ``option_spec``, pass the resulting option map to ``callable``,
and store the result of ``callable`` in the return value
(under the key ``name``).
Returns:
env: dict of
``game`` : game module
``method``: Learning method used
``model_loaders``: loaders for model
"""
logger = logging.getIndexedLogger('\u001b[31;1m|py|\u001b[0mrlpytorch.model_loader.load_env', '')
logger.info('Loading env')
game_loader_class = load_module(envs["game"]).Loader
logger.info(f'\u001b[32;1mModule game successfully loaded :\u001b[0m {envs["game"]}')
model_file = load_module(envs["model_file"])
logger.info(f'\u001b[32;1mModule model_file successfully loaded :\u001b[0m {envs["model_file"]}')
# TODO This is not good, need to fix.
if len(model_file.Models[envs["model"]]) == 2:
model_class, method_class = model_file.Models[envs["model"]]
sampler_class = Sampler
else:
model_class, method_class, sampler_class = \
model_file.Models[envs["model"]]
overrides = dict(overrides) if overrides else {}
overrides.update(getattr(model_file, "Overrides", {}))
option_spec = PyOptionSpec()
option_spec.merge(PyOptionSpec.fromClasses((
logging.GlobalLoggingConfigurator,
game_loader_class,
method_class,
sampler_class,
ModelInterface,
)))
if num_models is None:
option_spec.merge(ModelLoader.get_option_spec(model_class))
else:
for i in range(num_models):
option_spec.merge(
ModelLoader.get_option_spec(model_class, model_idx=i))
if additional_to_load:
for additional_option_spec, _ in additional_to_load.values():
option_spec.merge(additional_option_spec)
option_map = option_spec.parse(overrides=overrides)
global_logger_configurator = logging.GlobalLoggingConfigurator(option_map)
global_logger_configurator.configure()
game = game_loader_class(option_map)
method = method_class(option_map)
sampler = sampler_class(option_map)
mi = ModelInterface(option_map)
# You might want multiple models loaded.
if num_models is None:
model_loaders = [ModelLoader(option_map, model_class)]
else:
model_loaders = [ModelLoader(option_map, model_class, model_idx=i)
for i in range(num_models)]
env = dict(
game=game,
method=method,
sampler=sampler,
model_loaders=model_loaders,
mi=mi,
)
if additional_to_load:
for name, (_, option_map_callable) in additional_to_load.items():
env[name] = option_map_callable(option_map)
pretty_option_str = pprint.pformat(option_map.getOptionDict(), width=50)
logger.info(f'Parsed options:\n{pretty_option_str}')
logger.info('Finished loading env')
# env_str = ""
# for i in env:
# env_str += f"{i} : {env[i]}\n"
# logger.info(f"env :\n{env_str}")
return env
def get_option_spec(cls, model_class=None, model_idx=None):
spec = PyOptionSpec()
spec.addStrOption(
'load',
'load model',
'')
spec.addStrListOption(
'onload',
('functions to call after loading. e.g., reset,zero_first_layer. '
'These functions are specified in the model'),
[])
spec.addStrListOption(
'omit_keys',
'omitted keys when loading',
[])
spec.addStrListOption(
'replace_prefix',
'replace prefix',
[])
spec.addIntOption(
'gpu',
'which GPU to use',
-1)
spec.addBoolOption(
'check_loaded_options',
'Toggles consistency check of loaded vs. current model options.',
True)
spec.addBoolOption(
'use_fp16',
'use_fp16',
False)
spec.addFloatOption(
'load_model_sleep_interval',
('If zero, has no effect. If positive, then before loading the '
'model, we will sleep for an interval of '
'duration (secs) ~ Uniform[0, load_model_sleep_interval]'),
0.0)
if model_class is not None and hasattr(model_class, 'get_option_spec'):
spec.merge(model_class.get_option_spec())
idx_suffix = '' if model_idx is None else str(model_idx)
spec.addPrefixSuffixToOptionNames('', idx_suffix)
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'freq_update',
'frequency of model update',
1)
spec.addBoolOption(
'save_first',
'save first model',
False)
spec.addIntOption(
'num_games',
'number of games',
1024)
spec.addIntOption(
'batchsize',
'batch size',
128)
# print("\u001b[31;1m|py|\u001b[0m\u001b[37m", "Trainer::", inspect.currentframe().f_code.co_name)
# print("\u001b[31;1m", os.path.dirname(os.path.abspath(__file__)), " - ", os.path.basename(__file__), "\u001b[0m")
spec.merge(Evaluator.get_option_spec('trainer'))
spec.merge(ModelSaver.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'freq_update',
'frequency of model update',
1)
spec.addBoolOption(
'save_first',
'save first model',
False)
spec.addIntOption(
'num_games',
'number of games',
1024)
spec.addIntOption(
'batchsize',
'batch size',
128)
spec.merge(Evaluator.get_option_spec('trainer'))
spec.merge(ModelSaver.get_option_spec())
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addBoolOption('actor_only', 'TODO: fill this help message in',
False)
spec.addStrListOption(
'list_files', 'Provide a list of json files for offline training',
[])
spec.addIntOption('port', 'TODO: fill this help message in', 5556)
spec.addStrOption('server_addr', 'TODO: fill this help message in', '')
spec.addStrOption('server_id', 'TODO: fill this help message in', '')
spec.addIntOption('q_min_size', 'TODO: fill this help message in', 10)
spec.addIntOption('q_max_size', 'TODO: fill this help message in',
1000)
spec.addIntOption('num_reader', 'TODO: fill this help message in', 50)
spec.addIntOption('num_reset_ranking',
'TODO: fill this help message in', 5000)
spec.addIntOption(
'client_max_delay_sec',
'Maximum amount of allowed delays in sec. If the client '
'didn\'t respond after that, we think it is dead.', 1200)
spec.addBoolOption('verbose', 'TODO: fill this help message in', False)
spec.addBoolOption('keep_prev_selfplay',
'TODO: fill this help message in', False)
spec.addIntOption(
'num_games_per_thread',
('For offline mode, it is the number of concurrent games per '
'thread, used to increase diversity of games; for selfplay mode, '
'it is the number of games played at each thread, and after that '
'we need to call restartAllGames() to resume.'), -1)
spec.addIntOption('expected_num_clients', 'Expected number of clients',
-1)
spec.addIntOption('checkers_num_future_actions',
'TODO: fill this help message in', 1)
spec.addStrOption('mode', 'TODO: fill this help message in', 'play')
spec.addBoolOption('black_use_policy_network_only',
'TODO: fill this help message in', False)
spec.addBoolOption('white_use_policy_network_only',
'TODO: fill this help message in', False)
spec.addBoolOption('use_mcts', 'TODO: fill this help message in',
False)
spec.addBoolOption('use_mcts_ai2', 'TODO: fill this help message in',
False)
spec.addFloatOption(
'white_puct',
'PUCT for white when it is > 0.0. If it is -1 then we use'
'the same puct for both side (specified by mcts_options).'
'A HACK to use different puct for different model. Should'
'be replaced by a more systematic approach.', -1.0)
spec.addIntOption('white_mcts_rollout_per_batch',
'white mcts rollout per batch', -1)
spec.addIntOption('white_mcts_rollout_per_thread',
'white mcts rollout per thread', -1)
spec.addStrOption('dump_record_prefix',
'TODO: fill this help message in', '')
spec.addStrOption('selfplay_records_directory',
'TODO: fill this help message in', '')
spec.addStrOption('eval_records_directory',
'TODO: fill this help message in', '')
spec.addStrOption('records_buffer_directory',
'TODO: fill this help message in', '')
spec.addIntOption('policy_distri_cutoff',
'first N moves will be randomly', 0)
spec.addIntOption('selfplay_timeout_usec',
'TODO: fill this help message in', 0)
spec.addIntOption('gpu', 'TODO: fill this help message in', -1)
spec.addBoolOption('policy_distri_training_for_all',
'TODO: fill this help message in', False)
spec.addBoolOption('parameter_print',
'TODO: fill this help message in', True)
spec.addIntOption('batchsize', 'batch size', 128)
spec.addIntOption('batchsize2', 'batch size', -1)
spec.addIntOption('T', 'number of timesteps', 6)
spec.addIntOption(
'selfplay_init_num',
('Initial number of selfplay games to generate before training a '
'new model'), 2000)
spec.addIntOption(
'selfplay_update_num',
('Additional number of selfplay games to generate after a model '
'is updated'), 1000)
spec.addBoolOption('selfplay_async',
('Whether to use async mode in selfplay'), False)
spec.addIntOption(
'eval_num_games',
('number of evaluation to be performed to decide whether a model '
'is better than the other'), 400)
spec.addFloatOption('eval_winrate_thres',
'Win rate threshold for evalution', 0.55)
spec.addIntOption(
'eval_old_model',
('If specified, then we directly switch to evaluation mode '
'between the loaded model and the old model specified by this '
'switch'), -1)
spec.addStrOption(
'eval_model_pair',
('If specified for df_selfplay.py, then the two models will be '
'evaluated on this client'), '')
spec.addBoolOption(
'cheat_eval_new_model_wins_half',
'When enabled, in evaluation mode, when the game '
'finishes, the player with the most recent model gets 100% '
'win rate half of the time.'
'This is used to test the framework', False)
spec.addBoolOption(
'cheat_selfplay_random_result',
'When enabled, in selfplay mode the result of the game is random'
'This is used to test the framework', False)
spec.addBoolOption('human_plays_for_black', '', False)
spec.addIntOption(
'suicide_after_n_games',
'return after n games have finished, -1 means it never ends', -1)
spec.merge(PyOptionSpec.fromClasses((ContextArgs, MoreLabels)))
return spec
def get_option_spec(cls):
spec = PyOptionSpec()
spec.addIntOption(
'num_games',
'number of games',
1024)
spec.addIntOption(
'batchsize',
'batch size',
128)
spec.addIntOption(
'T',
'number of timesteps',
6)
spec.addBoolOption(
'verbose_comm',
'enables verbose comm',
False)
spec.addIntOption(
'mcts_threads',
'number of MCTS threads',
0)
spec.addIntOption(
'mcts_rollout_per_batch',
'Batch size for mcts rollout',
1)
spec.addIntOption(
'mcts_rollout_per_thread',
'number of rollotus per MCTS thread',
1)
spec.addBoolOption(
'mcts_verbose',
'enables mcts verbosity',
False)
spec.addBoolOption(
'mcts_verbose_time',
'enables mcts verbosity for time stats',
False)
spec.addBoolOption(
'mcts_persistent_tree',
'use persistent tree in MCTS',
False)
spec.addBoolOption(
'mcts_use_prior',
'use prior in MCTS',
False)
spec.addIntOption(
'mcts_virtual_loss',
'"virtual" number of losses for MCTS edges',
0)
spec.addStrOption(
'mcts_pick_method',
'criterion for mcts node selection',
'most_visited')
spec.addFloatOption(
'mcts_puct',
'prior weight',
1.0)
spec.addFloatOption(
'mcts_epsilon',
'for exploration enhancement, weight of randomization',
0.0)
spec.addFloatOption(
'mcts_alpha',
'for exploration enhancement, alpha term in gamma distribution',
0.0)
spec.addBoolOption(
"mcts_unexplored_q_zero",
'set all unexplored node to have Q value zero',
False)
spec.addBoolOption(
"mcts_root_unexplored_q_zero",
'set unexplored child of root node to have Q value zero',
False)
return spec