def reset_internal_config(run_name: str):
j = json.load(open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'r'))
if j['state'] == 'finished':
j['state'] = 'ft'
j['ft_started'] = False
j['finished'] = False
json.dump(j, open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'w'))
def _fully_train_logging(model: Network,
loss: float,
epoch: int,
attempt: int,
acc: float = -1):
print('epoch: {}\nloss: {}'.format(epoch, loss))
log = {}
metric_name = 'accuracy_fm_' + str(model.target_feature_multiplier) + (
"_r_" + str(attempt) if attempt > 0 else "")
if acc != -1:
log[metric_name] = acc
print('accuracy: {}'.format(acc))
print('\n')
internal_config.ft_epoch = epoch
save_config(config.run_name)
if config.use_wandb:
log['loss_' + str(attempt)] = loss
wandb.log(log)
model.save()
wandb.save(model.save_location())
wandb.config.update({'current_ft_epoch': epoch}, allow_val_change=True)
wandb.save(join(get_run_folder_path(config.run_name), 'config.json'))
def get_config_path(path: str, scheduler_run_name: str) -> Tuple[str, str]:
"""picks which config to use next."""
config_paths = read_json(build_file_path(path)) # dict: path -> num_runs
for config_path in config_paths:
n_runs = config_paths[config_path]
for i in range(n_runs):
config_dict = read_json(build_file_path(config_path))
scheduled_run_name = config_dict['run_name']
run_name = _get_effective_run_name(scheduled_run_name, i, scheduler_run_name)
run_path = run_man.get_run_folder_path(run_name)
run_folder_exists = os.path.exists(run_path)
if run_folder_exists:
cfg.internal_config.load(run_name)
run_currently_running_in_another_process = run_folder_exists and cfg.internal_config.running
if run_currently_running_in_another_process:
print('run {} is being run in another process, moving on'.format(run_name))
if cfg.internal_config.finished or run_currently_running_in_another_process:
cfg.internal_config.__init__() # reset internal config
continue
print('scheduler running', run_name)
if run_folder_exists:
cfg.internal_config.running = True
cfg.internal_config.save(run_name, False)
return config_path, run_name
raise Exception('Could not find any non-running/non-finished configs in the batch run')
def save(self, run_name: str, wandb_save=True):
file_path = join(runs_manager.get_run_folder_path(run_name), 'internal_config.json')
with open(file_path, 'w+') as f:
json.dump(self.__dict__, f, indent=2)
if configuration.config.use_wandb and wandb_save:
try:
wandb.save(file_path)
except ValueError:
print('Error: You must call `wandb.init` before calling save. This happens because wandb is not '
'initialized in the main thread in fully training. If you were not fully training this is should '
'be investigated, otherwise ignore it')
def get_fully_train_state(run_name):
"""reads the inner config of the given run, and determines if it is to be evolved/ FT'd"""
run_path = run_man.get_run_folder_path(run_name)
path_exists = os.path.exists(run_path)
if path_exists:
cfg.internal_config.load(run_name)
fully_training = cfg.internal_config.state == 'ft'
continue_fully_training = fully_training and cfg.internal_config.ft_started
cfg.internal_config.__init__()
return fully_training, continue_fully_training
def __init__(self, fm: float, best: int, attempt: int):
"""
@param fm: feature multiplier: how much bigger or smaller to make each layer
@param best: the ranking of the network in evolution - ie best = 1 mean that network got the highest accuracy
in evolution
"""
path = join(get_run_folder_path(config.run_name), 'logs', f'fm{fm}',
f'best{best}')
file = join(path, f'attempt{attempt}.log')
Path(path).mkdir(parents=True, exist_ok=True)
self.logger = logging.getLogger(file)
self.logger.addHandler(logging.FileHandler(file, 'a'))
self.logger.setLevel(logging.DEBUG)
self.logger.info(f'fm:{fm}')
self.logger.info(f'best:{best}')
self.logger.info(f'attempt:{attempt}')
self.logger.info(f'config:{config.__dict__}')
print(f'INITIALIZED FT LOGGER. DIR={file}')
sys.stdout.flush()
def set_run_inactive(run_name: str):
j = json.load(open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'r'))
j['running'] = False
json.dump(j, open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'w'))
def reset_internal_config(run_name: str):
j = json.load(open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'r'))
if j['state'] == 'finished':
j['state'] = 'ft'
j['ft_started'] = False
j['finished'] = False
json.dump(j, open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'w'))
def set_run_inactive(run_name: str):
j = json.load(open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'r'))
j['running'] = False
json.dump(j, open(os.path.join(get_run_folder_path(run_name), 'internal_config.json'), 'w'))
if __name__ == '__main__':
dirs = [d for d in os.listdir(__get_runs_folder_path()) if os.path.isdir(get_run_folder_path(d))]
for dir in dirs:
try:
wipe(dir)
reset_internal_config(dir)
set_run_inactive(dir)
except FileNotFoundError:
print(f'no relevant files in {dir}')
config.fully_train_best_n_blueprints)
print(
f'best blueprints ({len(best_blueprints)}): {[b[0].id for b in best_blueprints]}'
)
best = 1
for bp, _ in best_blueprints:
for fm in [1, 3, 5]:
old_file_name = f'bp-{bp.id}_fm-{fm}.model'
new_file_name = f'bp-{bp.id}_fm-{fm}-best-{best}.model'
old_file_path = os.path.join(get_fully_train_folder_path(run_name),
old_file_name)
new_file_path = os.path.join(get_fully_train_folder_path(run_name),
new_file_name)
if os.path.exists(old_file_path):
print(f'renaming: {old_file_name} to {new_file_name}')
os.rename(old_file_path, new_file_path)
best += 1
if __name__ == '__main__':
for run in os.listdir(__get_runs_folder_path()):
if 'base' in run or 'elite' in run:
fix(get_run_folder_path(run))
# fix(get_run_folder_path('elite_1'))
