return self.sessions[-1]
experiment = Experiment()
# Experiment defaults
experiment.name = 'experiment'
experiment.tags = []
experiment.samples = 0
experiment.model = {'fn': None, 'args': [], 'kwargs': {}}
experiment.optimizer = {'fn': None, 'args': [], 'kwargs': {}}
experiment.sessions = []
# Session defaults
session = AutoMunch()
session.losses = {'solubility': 0, 'l1': 0}
session.seed = random.randint(0, 99)
session.cpus = multiprocessing.cpu_count() - 1
session.device = 'cuda' if torch.cuda.is_available() else 'cpu'
session.log = {'when': []}
session.checkpoint = {'when': []}
# Experiment configuration
for string in args.experiment:
if '=' in string:
update = parse_dotted(string)
else:
with open(string, 'r') as f:
update = yaml.safe_load(f)
# If the current session is defined inside the experiment update the session instead
if 'session' in update:
return self.sessions[-1]
experiment = Experiment()
# Experiment defaults
experiment.name = 'experiment'
experiment.tags = []
experiment.samples = 0
experiment.model = {'fn': None, 'args': [], 'kwargs': {}}
experiment.optimizer = {'fn': None, 'args': [], 'kwargs': {}}
experiment.sessions = []
# Session defaults
session = AutoMunch()
session.losses = {'nodes': 0, 'count': 0, 'l1': 0}
session.seed = random.randint(0, 99)
session.cpus = multiprocessing.cpu_count() - 1
session.device = 'cuda' if torch.cuda.is_available() else 'cpu'
session.log = {'when': []}
session.checkpoint = {'when': []}
# Experiment configuration
for string in args.experiment:
if '=' in string:
update = parse_dotted(string)
else:
with open(string, 'r') as f:
update = yaml.safe_load(f)
# If the current session is defined inside the experiment update the session instead
if 'session' in update: