# Create experiment
ex = Experiment(
name='Dummy Experiment',
ingredients=[dataset, model, training]
)
# Runtime options
save_folder = '../../data/sims/test/'
ex.add_config({
'save': save_folder,
'no_cuda': False,
})
# Add dependencies
ex.add_package_dependency('torch', torch.__version__)
# Add observer
ex.observers.append(
FileStorageObserver.create(save_folder))
# ex.observers.append(
# MongoObserver.create(url='127.0.0.1:27017',
# db_name='MY_DB')
# )
@ex.capture
def log_training(tracer):
ex.log_scalar('training_loss', tracer.trace[-1])
tracer.trace.clear()
@ex.capture
def create_experiment(task,
name,
dataset_configs,
training_configs,
model_configs,
observers,
experiment_configs=None):
dataset, load_dataset = get_dataset_ingredient(task)
# Create experiment
ex = Experiment(name=name, ingredients=[dataset, model, training])
update_configs_(dataset, dataset_configs)
update_configs_(training, training_configs)
update_configs_(model, model_configs)
if experiment_configs is not None:
update_configs_(ex, experiment_configs)
# Runtime options
save_folder = '../../data/sims/deladd/temp/'
ex.add_config({
'no_cuda': False,
})
# Add dependencies
ex.add_source_file('../../src/model/subLSTM/nn.py')
ex.add_source_file('../../src/model/subLSTM/functional.py')
ex.add_package_dependency('torch', torch.__version__)
ex.observers.extend(observers)
def _log_training(tracer):
ex.log_scalar('training_loss', tracer.trace[-1])
tracer.trace.clear()
def _log_validation(engine):
for metric, value in engine.state.metrics.items():
ex.log_scalar('val_{}'.format(metric), value)
def _run_experiment(_config, seed):
no_cuda = _config['no_cuda']
batch_size = _config['training']['batch_size']
device = set_seed_and_device(seed, no_cuda)
training_set, test_set, validation_set = load_dataset(
batch_size=batch_size)
model = init_model(device=device)
trainer, validator, checkpoint, metrics = setup_training(
model,
validation_set,
save=save_folder,
device=device,
trace=False,
time=False)[:4]
tracer = Tracer().attach(trainer)
trainer.add_event_handler(Events.EPOCH_COMPLETED,
lambda e: _log_training(tracer))
validator.add_event_handler(Events.EPOCH_COMPLETED, _log_validation)
test_metrics = run_training(model=model,
train_data=training_set,
trainer=trainer,
test_data=test_set,
metrics=metrics,
model_checkpoint=checkpoint,
device=device)
# save best model performance and state
for metric, value in test_metrics.items():
ex.log_scalar('test_{}'.format(metric), value)
ex.add_artifact(str(checkpoint._saved[-1][1][0]), 'trained-model')
return ex, _run_experiment
from comms_rl.agents.random_agent import RandomAgent
from comms_rl.agents.round_robin_agent import *
from comms_rl.agents.proportional_fair import *
path_abs = "../../"
# Load agent parameters
with open(path_abs + 'config/config_agent.json') as f:
ac = json.load(f)
# Configure experiment
with open(path_abs + 'config/config_sacred.json') as f:
sc = json.load(f) # Sacred Configuration
ns = sc["sacred"][
"n_metrics_points"] # Number of points per episode to log in Sacred
ex = Experiment(ac["agent"]["agent_type"])
ex.add_package_dependency("comms_rl", "0.1")
ex.add_config(sc)
ex.add_config(ac)
#mongo_db_url = f'mongodb://{sc["sacred"]["sacred_user"]}:{sc["sacred"]["sacred_pwd"]}@' +\
# f'{sc["sacred"]["sacred_host"]}:{sc["sacred"]["sacred_port"]}/{sc["sacred"]["sacred_db"]}'
# ex.observers.append(MongoObserver(url=mongo_db_url, db_name=sc["sacred"]["sacred_db"])) # Uncomment to save to DB
# Load environment parameters
with open(path_abs + 'config/config_environment.json') as f:
ec = json.load(f)
ex.add_config(ec)
@ex.automain
def main(_run):
n_eps = _run.config["agent"]["n_episodes"]
