def check_experiment_type_can_train(self, param_file):
param_dict = pyhocon.ConfigFactory.parse_file(param_file)
params = Params(replace_none(param_dict))
model_class = concrete_models[params.pop("model_class")]
# Tests will try to create root directories as we have /net/efs paths,
# so we just remove the serialisation aspect here, alter the train/validation
# paths to the dummy test ones and make sure we only do one epoch to
# speed things up.
params["model_serialization_prefix"] = None
if len(params["train_files"]) > 1:
params["train_files"] = [self.TRAIN_FILE, self.TRAIN_BACKGROUND]
params["validation_files"] = [
self.VALIDATION_FILE, self.VALIDATION_BACKGROUND
]
else:
params["train_files"] = [self.TRAIN_FILE]
params["validation_files"] = [self.TRAIN_FILE]
params["num_epochs"] = 1
try:
if params["embeddings"]["words"]["pretrained_file"]:
params["embeddings"]["words"][
"pretrained_file"] = self.PRETRAINED_VECTORS_GZIP
except KeyError:
# No embedding/words field passed in the parameters,
# so nothing to change.
pass
model = self.get_model(model_class, params)
model.train()
def serve(port: int, param_file: str):
# read in the Typesafe-style config file
params = pyhocon.ConfigFactory.parse_file(param_file)
params = Params(replace_none(params))
retrieval_params = params.pop('retrieval')
corpus_file = params.pop('corpus', None)
num_neighbors = params.pop('num_neighbors', 10)
global retrieval
retrieval = VectorBasedRetrieval(retrieval_params)
if corpus_file is not None:
retrieval.read_background(corpus_file)
retrieval.fit()
retrieval.save_model()
else:
retrieval.load_model()
# start the server on the specified port
print("starting server")
app.run(host='0.0.0.0')
def __init__(self, params: Params):
self.num_stacked_rnns = params.pop('num_stacked_rnns', 1)
instance_type = params.pop('instance_type', "VerbSemanticsInstance")
self.instance_type = concrete_instances[instance_type]
super(VerbSemanticsModel, self).__init__(params)