def maybe_load_model(savedir):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
found_model = os.path.exists(state_path)
if found_model:
state = pickle_load(state_path, compression=True)
model_dir = "model-{}".format(state["num_iters"])
U.load_state(os.path.join(savedir, model_dir, "saved"))
logger.log("Loaded models checkpoint at {} iterations".format(state["num_iters"]))
return state
def load(self, path, session):
"""Load model if present at the specified path."""
if path is None:
return
state_path = os.path.join(os.path.join(path, 'training_state.pkl.zip'))
found_model = os.path.exists(state_path)
if found_model:
state = pickle_load(state_path, compression=True)
model_dir = "model-{}".format(state["num_iters"])
U.load_state(os.path.join(path, model_dir, "saved"), session=session)
self.logger.log("Loaded models checkpoint at {} iterations".format(state["num_iters"]))
if state is not None:
self.num_iters = state["num_iters"]
def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
logger.log("Attempting to download model from Azure")
found_model = container.get(savedir, 'training_state.pkl.zip')
else:
found_model = os.path.exists(state_path)
if found_model:
state = pickle_load(state_path, compression=True)
model_dir = "model-{}".format(state["num_iters"])
if container is not None:
container.get(savedir, model_dir)
U.load_state(os.path.join(savedir, model_dir, "saved"))
logger.log("Loaded models checkpoint at {} iterations".format(state["num_iters"]))
return state
def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
logger.log("Attempting to download model from Azure")
found_model = container.get(savedir, 'training_state.pkl.zip')
else:
found_model = os.path.exists(state_path)
if found_model:
state = pickle_load(state_path, compression=True)
model_dir = "model-{}".format(state["num_iters"])
if container is not None:
container.get(savedir, model_dir)
load_state(os.path.join(savedir, model_dir, "saved"))
logger.log("Loaded models checkpoint at {} iterations".format(
state["num_iters"]))
return state
def load_model():
load_state("saved_model/model.ckpt")
dict_state = pickle_load("saved_model/model_state.pkl.zip", compression=True)
return dict_state
