def download_model(workspace, path_on_data_store, target_path='.', overwrite=True):
blob_data_store = Datastore.get_default(workspace)
number_of_files_successfully_downloaded = blob_data_store.download(target_path=target_path,
prefix=path_on_data_store, overwrite=overwrite)
if number_of_files_successfully_downloaded == 0:
print('there is no model downloaded')
else:
print('model is downloaded to the directory of {}'.format(target_path))
# this is the URL to the CSV file containing the connected car component descriptions
cardata_url = ('https://quickstartsws9073123377.blob.core.windows.net/'
'azureml-blobstore-0d1c4218-a5f9-418b-bf55-902b65277b85/'
'quickstarts/connected-car-data/connected-car_components.csv')
cardata_ds_name = 'connected_car_components'
cardata_ds_description = 'Connected car components data'
embedding_dim = 100
training_samples = 90000
validation_samples = 5000
max_words = 10000
run = Run.get_context()
ws = run.experiment.workspace
ds = Datastore.get_default(ws)
#-------------------------------------------------------------------
#
# Process GloVe embeddings dataset
#
#-------------------------------------------------------------------
# The GloVe embeddings dataset is static so we will only register it once with the workspace
print("Downloading GloVe embeddings...")
try:
glove_ds = Dataset.get_by_name(workspace=ws, name=glove_ds_name)
print('GloVe embeddings dataset already registered.')
except:
def __init__(self, ws, service_name, model_name):
self.__ws = ws
self.__service_name = service_name
self.__model = Model(self.__ws, name=model_name)
self.__datastore = Datastore.get_default(ws)