def __init__(self):
""" Initializes the serving state, reads a trained model from HDFS"""
self.model_path = "Models/IrisFlowerClassifier/1/iris_knn.pkl"
print("Copying SKLearn model from HDFS to local directory")
hdfs.copy_to_local(self.model_path)
print("Reading local SkLearn model for serving")
self.model = joblib.load("./iris_knn.pkl")
print("Initialization Complete")
def __init__(self):
""" Initializes the serving state, reads a trained model from HDFS"""
self.model_path = "Models/XGBoost_Churn_Classifier/1/xgb_reg.pkl"
print("Copying model from HDFS to local directory")
hdfs.copy_to_local(self.model_path)
print("Reading local model for serving")
self.model = xgb_model_loaded = pickle.load(open("xgb_reg.pkl", "rb"))
print("Initialization Complete")
def load(hdfs_filename, **kwds):
"""
Reads a file from HDFS into a Numpy Array
Args:
:hdfs_filename: You can specify either a full hdfs pathname or a relative one (relative to your Project's path in HDFS).
:**kwds: You can add any additional args found in numpy.read(...)
Returns:
A numpy array
Raises:
IOError: If the file does not exist
"""
hdfs_path = hdfs._expand_path(hdfs_filename)
local_path = hdfs.copy_to_local(hdfs_path)
return np.load(local_path, **kwds)
def _copyHdfsToLocalOverwrite(hdfs_filename):
hdfs_path = hdfs._expand_path(hdfs_filename)
local_path = hdfs.copy_to_local(hdfs_path)
return local_path