From 4c3512b042cd50c31bd635a955082e91b195f8f3 Mon Sep 17 00:00:00 2001 From: Karanraj Chauhan Date: Mon, 24 Aug 2020 12:11:22 -0400 Subject: [PATCH 01/10] Added upstream models. Updated model.py to use these by default. Signed-off-by: Karan Chauhan --- device_prediction/model.py | 291 +++++++++++++++++- device_prediction/models/redhat/config.json | 4 + .../models/redhat/hgst_predictor.pkl | Bin 0 -> 2860606 bytes .../models/redhat/hgst_scaler.pkl | Bin 0 -> 1865 bytes .../models/redhat/seagate_predictor.pkl | Bin 0 -> 37062936 bytes .../models/redhat/seagate_scaler.pkl | Bin 0 -> 1481 bytes 6 files changed, 293 insertions(+), 2 deletions(-) create mode 100644 device_prediction/models/redhat/config.json create mode 100644 device_prediction/models/redhat/hgst_predictor.pkl create mode 100644 device_prediction/models/redhat/hgst_scaler.pkl create mode 100644 device_prediction/models/redhat/seagate_predictor.pkl create mode 100644 device_prediction/models/redhat/seagate_scaler.pkl diff --git a/device_prediction/model.py b/device_prediction/model.py index 7497b33..5404900 100755 --- a/device_prediction/model.py +++ b/device_prediction/model.py @@ -1,16 +1,280 @@ #!/usr/bin/env python3 # vim: ts=4 sw=4 expandtab +"""Machine learning model for disk failure prediction. + +This classes defined here provide the disk failure prediction module. +RHDiskFailurePredictor uses the models developed at the AICoE in the +Office of the CTO at Red Hat. These models were built using the open +source Backblaze SMART metrics dataset. +PSDiskFailurePredictor uses the models developed by ProphetStor as an +example. + +An instance of the predictor is initialized by providing the path to trained +models. Then, to predict hard drive health and deduce time to failure, the +predict function is called with 6 days worth of SMART data from the hard drive. +It will return a string to indicate disk failure status: "Good", "Warning", +"Bad", or "Unknown". + +An example code is as follows: + +>>> model = disk_failure_predictor.RHDiskFailurePredictor() +>>> status = model.initialize("./models") +>>> if status: +>>> model.predict(disk_days) +'Bad' +""" +import os +import pickle +import logging +import argparse + +import numpy as np import json import sys from enum import Enum + s_Reallocated_Sector_Count = '5' s_Reported_Uncorrectable_Errors = '187' s_Command_Timeout = '188' s_Current_Pending_Sector_Count = '197' s_Offline_Uncorrectable = '198' + +def get_diskfailurepredictor_path(): + path = os.path.abspath(__file__) + dir_path = os.path.dirname(path) + return dir_path + + +class RHDiskFailurePredictor(object): + """Disk failure prediction module developed at Red Hat + + This class implements a disk failure prediction module. + """ + + # json with manufacturer names as keys + # and features used for prediction as values + CONFIG_FILE = "config.json" + PREDICTION_CLASSES = {-1: "Unknown", 0: "Good", 1: "Warning", 2: "Bad"} + + # model name prefixes to identify vendor + MANUFACTURER_MODELNAME_PREFIXES = { + "WDC": "WDC", + "Toshiba": "Toshiba", # for cases like "Toshiba xxx" + "TOSHIBA": "Toshiba", # for cases like "TOSHIBA xxx" + "toshiba": "Toshiba", # for cases like "toshiba xxx" + "S": "Seagate", # for cases like "STxxxx" and "Seagate BarraCuda ZAxxx" + "ZA": "Seagate", # for cases like "ZAxxxx" + "Hitachi": "Hitachi", + "HGST": "HGST", + } + + LOGGER = logging.getLogger() + + def __init__(self): + """ + This function may throw exception due to wrong file operation. + """ + self.model_dirpath = "" + self.model_context = {} + + def initialize(self, model_dirpath): + """Initialize all models. Save paths of all trained model files to list + + Arguments: + model_dirpath {str} -- path to directory of trained models + + Returns: + str -- Error message. If all goes well, return None + """ + # read config file as json, if it exists + config_path = os.path.join(model_dirpath, self.CONFIG_FILE) + if not os.path.isfile(config_path): + return "Missing config file: " + config_path + else: + with open(config_path) as f_conf: + self.model_context = json.load(f_conf) + + # ensure all manufacturers whose context is defined in config file + # have models and scalers saved inside model_dirpath + for manufacturer in self.model_context: + scaler_path = os.path.join(model_dirpath, manufacturer + "_scaler.pkl") + if not os.path.isfile(scaler_path): + return "Missing scaler file: {}".format(scaler_path) + model_path = os.path.join(model_dirpath, manufacturer + "_predictor.pkl") + if not os.path.isfile(model_path): + return "Missing model file: {}".format(model_path) + + self.model_dirpath = model_dirpath + + def __preprocess(self, disk_days, manufacturer): + """Scales and transforms input dataframe to feed it to prediction model + + Arguments: + disk_days {list} -- list in which each element is a dictionary with key,val + as feature name,value respectively. + e.g.[{'smart_1_raw': 0, 'user_capacity': 512 ...}, ...] + manufacturer {str} -- manufacturer of the hard drive + + Returns: + numpy.ndarray -- (n, d) shaped array of n days worth of data and d + features, scaled + """ + # get the attributes that were used to train model for current manufacturer + try: + model_smart_attr = self.model_context[manufacturer] + except KeyError as e: + RHDiskFailurePredictor.LOGGER.debug( + "No context (SMART attributes on which model has been trained) found for manufacturer: {}".format( + manufacturer + ) + ) + return None + + # from the input json construct a list such that, each element is + # a tuple representing SMART data from one day e.g. (60, 100, 500.2) + days = sorted(disk_days['smart_data'].keys()) + values = [] + for day in days: + curr_day_values = [] + for attr in model_smart_attr: + if 'raw' in attr: + curr_day_values.append( + disk_days['smart_data'][day]['attr'][attr.split('_')[1]]['val_raw'] + ) + elif 'normalized' in attr: + curr_day_values.append( + disk_days['smart_data'][day]['attr'][attr.split('_')[1]]['val_norm'] + ) + elif 'user_capacity' in attr: + curr_day_values.append( + disk_days['capacity_bytes'] + ) + else: + raise ValueError('Unknown attribute found in config') + values.append( + curr_day_values + ) + + # create a numpy structured array i.e. include "column names" and + # "column dtypes" for the input matrix `values` + try: + struc_dtypes = [(attr, np.float64) for attr in model_smart_attr] + disk_days_sa = np.array(values, dtype=struc_dtypes) + except KeyError as e: + RHDiskFailurePredictor.LOGGER.debug( + "Mismatch in SMART attributes used to train model and SMART attributes available" + ) + return None + + # view structured array as 2d array for applying rolling window transforms + # do not include capacity_bytes in this. only use smart_attrs + disk_days_attrs = disk_days_sa[[attr for attr in model_smart_attr if 'smart_' in attr]]\ + .view(np.float64).reshape(disk_days_sa.shape + (-1,)) + + # featurize n (6 to 12) days data - mean,std,coefficient of variation + # current model is trained on 6 days of data because that is what will be + # available at runtime + + # rolling time window interval size in days + roll_window_size = 6 + + # rolling means generator + gen = (disk_days_attrs[i: i + roll_window_size, ...].mean(axis=0) \ + for i in range(0, disk_days_attrs.shape[0] - roll_window_size + 1)) + means = np.vstack(gen) + + # rolling stds generator + gen = (disk_days_attrs[i: i + roll_window_size, ...].std(axis=0, ddof=1) \ + for i in range(0, disk_days_attrs.shape[0] - roll_window_size + 1)) + stds = np.vstack(gen) + + # coefficient of variation + cvs = stds / means + cvs[np.isnan(cvs)] = 0 + + # combine all extracted features + featurized = np.hstack(( + means, + stds, + cvs, + disk_days_sa['user_capacity'][: disk_days_attrs.shape[0] - roll_window_size + 1].reshape(-1, 1) + )) + + # scale features + scaler_path = os.path.join(self.model_dirpath, manufacturer + "_scaler.pkl") + with open(scaler_path, 'rb') as f: + scaler = pickle.load(f) + featurized = scaler.transform(featurized) + return featurized + + @staticmethod + def __get_manufacturer(model_name): + """Returns the manufacturer name for a given hard drive model name + + Arguments: + model_name {str} -- hard drive model name + + Returns: + str -- manufacturer name + """ + for prefix, manufacturer in RHDiskFailurePredictor.MANUFACTURER_MODELNAME_PREFIXES.items(): + if model_name.startswith(prefix): + return manufacturer + # print error message + RHDiskFailurePredictor.LOGGER.debug( + "Could not infer manufacturer from model name {}".format(model_name) + ) + + def predict(self, disk_days): + # get manufacturer preferably as a smartctl attribute + manufacturer = disk_days.get("vendor", disk_days.get("nvme_vendor", None)) + + # if not available then try to infer using model name + if manufacturer is None: + RHDiskFailurePredictor.LOGGER.debug( + '"vendor" field not found in smartctl output. Will try to infer manufacturer from model name.' + ) + if "model_name" not in disk_days.keys(): + RHDiskFailurePredictor.LOGGER.debug( + '"vendor" field not found in smartctl output. Will try to infer manufacturer from model name.' + ) + else: + manufacturer = RHDiskFailurePredictor.__get_manufacturer( + disk_days["model_name"] + ) + + # print error message and return Unknown + if manufacturer is None: + RHDiskFailurePredictor.LOGGER.debug( + "Manufacturer could not be determiend. This may be because \ + DiskPredictor has never encountered this manufacturer before, \ + or the model name is not according to the manufacturer's \ + naming conventions known to DiskPredictor" + ) + return RHDiskFailurePredictor.PREDICTION_CLASSES[-1] + else: + manufacturer = manufacturer.lower() + + # preprocess for feeding to model + preprocessed_data = self.__preprocess(disk_days, manufacturer) + if preprocessed_data is None: + return RHDiskFailurePredictor.PREDICTION_CLASSES[-1] + + # get model for current manufacturer + model_path = os.path.join( + self.model_dirpath, manufacturer + "_predictor.pkl" + ) + with open(model_path, 'rb') as f: + model = pickle.load(f) + + # use prediction for latest day as the output + pred_class_id = model.predict(preprocessed_data)[-1] + return RHDiskFailurePredictor.PREDICTION_CLASSES[pred_class_id] + + class PreditionResult(Enum): GOOD = 0 FAIL = 1 @@ -26,9 +290,32 @@ def simple_prediction(device_data): return PreditionResult.GOOD def main(): + # args to decide which model to use for prediction + parser = argparse.ArgumentParser() + parser.add_argument( + '--predictor-model', '--pm', + type=str, + choices=['redhat', 'prophetstor', 'dummy'], + default='redhat', + ) + args = parser.parse_args() + inp_json = sys.stdin.read() - device_data = json.loads(inp_json) - prediction_result = simple_prediction(device_data) + # fname = 'input_samples/predict_1669.json' + # with open(fname, 'rb') as f: + # device_data = json.load(f) + + if args.predictor_model == 'dummy': + prediction_result = simple_prediction(device_data) + elif args.predictor_model == 'redhat': + # init model + predictor = RHDiskFailurePredictor() + predictor.initialize("{}/models/{}".format(get_diskfailurepredictor_path(), args.predictor_model)) + + # make prediction + prediction_result = predictor.predict(device_data) + elif args.predictor_model == 'prophetstor': + raise NotImplementedError("ProphetStor sample model has not been updated for use on Grafana dashboards") #print(prediction_result) diff --git a/device_prediction/models/redhat/config.json b/device_prediction/models/redhat/config.json new file mode 100644 index 0000000..62a0d82 --- /dev/null +++ b/device_prediction/models/redhat/config.json @@ -0,0 +1,4 @@ +{ +"seagate": ["user_capacity", "smart_1_raw", "smart_5_raw", "smart_7_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_190_raw", "smart_193_raw", "smart_197_raw", "smart_198_raw", "smart_241_raw", "smart_1_normalized", "smart_5_normalized", "smart_7_normalized", "smart_10_normalized", "smart_187_normalized", "smart_188_normalized", "smart_190_normalized", "smart_193_normalized", "smart_197_normalized", "smart_198_normalized", "smart_241_normalized"], +"hgst": ["user_capacity", "smart_1_normalized", "smart_1_raw", "smart_2_normalized", "smart_2_raw", "smart_3_normalized", "smart_3_raw", "smart_4_raw", "smart_5_normalized", "smart_5_raw", "smart_7_normalized", "smart_7_raw", "smart_8_normalized", "smart_8_raw", "smart_9_normalized", "smart_9_raw", "smart_10_normalized", "smart_10_raw", "smart_12_raw", "smart_192_normalized", "smart_192_raw", "smart_193_normalized", "smart_193_raw", "smart_194_normalized", "smart_194_raw", "smart_196_normalized", "smart_196_raw", "smart_197_normalized", "smart_197_raw", "smart_198_raw", "smart_199_raw"] +} diff --git a/device_prediction/models/redhat/hgst_predictor.pkl b/device_prediction/models/redhat/hgst_predictor.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9894d9f553bf10f88a7d942c9962a56e96803350 GIT binary patch literal 2860606 zcmdRXcU)6T@b9srhzj;@K^T@m#&{^I7iiz0c>p{K1!#%TT= zFDT4!s&}BDnSXGoUs$-lvsaLB@Kih9gH51USXh96fM2M-oa;DOUTK!RatHT19LITk zh5309@_?yc;lZKuU&)1s`uUk@|L70$^9cwG2o4%W9>fV%B?(oVC9mGWy(US>Co~}3 zFO-nU_1vqH=ScxU0rDE|HORkVA%Owm?^w){`Lzgu)n;veI@rM@WXV!fJw4&5C2dvpKy`|4;?|B z!Q@xn$K8N{R+hh?S9k=`j=Zje?z>Q(jy%G`y~6$Eh7LN7Xz8^49znsrgwn{ty`h$y zWGrB6NCYWm6v?trs2@?ByxuPn^?oPfvtOSVdzy@E7WUhn7nBR 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