Commit 3cabc89e authored by dasharatha.vamshi's avatar dasharatha.vamshi

init

parent 7363b200
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\ No newline at end of file
FROM python:3.7-slim
ADD . /opt
WORKDIR /opt
RUN pip install -r requirements.txt
CMD python main.py
#---------------Service Configurations----------------#
SERVICE_CONFIG:
LOG_LEVEL: info
LOG_HANDLER_NAME: ModelObject
LOGSTASH_HOST: 192.168.1.47
LOGSTASH_PORT: 5000
#--------------System Configurations--------------------#
# for testing use /temp/mnt/ilens/ai-jobs/
SYSTEM_CONFIG:
shared_mount_base_ai_job: /mnt/ilens/ai-jobs/
default_run_id: default_run_id
#----------------------If read conf from mongo------------#
FOR_EACH_MONGO_CONFIG:
READ_FROM_MONGO: true
MONGO_URI: mongodb://192.168.0.210:27017
MONGO_DB: iLensAiPipeline
MONGO_RUN_COLL: runMetadata
MONGO_SITE_COLL: siteMetadata
import traceback
from scripts.common.config_parser import *
from scripts.common.constants import ModelObjectConstants, ComponentExceptions
from scripts.common.logsetup import logger
from sklearn.ensemble import RandomForestRegressor
import pickle
class ModelObject:
def __init__(self, model_object, component_out_dir):
self.model_object = model_object
self.component_out_dir = component_out_dir
def random_forest(self):
try:
logger.info("Creating Model object for the model " + self.model_object)
rf = RandomForestRegressor()
filename = 'random_forest.pkl'
logger.info("Pickling the model.....")
pickle.dump(rf, open(os.path.join(self.component_out_dir, filename), 'wb'))
return True
except Exception as e:
logger.info(traceback.format_exc())
raise Exception(e)
if __name__ == '__main__':
try:
obj = ModelObject(config['model_name'], config['component_output_dir'])
if config['model_name'] == ModelObjectConstants.RANDOMFOREST:
val = obj.random_forest()
if val:
if len(os.listdir(config['component_output_dir'])) > 0:
logger.info("File Downloaded Successfully")
else:
logger.info("The output directory is empty")
else:
logger.info("Model Object Download Component Failed")
logger.info(traceback.format_exc())
except:
logger.info("Model Object Download Component Failed")
logger.info(traceback.format_exc())
#!/usr/bin/env python
import os
import sys
import yaml
import json
from pymongo import MongoClient, DESCENDING
from scripts.common.constants import ModelObjectConstants
config_path = os.path.join(os.getcwd(), "conf", "configuration.yml")
if os.path.exists(config_path):
sys.stderr.write("Reading config from --> {}".format(config_path))
sys.stderr.write("\n")
with open(config_path, 'r') as stream:
_config = yaml.safe_load(stream)
else:
sys.stderr.write("Configuration not found...")
sys.stderr.write("Exiting....")
sys.exit(1)
# uncomment for Testing
os.environ['pipeline_id'] = "pipe1"
os.environ['model_name'] = 'RANDOMFOREST'
# ------------------------ Configurations -----------------------------------------------------------------------------
pipeline_id = os.environ.get('PIPELINE_ID', default="pipeline_313")
shared_mount_base_ai_job = os.environ.get("shared_mount_base_ai_job",
_config.get("SYSTEM_CONFIG", {}).get('shared_mount_base_ai_job'))
# read from $shared_mount_base_ai_job/$pipeline_id/run.config
# run_id_path = shared_mount_base_ai_job + "/" + pipeline_id + "/run_config.json"
run_id_path = os.path.join(shared_mount_base_ai_job, pipeline_id, "run_config.json")
try:
sys.stderr.write("Checking for run id parameters at path " + run_id_path + "\n")
with open(run_id_path) as f:
run_id_param = json.load(f)
run_id = run_id_param['run_id']
except Exception as e:
sys.stderr.write("No run_config.json file is there so keeping run id as default_run_id " + "\n")
run_id = _config.get("SYSTEM_CONFIG", {}).get('default_run_id')
# $shared_mount_base_ai_job/$pipeline_id/$run_id/PreprocessWeatherData/input/$artifact_Name component_output_dir =
# shared_mount_base_ai_job + "/" + pipeline_id + "/" + run_id + "/" + GetDataFromStoreConstants.NEXT_COMPONENT +
# "/input"
component_output_dir = os.path.join(shared_mount_base_ai_job, pipeline_id, run_id,
ModelObjectConstants.NEXT_COMPONENT, "input")
model_name = os.environ.get('model_name', default="pipeline_313").lower()
BASE_LOG_PATH = os.path.join(os.getcwd(), "logs")
if not os.path.exists(os.path.join(os.getcwd(), 'logs')):
os.mkdir(os.path.join(os.getcwd(), 'logs'))
LOG_LEVEL = os.environ.get("LOG_LEVEL", _config.get('SERVICE_CONFIG', {}).get("LOG_LEVEL", "INFO")).upper()
LOG_HANDLER_NAME = _config.get('SERVICE_CONFIG', {}).get("LOG_HANDLER_NAME", "ModelObject")
ENABLE_LOGSTASH_LOG = os.environ.get("ENABLE_LOGSTASH_LOG", 'False').lower()
LOGSTASH_HOST = _config.get('SERVICE_CONFIG', {}).get('LOGSTASH_HOST')
LOGSTASH_PORT = str(_config.get('SERVICE_CONFIG', {}).get('LOGSTASH_PORT'))
# os.environ['azure_file_path'] = '/data/model/tested/test1.pkl'
# os.environ['local_file_path'] = r'E:\iLens-AI\azure-file-download\StandardScaler.pkl'
config = {
'pipeline_id': pipeline_id,
'run_id': run_id,
'shared_mount_base_ai_job': shared_mount_base_ai_job,
'component_output_dir': component_output_dir,
'model_name': model_name
}
if not os.path.exists(config['shared_mount_base_ai_job']):
sys.stderr.write("Shared path does not exist!" + "\n")
sys.stderr.write("Creating path --> {}".format(config['shared_mount_base_ai_job'] + "\n"))
os.makedirs(config['shared_mount_base_ai_job'])
if not os.path.exists(config['component_output_dir']):
sys.stderr.write("component_output_dir does not exist!" + "\n")
sys.stderr.write("Creating path --> {}".format(config['component_output_dir'] + "\n"))
os.makedirs(config['component_output_dir'])
#!/usr/bin/env python
class ModelObjectConstants:
NEXT_COMPONENT = "ModelFitting"
COMPONENT_NAME = "ModelObject"
HTTP = "http://"
RANDOMFOREST = "randomforest"
LOG_VAR_MESSAGE = "\n" + "#" * 25 + "\n" + "{}" + "\n" + "#" * 25 + "\n" + "{}\n"
class ComponentExceptions:
INVALID_AZURE_FILE_PATH_EXCEPTION = "AZURE PATH ERROR"
INVALID_LOCAL_FILE_PATH_EXCEPTION = "No File in the local path"
INVALID_ARTIFACT_BASE_PATH_EXCEPTION = "Artifact base path value is missing"
INVALID_AZURE_FILE_NAME_EXCEPTION = "Artifact name is missing"
INVALID_CONTAINER_NAME = "Container name is missing"
import os
import logging
from logging.handlers import RotatingFileHandler
from logstash_async.handler import AsynchronousLogstashHandler
from scripts.common.config_parser import LOG_LEVEL, LOG_HANDLER_NAME, BASE_LOG_PATH
from scripts.common.config_parser import LOG_LEVEL, LOG_HANDLER_NAME, BASE_LOG_PATH, LOGSTASH_HOST, LOGSTASH_PORT, ENABLE_LOGSTASH_LOG
DEFAULT_FORMAT = '%(asctime)s %(levelname)5s %(name)s %(message)s'
DEBUG_FORMAT = '%(asctime)s %(levelname)5s %(name)s [%(threadName)5s:%(filename)5s:%(funcName)5s():%(lineno)s] %(' \
'message)s '
EXTRA = {}
FORMATTER = DEFAULT_FORMAT
if LOG_LEVEL.strip() == "DEBUG":
FORMATTER = DEBUG_FORMAT
logging.trace = logging.DEBUG - 5
logging.addLevelName(logging.DEBUG - 5, 'TRACE')
class ILensLogger(logging.getLoggerClass()):
def __init__(self, name):
super().__init__(name)
def trace(self, msg, *args, **kwargs):
if self.isEnabledFor(logging.trace):
self._log(logging.trace, msg, args, **kwargs)
def get_logger(log_handler_name):
"""
Purpose : To create logger .
:param log_handler_name: Name of the log handler.
:return: logger object.
"""
log_path = os.path.join(BASE_LOG_PATH, log_handler_name + ".log")
logging.setLoggerClass(ILensLogger)
_logger = logging.getLogger(log_handler_name)
_logger.setLevel(LOG_LEVEL.strip().upper())
log_handler = logging.StreamHandler()
log_handler.setLevel(LOG_LEVEL)
formatter = logging.Formatter(FORMATTER)
log_handler.setFormatter(formatter)
handler = RotatingFileHandler(log_path, maxBytes=10485760,
backupCount=5)
handler.setFormatter(formatter)
_logger.addHandler(log_handler)
_logger.addHandler(handler)
if ENABLE_LOGSTASH_LOG == 'true' and LOGSTASH_PORT is not None and LOGSTASH_HOST is not None and LOGSTASH_PORT.isdigit():
_logger.addHandler(AsynchronousLogstashHandler(LOGSTASH_HOST, int(LOGSTASH_PORT), database_path=None))
return _logger
logger = get_logger(LOG_HANDLER_NAME)
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