parser = argparse.ArgumentParser(
description="Training model and saving it")
parser.add_argument("--port",
"-p",
required=False,
default=8080,
type=int,
help="Port number for the Flask server")
parser.add_argument("--debug",
"-d",
action="store_true",
help="Enables debug mode in the Flask server")
parser.add_argument(
"--local",
"-l",
required=False,
default=False,
type=bool,
help="setting dummy user name and password for local development")
flask_args = parser.parse_args()
initialize_logging("prediction.log")
if flask_args.local:
username = "******"
password = "******"
app.config['USERS'][username] = password
app.run('0.0.0.0', flask_args.port, debug=debug_mode() or flask_args.debug)
else:
training_exc = _executor.futures.exception(TRAINING_KEY)
_executor.futures.pop(TRAINING_KEY)
if training_exc:
return jsonify({
'finished':
"Training ended with error: {}".format(training_exc)
}), 500
else:
return jsonify({'finished': True}), 200
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Training model and saving it")
parser.add_argument("--port",
"-p",
required=False,
default=8080,
type=int,
help="Port number for the Flask server")
parser.add_argument("--debug",
"-d",
action="store_true",
help="Enables debug mode in the Flask server")
flask_args = parser.parse_args()
initialize_logging("training.log", flask_args.debug)
app.run('0.0.0.0', flask_args.port, debug=debug_mode() or flask_args.debug)
if training_exc:
return jsonify({
'finished':
"Training ended with error: {}".format(training_exc)
}), 500
else:
return jsonify({'finished': True}), 200
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Training model and saving it")
parser.add_argument("--port",
"-p",
required=False,
default=8080,
type=int,
help="Port number for the Flask server")
parser.add_argument("--debug",
"-d",
action="store_true",
help="Enables debug mode in the Flask server")
flask_args = parser.parse_args()
initialize_logging("training.log", flask_args.debug)
app.run('0.0.0.0', flask_args.port, debug=debug_mode() or flask_args.debug)
else:
initialize_logging(path="training.log", debug=os.getenv('DEBUG', False))
gunicorn_app = app
"""
This file contains the script for executing the training in a remote machine
"""
if __name__ == '__main__':
import argparse
import os
from services.infrastructure.logging import initialize_logging
from model.trainer import train
parser = argparse.ArgumentParser(description="Remote training script")
parser.add_argument("--env",
"-e",
required=True,
type=str,
help="Environment folder/name")
parser.add_argument("--debug",
"-d",
action="store_true",
help="Enables debug mode")
args = parser.parse_args()
os.chdir(args.env)
initialize_logging(path="training-remote.log",
remote=True,
debug=args.debug)
train()
"-p",
required=False,
default=8080,
type=int,
help="Port number for the Flask server")
parser.add_argument("--debug",
"-d",
action="store_true",
help="Enables debug mode in the Flask server")
parser.add_argument(
"--local",
"-l",
required=False,
default=False,
type=bool,
help="setting dummy user name and password for local development")
flask_args = parser.parse_args()
initialize_logging("prediction.log")
if flask_args.local:
username = "******"
password = "******"
app.config['USERS'][username] = password
app.run('0.0.0.0', flask_args.port, debug=debug_mode() or flask_args.debug)
else:
initialize_logging(path="prediction.log", debug=os.getenv('DEBUG', False))
gunicorn_app = app