I am trying to deploy a machine learning model on AWS EC2 instance using Flask. These are sklearn's fitted Random Forest models that are pickled using joblib. When I host Flask on localhost and load them into memory everything runs smoothly. However, when I deploy it on the apache2 server using mod_wsgi, joblib works sometimes(i.e. the models are loaded using joblib sometimes) and the other times the server just hangs. There is no error in logs. Any ideas would be appreciated.
Here is the relevant code that I am using:
# In[49]:
from flask import Flask, jsonify, request, render_template
from datetime import datetime
from sklearn.externals import joblib
import pickle as pkl
import os
# In[50]:
app = Flask(__name__, template_folder="/home/ubuntu/flaskapp/")
# In[51]:
log = lambda msg: app.logger.info(msg, extra={'worker_id': "request.uuid" })
# Logger
import logging
handler = logging.FileHandler('/home/ubuntu/app.log')
handler.setLevel(logging.ERROR)
app.logger.addHandler(handler)
# In[52]:
#app.route('/')
def host_template():
return render_template('Static_GUI.html')
# In[53]:
def load_models(path):
model_arr = [0]*len(os.listdir(path))
for filename in os.listdir(path):
f = open(path+"/"+filename, 'rb')
model_arr[int(filename[2:])] = joblib.load(f)
print("Classifier ", filename[2:], " added.")
f.close()
return model_arr
# In[54]:
partition_limit = 30
# In[55]:
print("Dictionaries being loaded.")
dict_file_path = "/home/ubuntu/Dictionaries/VARR"
dictionaries = pkl.load(open(dict_file_path, "rb"))
print("Dictionaries Loaded.")
# In[56]:
print("Begin loading classifiers.")
model_path = "/home/ubuntu/RF_Models/"
classifier_arr = load_models(model_path)
print("Classifiers Loaded.")
if __name__ == '__main__':
log("/home/ubuntu/print.log")
print("Starting API")
app.run(debug=True)
I was stuck with this for quite sometime. Posting the answer in case someone runs into this problem. Using print statements and looking at logs I narrowed the problem down to joblib.load statement. I found this awesome blog: http://blog.rtwilson.com/how-to-fix-flask-wsgi-webapp-hanging-when-importing-a-module-such-as-numpy-or-matplotlib
The idea of using a global process group fixed the problem. That forced the use of main interpreter just as the top comment on that blog page mentions.
Related
I'm using Flask on a project on an embedded system and I'm having performance issues. I'm running gunicorn with one eventlet worker by running:
gunicorn -b 0.0.0.0 --worker-class eventlet -w 1 'app:create_app()'
The problem I'm facing is that, when the MQTT messages start to pour with more cadence, the application starts to use almost all the CPU I have available. My initial thought was that I was handling the messages not ideally but, I even took out my handler, and just receive the messages, and the problem still persists.
I have another python application that subscribes to the same information with the paho client and this is not an issue, so I'm assuming I'm missing something on my Flask application and not the information itself.
My code is:
import eventlet
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager, current_user
from flask_socketio import SocketIO
from flask_mqtt import Mqtt
eventlet.monkey_patch()
#USERS DB
db_alchemy = SQLAlchemy()
#socketIO
socketio = SocketIO(cors_allowed_origins="*", async_mode='eventlet')
# MQTT
mqtt_client = Mqtt()
'''
APPLICATION CREATION
'''
def create_app():
app = Flask(__name__)
if app.config["ENV"] == "production":
app.config.from_object("config.ProductionConfig")
else:
app.config.from_object("config.DevelopmentConfig")
#USERS DB
db_alchemy.init_app(app)
#LoginManager
login_manager = LoginManager()
login_manager.login_view = "auth.login"
login_manager.init_app(app)
#SOCKETIO
socketio.init_app(app)
#FLASK-MQTT
app.config['MQTT_BROKER_URL'] = 'localhost' #
app.config['MQTT_BROKER_PORT'] = 1883
app.config['MQTT_KEEPALIVE'] = 20
app.config['MQTT_TLS_ENABLED'] = False
mqtt_client.init_app(app)
return app
#MQTT
#mqtt_client.on_connect()
def mqtt_on_connect():
mqtt_client.subscribe('testTopic/#', 0)
#mqtt_client.on_disconnect()
def mqtt_on_disconnect():
loggerMqtt.warning(' > Disconnected from broker')
#mqtt_client.on_subscribe()
def mqtt_on_subscribe(client, obj, mid, granted_qos):
pass
#mqtt_client.on_message()
def mqtt_on_message(client, userdata, message):
pass
#mqtt_topicSplitter(client, userdata, message)
As you can see my handler mqtt_topicSplitter is commented but I'm still having performance issues. I've tried adding an sleep command [eventlet.sleep(0.1)] on the on_message handler which solved the CPU consumption problem but resulted on my application being constantly kicked from the broker.
I also tried using other workers (gevent, asyncio, ..) without success. Using the Flask development server is not an option, since is not recommended for production.
I'm sorry if I wasn't clear, but I'm not an expert, please feel free to ask me any questions if needed.
Thanks in advance.
I am not able to get http://127.0.0.1:5000/movie Url from the browser.
Every time it gives 404. The only time it worked was with URL from hello world.
I am trying to run a recommender system deployment solution with flask, based on https://medium.com/analytics-vidhya/build-a-movie-recommendation-flask-based-deployment-8e2970f1f5f1.
I have tried uninstall flask and install again but nothing seems to work.
Thank you!
from flask import Flask,request,jsonify
from flask_cors import CORS
import recommendation
app = Flask(__name__)
CORS(app)
#app.route('/movie', methods=['GET'])
def recommend_movies():
res = recommendation.results(request.args.get('title'))
return jsonify(res)
if __name__=='__main__':
app.run(port = 5000, debug = True)
http://127.0.0.1:5000/movie
be careful not writing '/' after movie like http://127.0.0.1:5000/movie/
I have created a Flask prediction app (within Google Colab) and when I am trying to run it post adding all the dependencies within the colab environment I am getting the url but when I click on it it show site cannot be reached.
I have the Procfile, the pickled model and the requirements text file but for some reason its not working. Also, I tried deploying this app using Heroku and it met the same fate where I got the app error.
For more context please visit my github repo.
Any help or guidance will be highly appreciated.
from flask import Flask, url_for, redirect, render_template, jsonify
from pycaret.classification import*
import pandas as pd
import numpy as np
import pickle
app = Flask(__name__)
model = load_model('Final RF Model 23JUL2021')
cols = ['AHT','NTT','Sentiment','Complaints','Repeats']
#app.route('/')
def home():
return render_template("home.html")
#app.route('/predict',methods=['POST'])
def predict():
int_features = [x for x in request.form.values()]
final = np.array(int_features)
data_unseen = pd.DataFrame([finak], columns = cols)
prediction = predict_model(model, data=data_unseen, round=0)
prediction = int(prediction.Label[0])
return render_template('home.html',pred='Predicted Maturiy Level is{}'.format(prediction))
#app.route('/predict_api',methods=['POST'])
def predict_api():
data = request.get_json(force=True)
data_unseen = pd.DataFrame([data])
prediction = predict_model(model, data=data_unseen)
output = prediction.Label[0]
return jsonify(output)
if __name__ == '__main__':
app.run(debug=True)
You cannot run flask app same as in your machine. You need to use flask-ngrok.
!pip install flask-ngrok
from flask_ngrok import run_with_ngrok
[...]
app = Flask(__name__)
run_with_ngrok(app)
[...]
app.run()
You can't use debug=True parameter in ngrok.
I am currently deploying a Flask app to AWS Beanstalk, and I am trying to log out some stuff in the application (print), but I am not sure how to view it within Beanstalk, do guide me along, thank you!
The print should be in /var/log/web.output.log. However, they can show up with a delay. Thus, I found that its easier to hook up into gunicorn logger from flask which update web.output.log in real time.
Below is sample application.py that you can test it out and see how to set it up:
import os
from flask import Flask, Blueprint
from flask_restful import Api
from datetime import datetime
import logging
application = Flask(__name__)
#application.route("/")
def hello():
current_time = datetime.now().strftime("%H:%M:%S")
print(f"print from the app {current_time}")
application.logger.info(f"info from hello {current_time}")
application.logger.error(f"error from hello {current_time}")
return "<h1 style='color:blue'>Hello There!</h1>"
if __name__ == '__main__':
application.run()
else:
gunicorn_logger = logging.getLogger('gunicorn.error')
application.logger.handlers = gunicorn_logger.handlers
application.logger.setLevel(logging.INFO)
I want to run RASA with --enable-api inside the python code rather than the command line. Below is my code which is not working. Let me know how can i do that. The issue is once i hit the service because the channel is 'cmdline' it comes to the command line. I don't know how to resolve this.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import rasa_core
from rasa_core.agent import Agent
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
from rasa_core.run import serve_application
from rasa_core import config
from rasa_core.policies.fallback import FallbackPolicy
from rasa_core.policies.keras_policy import KerasPolicy
from flask import Flask
from flask_cors import CORS, cross_origin
app = Flask(__name__)
CORS(app)
logger = logging.getLogger(__name__)
#app.route("/conversations/default/respond",methods=['POST'])
def run_weather_bot(serve_forever=True):
logging.basicConfig(level="ERROR")
interpreter = RasaNLUInterpreter('C:\\xxxx_nlu\\models\\nlu\\default\\weathernlu')
action_endpoint = EndpointConfig(url="http://xxx.xx.xx.xxx:5055/webhook")
agent = Agent.load('C:\\xxxx_nlu\\models\\dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
rasa_core.run.serve_application(agent,channel='cmdline')
return agent
if __name__ == '__main__':
app.run("xxx.xx.xx.xxx",5005,debug=True)
You're calling rasa bot in the command line in your run_weather_bot function using below command.
rasa_core.run.serve_application(agent,channel='cmdline')
As you can see its serving as command line application.
I have made some changes in your code for a conversation with rasa chatbot. You can refer AGENT documentation and Weather bot article for connection of RASA agent and how RASA agent handles the input message.
def rasa_agent():
interpreter = RasaNLUInterpreter("Path for NLU")
action_endpoint = EndpointConfig(url="Webhook URL")
agent = Agent.load('Path to Dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
## Next line runs the rasa in commandline
# rasa_core.run.serve_application(agent,channel='cmdline')
return agent
#app.route("/conversations/default/respond",methods=['POST'])
def run_weather_bot(serve_forever=True):
agent = rasa_agent() # calling rasa agent
## Collect Query from POST request
## Send Query to Agent
## Get Response of BOT
output = {} ## Append output
return jsonify(output)