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Python program on Google CoLAB

Krishnaji
Krishnaji in Python client
Hi all,

I have written a code to capture one min OHLCV data in real time. Its running fine in my system. As I have to run it from 9.15 am to 3.30 pm, in between some times I am facing problem with my Internet or system or power failure. To avoid all these I want to run my python file on online platforms. I have checked Google CoLABs. I am stuck at installing kiteconnect module it self. Plz help, how to install all the packages and run the python files in CoLABs. Any other suggestion of online platform where I can run files easily, other than Google CoLABs I would like to try.

Thank You

from
Krishna
Tagged:
  • sujith
    sujith
    Can you elaborate?
  • sujith
    sujith
    We can't help you if there is a piece of partial information. Tell us the exact steps to reproduce and include stacktrace in the query.
  • ankitjoshi
    ankitjoshi
    Hi,

    You can run the below-mentioned lines in the cell of your Google Colab Notebook to install and import kiteconnect! 
    try:
      from kiteconnect import KiteConnect
    except ImportError:
      !pip install kiteconnect
      from kiteconnect import KiteConnect
  • kavanlimbasiya
    kavanlimbasiya
    The above solution is cumbersome. installation of libraries in colab doesn't persist after the session is terminated. so you have to install every time you run a code which is time consuming. any other solution?
  • KamalChhirang
    KamalChhirang edited June 2021
    Please also remember that Google colab can get disconnected anytime. You can buy Colab pro for 10$/month to reduce the chances of disconnection.

    You can also use GCP, you will get 300$ credit on signup. You can run your code on the Mumbai server for low latency.
  • jayaprakashv2005
    jayaprakashv2005
    in desktop, google colab runs for 6Hrs+ easily (market duration), in mobile it disconnects frequently
  • DD1365
    DD1365
    Why don't you simply deploy your code on an AWS cloud virtual machine for free for 1 year. Its simple and easy, and once you configure the virtual machine and deploy your code, you won't have to bother again.
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