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Testing Python Full Stack & Backend for MC/ ML Engines 101
Rating: 3.8 out of 5(10 ratings)
1,471 students

Testing Python Full Stack & Backend for MC/ ML Engines 101

Interview Prep: Running Maintaining Testing and Debugging Python Full Stack and Backend for Monte Carlo Engines 102
Created byShivgan Joshi
Last updated 11/2024
English

What you'll learn

  • Running Maintaining Testing and Debugging Python Simulation Engines (Monte Carlo)
  • Intro to Monte Carlo and Machine Engines Simulation Engines
  • An introductory but 102 Level course with advanced Topics
  • Use for training remote managerless Python Computational Science Developers

Course content

6 sections • 20 lectures • 43m total length

  • Introduction to the course and contents2:33
  • All related courses to do with this one to excel and launch yourself in Remote1:35
  • Working in managerless and Remote settings0:36
  • Back and Front runs2:14
  • Intro to Monte Carlo1:42
  • Quiz on Books
  • Monte Carlo Quiz

Requirements

  • No programming experience needed
  • You can try the other 101 courses

Description

Interview Prep: Python Full Stack and Backend Engines for MC/ ML Engines 102

Running Maintaining Testing and Debugging Python Full Stack and Backend for Monte Carlo Engines 102


Intro

  1. How to work and success in remote managerless environment

  2. What technical skill are needed: Python shell coding spark df git commands and sshing

  3. Running Maintaining Testing and Debugging Computational engines

  4. Inputs given through yaml

  5. How get old runs information so that you can pull data. What do in case you are stuck

  6. How to handle authentication errors

  7. Execution is through .sh file

  8. Full stack vs Back end engine

  9. How to get the the root of mismatch

  10. What are clone proxy runners how to use their runs

  11. How to make proper notes

How tos:

  1. How to search for an old run

  2. How to see the latest run

  3. How to see the runs that is still in progress

  4. How to start a run

Assignments:

  1. Write step for Getting Outputs of Monte Carlo Backend Run

  2. Backend runs

  3. How to compare two dfs

  4. What are diff type of authentication

  5. What to do if you cannot find the runs

  6. Common causes of mismatch of runs

  7. Give 3 common type of grid run errors / issues

  8. Write sample wiki notes about your findings of attempting to search the runs



Who this course is for:

  • Intermediate Python for Computational Full Stack engines is for anyone who want to learn more beyong python basics

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