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

Testing Python Full Stack & Backend for MC/ ML Engines 101, Running Maintaining Testing and Debugging Python Full Stack and Backend for Monte Carlo Engines 102.

Course Description

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