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

🚀 Course Title: Python Full Stack and Backend Engines for MC/ ML Engines 102

🛠️ Course Headline: Running, Maintaining, Testing, and Debugging Python Full Stack and Backend for Monte Carlo Engines 102 with Shivgan Joshici

Understanding the Course: Python Full Stack and Backend Engines for MC/ ML Engines 102

This course is meticulously designed to equip you with the practical skills necessary to navigate, maintain, test, and debug Python full stack and backend engines in a remote, managerless environment. You'll dive deep into essential technical skills such as Python shell coding, Spark DataFrame operations, Git commands, and SSH (Secure Shell) connections.

What You'll Learn:

• Operating in a Remote Environment: Learn how to work effectively and successfully without the traditional oversight of a manager.
• Technical Skills: Master Python shell coding, Spark DataFrame manipulation, Git commands for version control, and SSH for secure remote access.
• Running and Maintaining Computational Engines: Gain insights into how to initiate, manage, test, and troubleshoot computational engines efficiently.
• Data Inputs and Outputs: Understand how to handle inputs provided through YAML configurations and extract meaningful data from old runs when necessary.
• Authentication Errors and Resolution: Learn to manage authentication errors and navigate around such obstacles.
• Execution: Execute your commands through shell scripts (.sh files) and distinguish between full stack and backend engine operations.
• Identifying Mismatches and Utilizing Proxy Runners: Pinpoint discrepancies in your runs and learn how to leverage clone, proxy, and runner processes effectively.
• Keeping Detailed Notes: Develop the habit of making proper notes to document your findings accurately, which is crucial for problem-solving.

Mastering the Art of Execution

With a focus on practical application, you'll learn how to:

• Search for an Old Run: Discover methods to efficiently locate past executions within the system.
• View Latest Run: Learn to access and understand the latest run in your project.
• Monitor In-Progress Runs: Gain knowledge on how to track runs that are currently being processed.
• Initiate a New Run: Understand the steps required to start a new batch of computations.

Hands-On Assignments

Through real-world tasks, you'll apply your skills to solve complex problems:

1. Getting Outputs from Monte Carlo Backend Run: Step-by-step guide for extracting outputs from a backend run.
2. Backend Runs: Engage with the backend engine execution process.
3. Comparing DataFrames: Learn to compare two DataFrames in Spark.
4. Understanding Authentication Mechanisms: Explore different types of authentication and their implications.
5. Troubleshooting Disappeared Runs: What to do when you can't find the runs you expect.
6. Identifying Common Causes of Run Mismatches: Understand the typical reasons behind mismatched run results.
7. Diving into Grid Run Errors/Issues: Identify and address three common types of errors encountered during grid runs.
8. Writing Wiki Notes: Document your findings from searching for and attempting to access past runs, providing a valuable resource for fellow engineers.

Join us in this comprehensive course where you'll not only learn the intricacies of Python full stack and backend operations but also how to apply these skills effectively in a Monte Carlo simulation environment. 🧪💪 Prepare to elevate your Python expertise and become a proficient backend engineer with real-world applications and scenarios! 🚀💻