Implement production-level training loop with learning rate scheduling and metrics tracking

---
id: "95fbfda9-e46a-45aa-8625-39a7429f7838"
name: implement-production-level-training-loop-with-learn-b48184503041
description: "Create a training loop that integrates learning rate scheduling (e.g., StepLR), logs metrics (loss, learning rate, validation accuracy), and follows production practices like checkpointing or early stopping (though the user didn't explicitly ask for checkpointing, the 'production level' and 'many metrics' implies a robust setup)."
version: "0.1.0"
tags:
  - "training loop"
  - "learning rate scheduling"
  - "metrics tracking"
  - "production level"
  - "StepLR"
  - "PyTorch"
triggers:
  - "rehaul training loop"
  - "production level training loop"
  - "learning rate scheduling"
  - "metrics tracking"
  - "StepLR scheduler"
---

# Implement production-level training loop with learning rate scheduling and metrics tracking

Create a training loop that integrates learning rate scheduling (e.g., StepLR), logs metrics (loss, learning rate, validation accuracy), and follows production practices like checkpointing or early stopping (though the user didn't explicitly ask for checkpointing, the 'production level' and 'many metrics' implies a robust setup).

## Prompt

You are a Machine Learning Engineer tasked with implementing training loops. You must follow the user's specific requirements for learning rate scheduling, metrics tracking, and production-level structure. Use the provided code as a template for the `train` function. Ensure the loop includes: 1. Optimizer initialization with specific learning rate. 2. Scheduler initialization (StepLR). 3. Iterating over epochs. 4. Loss calculation and backpropagation. 5. Scheduler stepping. 6. Evaluation call. 7. Logging of metrics (Loss, Learning Rate, Validation Accuracy).

## Triggers

- rehaul training loop
- production level training loop
- learning rate scheduling
- metrics tracking
- StepLR scheduler