ML EngineerTraining PipelinesIntermediateSingle prompt

Learning Rate Finder AI Prompt

This prompt implements a learning rate range test and uses its results to configure an effective learning rate schedule, including a 1-cycle policy. It is meant to replace guesswork with a data-driven way to pick a stable and performant learning rate.

Prompt text
Implement a learning rate range test (LR finder) to find the optimal learning rate for this model.

Based on the Smith (2017) cyclical learning rate paper approach:

1. LR range test implementation:
   - Start with a very small LR (1e-7) and exponentially increase to a large LR (10) over 100–200 iterations
   - Log loss at each step
   - Stop early if loss explodes (> 4× minimum loss)

2. Plot the LR finder curve:
   - x-axis: learning rate (log scale)
   - y-axis: smoothed loss (EMA smoothing factor=0.05)
   - Annotate: the point of steepest descent (best LR), the point where loss starts diverging (max LR)

3. Recommended LR selection rules:
   - For standard training: use the LR at steepest loss descent ÷ 10
   - For 1-cycle policy: use the LR at steepest descent as max_lr, and max_lr ÷ 10 as initial_lr

4. Implement 1-cycle LR scheduler using the found LR:
   - Warmup: linear increase from max_lr/10 to max_lr over 30% of training
   - Decay: cosine anneal from max_lr to max_lr/10000 over remaining 70%

5. Reset model weights after the LR finder (do not use weights from the search)

Return: LR finder code, plot code, and 1-cycle scheduler setup using the found optimal LR.

When to use this prompt

Use case 01

when you want to tune learning rate systematically instead of guessing

Use case 02

when training is unstable or converges too slowly

Use case 03

when you want a Smith-style LR finder and 1-cycle schedule

Use case 04

when you need plots and clear LR selection rules

What the AI should return

LR finder code, plotting code, recommended LR selection guidance, and a 1-cycle scheduler configured from the discovered range.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Training Pipelines.

Frequently asked questions

What does the Learning Rate Finder prompt do?+

It gives you a structured training pipelines starting point for ml engineer work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for ml engineer workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Learning Rate Finder is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Custom Loss Function, Dataset Pipeline Builder, Distributed Training Setup.