ai-agent

pytorch-lr-finder: Optimize Learning Rates

Find optimal learning rates with pytorch-lr-finder, a tool for founders using PyTorch
1,006 stars122 forksPythonGuide quality 8/10Updated 6/24/2025100% free · open source
What it does

Automates the process of finding the optimal learning rate for a PyTorch model by performing a learning rate range test

When to use it
  • When training a new PyTorch model and unsure about the ideal learning rate
  • When experiencing underfitting or overfitting issues due to a suboptimal learning rate
  • When comparing the performance of different PyTorch models with varying learning rates
Ready-to-paste prompt
lr_finder = LRFinder(model=resnet18(), optimizer=torch.optim.SGD(model.parameters(), lr=1e-6)); lr_finder.find(learning_rate=1e-6, end_lr=1, num_iter=100); lr_finder.plot()
Heads up: Make sure to specify the correct model and optimizer when initializing the LRFinder instance, as the learning rate range test will be performed on the provided model and optimizer
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/pytorch-lr-finder && curl -fsSL https://workflowstacks.com/api/skills/pytorch-lr-finder/claude-skill -o ~/.claude/skills/pytorch-lr-finder/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

Quick Actions
Details
Creator
davidtvs
Language
Python
Category
ai-agent
Published
11/20/2018

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