pytorch-lightning Skill
Clean training loops with built-in distributed support. Published by NousResearch in hermes-agent.
Decision snapshot
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Data analysis, Includes SKILL.md, Reusable instructions
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30 days ago · MIT license
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What is pytorch-lightning Skill?
Clean training loops with built-in distributed support. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Declared author: Orchestra Research
- Declared license: MIT
- Source file: optional-skills/mlops/pytorch-lightning/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use pytorch-lightning Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/pytorch-lightning/SKILL.md
What it does
Clean training loops with built-in distributed support.
Skill instructions
PyTorch Lightning - High-Level Training Framework Quick start PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility. Installation: bash pip install lightning Convert PyTorch to Lightning (3 steps): python import lightning as L import torch from torch import nn from torch.utils.data import DataLoader, Dataset Step 1: Define LightningModule (organize your PyTorch code) class LitModel(L.LightningModule): def init(self, hiddensize=128): super().init() self.model = nn.Sequential( nn.Linear(28 28, hiddensize), nn.ReLU(), nn.Linear(hiddensize, 10) ) def trainingstep(self, batch, batchidx): x, y = batch yhat = self.model(x) loss = nn.functional.crossentropy(yhat, y) self.log('trainloss', loss) Auto-logged to TensorBoard return loss def configureoptimizers(self): return torch.optim.Adam(self.parameters(), lr=1e-3) Step 2: Create data trainloader = DataLoader(traindataset, batchsize=32) Step 3: Train with Trainer (handles everything else!) trainer = L.Tr
Verified compatibility and discovery
Frequently asked questions
What is pytorch-lightning?
pytorch-lightning is a open-source AI agent skill with Copy skill directory. Clean training loops with built-in distributed support.
Who is pytorch-lightning best for?
pytorch-lightning is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install pytorch-lightning?
Install or run pytorch-lightning using Copy skill directory. Check pytorch-lightning for the latest setup command.
Is pytorch-lightning actively maintained?
pytorch-lightning may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
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Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
