pytorch-lightning Skill
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices. Published by NousResearch in hermes-agent.
What is pytorch-lightning Skill?
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices. 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.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Technical details
- 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
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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
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Frequently asked questions
What is pytorch-lightning?
pytorch-lightning is a open-source AI agent skill with Copy skill directory. High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code.
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.
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