accelerate Skill
Run PyTorch training across GPUs with minimal changes. Published by NousResearch in hermes-agent.
Decision snapshot
Is this a fit?
Data analysis, Includes SKILL.md, Reusable instructions
Compatibility not yet detected.
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Copy skill directory
21 days ago · MIT license
No specific cautions were detected. Review the source and requested permissions before installing.
What is accelerate Skill?
Run PyTorch training across GPUs with minimal changes. 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/accelerate/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 accelerate Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/accelerate/SKILL.md
What it does
Run PyTorch training across GPUs with minimal changes.
Skill instructions
HuggingFace Accelerate - Unified Distributed Training Quick start Accelerate simplifies distributed training to 4 lines of code. Installation: bash pip install accelerate Convert PyTorch script (4 lines): python import torch + from accelerate import Accelerator + accelerator = Accelerator() model = torch.nn.Transformer() optimizer = torch.optim.Adam(model.parameters()) dataloader = torch.utils.data.DataLoader(dataset) + model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) for batch in dataloader: optimizer.zerograd() loss = model(batch) - loss.backward() + accelerator.backward(loss) optimizer.step() Run (single command): bash accelerate launch train.py Common workflows Workflow 1: From single GPU to multi-GPU Original script: python train.py import torch model = torch.nn.Linear(10, 2).to('cuda') optimizer = torch.optim.Adam(model.parameters()) dataloader = torch.utils.data.DataLoader(dataset, batchsize=32) for epoch in range(10): for batch in dataloader: bat
Verified compatibility and discovery
Frequently asked questions
What is accelerate?
accelerate is a open-source AI agent skill with Copy skill directory. Run PyTorch training across GPUs with minimal changes.
Who is accelerate best for?
accelerate is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install accelerate?
Install or run accelerate using Copy skill directory. Check accelerate for the latest setup command.
Is accelerate actively maintained?
accelerate may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
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.
