torchserve
Model serving engine for PyTorch. Focuses on MAR packaging, custom handlers for preprocessing/inference, and management of multi-GPU worker scaling. (torchserve, mar-file, handler, basehandler, model-archiver, inference-api)
npx skills add majiayu000/claude-skill-registry --skill torchserve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Overview TorchServe is a flexible and easy-to-use tool for serving PyTorch models. It provides capabilities for packaging models, scaling workers based on hardware availability, and managing multiple model versions via a REST/gRPC API. ## When to Use Use TorchServe when you need a production-ready inference server that handles multi-GPU load balancing, request batching, and custom preprocessing/postprocessing logic via Python handlers. ## Decision Tree 1. Do you need custom logic for image resizing or JSON parsing before model inference? - OVERRIDE: `preprocess()` in a class inheriting from `BaseHandler`. 2. Do you have multiple GPUs available? - RELY: On TorchServe's round-robin assignment; check the `gpu_id` in the handler context. 3. Do you want to deploy to a system with limited resources? - CAUTION: TorchServe is in limited maintenance; check environment compatibility. ## Workflows 1. **Packaging and Serving a Model** 1. Write a custom handler or use a default one (e.g., 'image_classifier'). 2. Use `torch-model-archiver` to package the model, weights, and handler into a `.mar` file. 3. Start TorchServe specifying the model store and the initial models to load. 4. Test the e
- Overview
- When to Use
- Decision Tree
- Workflows
- Non-Obvious Insights
- Evidence
- Scripts
- Dependencies
- References
What does the torchserve skill do?
Model serving engine for PyTorch. Focuses on MAR packaging, custom handlers for preprocessing/inference, and management of multi-GPU worker scaling. (torchserve, mar-file, handler, basehandler, model-archiver, inference-api)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill torchserve --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.