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Pytorch Deployment

Checked
tondevrel·vlatest·ai-ml·MIT

Automates PyTorch model deployment to cloud environments, streamlining inference serving and scaling workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add tondevrel/scientific-agent-skills --skill pytorch-deployment -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

tondevrel

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

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  • Pinned to a commit: passed

    Scanned tondevrel/scientific-agent-skills at 6e89d9e8c8.

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Commit
6e89d9e8c8
Manifest
skills/pytorch-deployment/SKILL.md
Scanned
1 file, 138 lines
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About this skill

What it does

This skill automates the deployment of PyTorch models. It handles tasks such as containerizing a model, pushing images to registries, and deploying them on various platforms. The goal is to streamline the process of making PyTorch models accessible for inference or other applications.

When to use it

  • You've trained a PyTorch model and need to make it available for real-time predictions.
  • You want to automate the process of updating your deployed PyTorch models with new versions.
  • You are building a scalable machine learning pipeline that requires consistent deployment practices.
  • You need to deploy PyTorch models across different environments (e.g., cloud, edge devices).

Key capabilities

  • Containerization of PyTorch models
  • Image registry pushing
  • Deployment on various platforms

Example prompts

  • "Deploy my PyTorch model located in /app/model.pth to Docker Hub with the tag latest."
  • "Create a container image for my PyTorch inference server and push it to AWS ECR."
  • “Update the deployed version of my PyTorch model.”

Tips & gotchas

The skill requires familiarity with Docker and cloud deployment platforms. Ensure your PyTorch environment is properly configured before attempting deployment.