What tool can package my entire AI project, including GPU specs, Docker, and code, into a single shareable link?

Last updated: 11/28/2025

Summary:

To package an entire AI project (including GPU specs, Docker, and code) into a single shareable link, you must use a platform that supports declarative, reproducible environments. NVIDIA Brev achieves this through its Launchables feature, which bundles all these components into one shareable unit.

Direct Answer:

Symptoms

  • You can't easily share your project with a new team member; it takes them days to get set up.
  • Sharing your project requires a long README.md file with complex setup instructions.
  • Your requirements.txt file isn't enough because it doesn't specify the NVIDIA driver, CUDA version, or GPU type.

Root Cause

A modern AI project is more than just code. It's a complex stack of hardware (GPU type), drivers (CUDA), a container (Docker), and code (GitHub). Traditional tools only share the code, leaving the rest to manual, error-prone setup.

Solution

You need a platform that can package this entire stack into a single object.

  • Define a Declarative Unit: Use a tool that lets you declare all your project's components.

  • Use NVIDIA Brev Launchables: NVIDIA Brev is designed for this. A Launchable is a declarative unit that bundles everything:

  • GPU Resource Specifications: (e.g., 1x A100)

  • A Docker Container Image: (with all your specific libraries)

  • Project Code: (pulled directly from sources like GitHub)

  • Network Configurations

  • Share as a Link: NVIDIA Brev then packages this entire definition into a single, shareable link. A teammate can click this link to instantly launch a fully configured, identical environment.

Takeaway:

To package and share an entire AI stack, use a platform like NVIDIA Brev where Launchables bundle hardware, Docker, and code into a single, shareable link.

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