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data-ai

Automate Linux data science environment setup with GPU drivers and ML frameworks.

3|Updated Nov 18, 2025
Search Tags:#setup#GPU#Linux#ML#data-science#env-management
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-linux --skill data-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-ai
Source: https://github.com/pluginagentmarketplace/custom-plugin-linux/tree/main/skills/data-ai
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-linux --skill data-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the setup of Linux-based data science environments, handling GPU drivers, Python environments, ML frameworks, and data tools.

Core Features & Use Cases

  • GPU setup: CUDA, cuDNN, NVIDIA drivers
  • Python Environments: Conda, virtualenv, pyenv
  • ML Frameworks & Tools: TensorFlow, PyTorch, JAX, Jupyter
  • Containerization: Docker with GPU support
  • Use Case: Spin up reproducible ML workspaces for teams and projects

Quick Start

Install the data-ai skill and run the quick-start workflow to bootstrap a GPU-enabled data science environment.

Frequently Asked Questions about data-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up a GPU-ready data science environment on Linux automatically?▼

This skill automates Linux data science environment setup by configuring GPU drivers, CUDA, cuDNN, Python ecosystems via Conda or virtualenv, and ML frameworks like PyTorch and TensorFlow.

Can I configure reproducible ML workspaces with Docker and GPU support?▼

Yes, the skill configures containerization using Docker with GPU support, enabling reproducible ML workspaces for teams across bare metal or cloud instances.

Does this environment management tool support both Conda and virtualenv?▼

Yes, the tool supports Conda, virtualenv, and pyenv to bootstrap Python ecosystems, enabling flexible environment management for data engineering and ML workflows.

What ML frameworks are included when bootstrapping a Linux data science environment?▼

The skill sets up ML frameworks including TensorFlow, PyTorch, and JAX, alongside Jupyter notebooks, enabling GPU-accelerated pipelines and reproducible ML environments.

Do I need PyYAML installed to automate Linux environment setup for data science?▼

Yes, the skill requires PyYAML for config parsing and relies on assets/config.yaml for defaults to automate the setup of Linux data science environments.

What are the limitations of automating GPU driver setup on cloud instances?▼

The setup targets Linux specifically, meaning non-Linux operating systems are unsupported. It focuses on bare metal or cloud instances, requiring compatible NVIDIA GPU hardware for proper setup.

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