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python-ml-deps

Install ML/AI Python dependencies with uv and CUDA-aware index URLs.

17|3|Updated Apr 3, 2026
Search Tags:#uv#pytorch#cuda#bitsandbytes#flash-attention#rapids#ml-deps
One-click install
npx skills add https://github.com/jayll1303/AIEKit --skill python-ml-deps
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-ml-deps
Source: https://github.com/jayll1303/AIEKit/tree/main/.kiro/skills/python-ml-deps
Command: npx skills add https://github.com/jayll1303/AIEKit --skill python-ml-deps

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Install ML libraries (PyTorch, Flash-Attention, DeepSpeed, bitsandbytes, xformers, RAPIDS, llama-cpp-python) with uv while avoiding CUDA/CuDNN driver conflicts and version mismatches across the stack.

Core Features & Use Cases

  • Install ML libraries with CUDA-aware wheels and uv index configuration for reproducible, GPU-ready environments.
  • Diagnose and resolve CUDA toolkit, driver, and cuDNN compatibility issues when building or deploying ML stacks.
  • Prepare Python projects with pyproject.toml snippets and extras to pin CUDA-enabled dependencies.

Quick Start

Install ML dependencies with uv and CUDA-aware index URLs to set up a CUDA-enabled PyTorch workflow.

Frequently Asked Questions about python-ml-deps

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

FAQPage Schema
How do I install PyTorch with CUDA wheels using uv without version conflicts?▼

Install PyTorch with CUDA-aware uv index URLs to pin specific wheels and extras in your pyproject.toml, resolving NVIDIA CUDA version conflicts across your ML stack.

Why does Flash-Attention or bitsandbytes fail to build with my CUDA toolkit?▼

Flash-Attention and bitsandbytes fail due to CUDA toolkit, driver, and cuDNN compatibility mismatches. Configure uv index URLs to pin CUDA-enabled wheels and resolve these version conflicts.

Can I use uv to configure RAPIDS and xformers with a CUDA-enabled PyTorch stack?▼

Yes, uv configures RAPIDS and xformers alongside PyTorch by applying CUDA-aware index URLs and pyproject.toml extras to pin compatible CUDA-enabled dependencies.

What's the best way to set up llama-cpp-python for GPU inference with uv?▼

Configure llama-cpp-python for GPU inference by applying uv index configuration with CUDA-aware URLs, ensuring the CUDA toolkit and PyTorch stack versions match to prevent driver conflicts.

Do I need a specific NVIDIA driver version before installing ML dependencies with uv?▼

You need a compatible CUDA toolkit, matching NVIDIA driver, and cuDNN version before using uv. The Skill diagnoses these compatibility issues to ensure your PyTorch and ONNX Runtime stack functions correctly.

How does uv index configuration help resolve cuDNN driver conflicts for ML libraries?▼

uv index configuration pins specific CUDA-enabled wheels and pyproject.toml extras, ensuring reproducible GPU-ready environments by locking compatible versions of ML libraries like DeepSpeed and RAPIDS.

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