ai-studio-blueprint-kit 0.1.0

Reusable Python utilities for HP AI Studio and notebook-centric ML workflows.

AI-Studio-Blueprint-Kit

AI-Studio-Blueprint-Kit is a public Python package and source repository for reusable utilities that support AI Studio blueprint development.

This repository is intentionally structured for growth. It starts with the memory_guard module and can later include additional modules under the same top-level package.

Current module

ai_studio_blueprint_kit.memory_guard

A notebook-oriented RAM and VRAM resource guard designed for local AI and ML workflows.

Main capabilities:

• checks Linux or WSL RAM availability from /proc/meminfo
• attempts NVIDIA VRAM detection through nvidia-smi, torch, or pynvml
• renders rich notebook warnings and pass/fail status with IPython HTML and Markdown when available
• shuts down the active Jupyter kernel when total hardware is insufficient

Installation

Base install:

pip install ai-studio-blueprint-kit

With notebook UI dependencies:

pip install "ai-studio-blueprint-kit[notebook]"

With GPU helper dependency:

pip install "ai-studio-blueprint-kit[gpu]"

With Torch fallback support:

pip install "ai-studio-blueprint-kit[torch]"

Usage

from ai_studio_blueprint_kit.memory_guard import run_memory_check_notebook

run_memory_check_notebook(
    min_total_ram_gb=16.0,
    min_total_vram_gb=8.0,
)

Lower-level usage:

from ai_studio_blueprint_kit.memory_guard import check_ram, check_vram

ram = check_ram()
vram = check_vram()

print(ram)
print(vram)

Repository layout

AI-Studio-Blueprint-Kit/
├── .github/
│   └── workflows/
├── src/
│   └── ai_studio_blueprint_kit/
│       ├── __init__.py
│       └── memory_guard/
│           ├── __init__.py
│           └── core.py
├── tests/
├── LICENSE
├── MANIFEST.in
├── README.md
└── pyproject.toml

Adding future modules

New modules should be added under:

src/ai_studio_blueprint_kit/

Examples:

src/ai_studio_blueprint_kit/data_checks/
src/ai_studio_blueprint_kit/model_utils/
src/ai_studio_blueprint_kit/notebook_ui/

This keeps the public import surface consistent:

from ai_studio_blueprint_kit.memory_guard import run_memory_check_notebook

Development

Create a virtual environment and install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Build package distributions:

python -m build

Validate distributions:

python -m twine check dist/*

Publishing

Recommended release flow:

1. publish the repository publicly on GitHub
2. test on TestPyPI first
3. configure PyPI Trusted Publishing for GitHub Actions
4. push a version tag such as v0.1.0

Notes

• The current RAM check is Linux and WSL oriented because it reads /proc/meminfo.
• VRAM detection is primarily intended for NVIDIA environments.
• Notebook rendering is optional and is only used when IPython is available.
• Kernel shutdown on hard failure is intentional in the current memory_guard behavior.

License

MIT