kandc 0.0.6

Keys & Caches CLI - Accelerate your Python functions with cloud GPUs

Keys & Caches

Welcome to Keys & Caches — the fastest way to run PyTorch models on cloud GPUs with automatic profiling and performance insights.

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📚 Documentation Overview

This documentation will help you get started with Keys & Caches and make the most of its powerful features for GPU-accelerated machine learning.

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What is Keys & Caches?

Keys & Caches is a command-line tool that makes it effortless to run PyTorch models on high-performance cloud GPUs. With just one command, you can:

• 🚀 Submit jobs to cloud GPUs — Access A100, H100, and L4 GPUs instantly
• 📊 Get automatic profiling — Detailed performance traces for every model forward pass
• 🔍 Debug performance bottlenecks — Chrome trace format for visual analysis
• ⚡ Stream real-time logs — Watch your training progress live
• 💰 Pay only for what you use — No idle time charges

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Key Features

🎯 One-Command Deployment

cd examples
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run any PyTorch script on cloud GPUs
kandc run python basic_models/simple_cnn.py

# Or capture locally with profiling
kandc capture python basic_models/simple_cnn.py

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📈 Automatic Model Profiling

from kandc import capture_model_class

@capture_model_class(model_name="MyModel")
class MyModel(nn.Module):
    # Your model automatically gets profiled!

🎓 Students & Educators

• Email us at [e-mail] for free credits!
• Access high-end GPUs for coursework and research
• Learn about model optimization with built-in profiling tools
• Focus on ML concepts, not DevOps complexity

🚀 Startups & Small Teams

• Get enterprise-grade GPU access without upfront costs
• Scale compute resources based on actual needs
• Streamline ML workflows from development to production

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Ready to Get Started?

👉 Jump to the Getting Started Guide to install Keys & Caches and run your first GPU job in under 5 minutes!

📦 Publishing to PyPI

🚀 Publish Stable Release (kandc)

1. Bump the version in pyproject.toml (e.g., 0.0.4).

2. Run the following commands:

   rm -rf dist build *.egg-info
   python -m pip install --upgrade build twine
   python -m build
   export TWINE_USERNAME=__token__
   twine upload dist/*
   

🧪 Publish Dev Release (kandc-dev)

1. Bump the dev version in pyproject.dev.toml (e.g., 0.0.4.dev1).

2. Run the following commands:

   rm -rf dist build *.egg-info
   cp pyproject.dev.toml pyproject.toml
   python -m pip install --upgrade build twine
   python -m build
   export TWINE_USERNAME=__token__
   twine upload dist/*
   git checkout -- pyproject.toml   # Restore the original pyproject.toml after publishing (undo the cp above)