kandc-dev 0.0.4.dev0

Keys & Caches CLI (DEV)

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

# Run any PyTorch script on cloud GPUs
kandc run python infer.py --model-size large --epochs 100

# Or capture locally with profiling
kandc capture python infer.py --model-size large --epochs 100

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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!

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🔧 Simple Command Formats

# Cloud GPU execution:
kandc run python script.py --script-args                                  # Interactive
kandc run --app-name job --gpu H100:4 -- python script.py --script-args  # Separator

# Local execution with capture:
kandc capture python script.py --script-args                              # Interactive
kandc capture --app-name test -- python script.py --script-args           # Direct

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Who Should Use Keys & Caches?

🧑‍🔬 Machine Learning Researchers

• Quickly test models on powerful GPUs without infrastructure setup
• Get detailed performance profiles to optimize model architectures
• Scale experiments from 1 to 8 GPUs seamlessly

🏢 ML Engineers & Data Scientists

• Accelerate training jobs without managing cloud infrastructure
• Debug performance issues with automatic profiling
• Iterate faster with real-time progress monitoring

🎓 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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Why Choose Keys & Caches?

• 🚀 Instant Access — No account setup, no credit cards, no waiting. Install and run immediately.
• 💡 Built for ML — Purpose-built for PyTorch with automatic model profiling and optimization insights.
• 🎯 Developer-Friendly — Simple CLI that works with your existing code. No code changes required.
• 📊 Performance-First — Every job includes detailed performance traces to help you optimize your models.
• 💰 Cost-Effective — Pay only for actual GPU time. No idle charges, no minimum commitments.

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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!

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Quick Example

Here’s how easy it is to run a PyTorch model on cloud GPUs:

# your_model.py
import torch
import torch.nn as nn
from kandc import capture_model_class

@capture_model_class(model_name="SimpleModel")
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(784, 10)
    
    def forward(self, x):
        return self.linear(x)

# Train your model...
model = MyModel()
x = torch.randn(32, 784)
output = model(x)  # This gets automatically profiled!

# Run on cloud GPUs with one command
kandc run python your_model.py

# Or run locally with profiling and upload results
kandc capture python your_model.py

That’s it! Your model runs on high-performance GPUs with automatic profiling. 🎉

📦 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/*
   pip install -U kandc
   

🧪 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)
   pip install -U kandc-dev
   # If you gave dev its own CLI:
   kandc-dev --help
   # Otherwise, it’s the same `kandc` entry point.