kandc 0.0.28

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

Keys & Caches

The open-source profiler that gives you a unified view of your entire stack - from PyTorch down to the GPU.
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What is Keys & Caches?

Keys & Caches is a Python library that provides experiment tracking and workflow management for machine learning projects. With a simple API, you can:

• 📊 Track experiments — Automatically log metrics and hyperparameters
• 🌐 Cloud dashboard — Real-time visualization of your experiments
• 🏷️ Organize projects — Group related experiments together
• 🎯 Zero-overhead when disabled — Tracking only activates when initialized

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Installation

pip install kandc

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Quick Start

import kandc
import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU(),
            nn.Linear(128, 10),
        )

    def forward(self, x):
        return self.layers(x)

def main():
    # Initialize experiment tracking
    kandc.init(
        project="my-project",
        name="experiment-1",
        config={"batch_size": 32, "learning_rate": 0.01}
    )

    # Your training/inference code
    model = SimpleNet()
    data = torch.randn(32, 784)
    output = model(data)
    loss = output.mean()

    # Log metrics
    kandc.log({"loss": loss.item(), "accuracy": 0.85})

    # Finish the run
    kandc.finish()

if __name__ == "__main__":
    main()

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Key Features

🎯 Simple Initialization

kandc.init(
    project="my-ml-project",
    name="experiment-1",
    config={
        "learning_rate": 0.001,
        "batch_size": 32,
        "model": "resnet18",
    }
)

📊 Metrics Logging

# Log single or multiple metrics
kandc.log({"loss": 0.25, "accuracy": 0.92})

# Log with step numbers for training loops
for epoch in range(100):
    loss = train_epoch()
    kandc.log({"epoch_loss": loss}, step=epoch)

🌐 Multiple Modes

# Online mode (default) - full cloud experience
kandc.init(project="my-project")

# Offline mode - local development
kandc.init(project="my-project", mode="offline")

# Disabled mode - zero overhead
kandc.init(project="my-project", mode="disabled")

🔮 Inference Tracking

import kandc
import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU(),
            nn.Linear(128, 10)
        )
    
    def forward(self, x):
        return self.layers(x)

def run_inference():
    # Initialize inference tracking
    kandc.init(
        project="inference-demo",
        name="simple-inference",
        config={"batch_size": 32}
    )
    
    # Create model and wrap with profiler
    model = SimpleNet()
    model = kandc.capture_model_instance(model, model_name="SimpleNet")
    model.eval()
    
    # Run inference
    data = torch.randn(32, 784)
    with torch.no_grad():
        predictions = model(data)
        confidence = torch.softmax(predictions, dim=1).max(dim=1)[0].mean()
    
    # Log results
    kandc.log({
        "avg_confidence": confidence.item(),
        "batch_size": 32
    })
    
    kandc.finish()

if __name__ == "__main__":
    run_inference()

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Examples

See the examples/ directory for detailed examples:

• complete_example.py - Simple getting started example
• offline_example.py - Offline mode usage

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API Reference

Core Functions

• kandc.init() - Initialize a new run with configuration
• kandc.finish() - Finish the current run and save all data
• kandc.log() - Log metrics to the current run
• kandc.get_current_run() - Get the active run object
• kandc.is_initialized() - Check if kandc is initialized

Run Modes

• "online" - Default mode, full cloud functionality
• "offline" - Save everything locally, no server sync
• "disabled" - No-op mode, zero overhead

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🎓 Students and Educators

Email us at [e-mail] for support and collaboration opportunities!

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📦 Publishing to PyPI

🚀 Publish Stable Release

1. Bump the version in pyproject.toml (e.g., 0.0.15).

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

1. Bump the dev version in pyproject.dev.toml (e.g., 0.0.15.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