optixlog 0.1.0

Client SDK for logging photonic simulations to OptixLog with MPI support

OptixLog SDK

Experiment tracking for photonic simulations with automatic MPI support.

🚀 Quick Start

pip install http://optixlog.com/optixlog-0.1.0-py3-none-any.whl

import optixlog

# Set your API key
export OPTIX_API_KEY="proj_your_key_here"

# Start logging
with optixlog.run("my_experiment", config={"lr": 0.001}) as client:
    # Log metrics
    client.log(step=0, loss=0.5, accuracy=0.9)
    
    # Log plots (one line!)
    import matplotlib.pyplot as plt
    plt.plot([1,2,3], [1,4,9])
    client.log_matplotlib("my_plot", plt.gcf())

That's it! View your results at optixlog.com

📚 Documentation

→ Complete API Reference - Every function with examples

✨ Key Features

• Zero Boilerplate: Log matplotlib plots in one line
• Context Managers: Clean with statement support
• Input Validation: Catches NaN/Inf and invalid data
• Rich Output: Colored console feedback
• MPI Support: Automatic detection and rank 0 logging
• Batch Operations: Fast parallel uploads
• Return Values: Get URLs and status for everything
• Query API: Programmatic access to runs and artifacts

🎯 Common Use Cases

Log Training Metrics

with optixlog.run("training") as client:
    for epoch in range(100):
        client.log(step=epoch, loss=0.5, accuracy=0.9)

Log Matplotlib Plots

# Old way (15+ lines of boilerplate)
plt.savefig("plot.png")
with open("plot.png", "rb") as f:
    img = PIL.Image.open(f)
    client.log_image("plot", img)
os.remove("plot.png")

# New way (one line!)
client.log_matplotlib("plot", plt.gcf())

Log Field Data

import numpy as np
field = np.random.rand(100, 100)
client.log_array_as_image("field", field, cmap='hot')

Log Multiple Metrics

metrics = [{"step": i, "loss": losses[i]} for i in range(1000)]
result = client.log_batch(metrics)  # Fast batch upload!

Query Previous Runs

runs = optixlog.list_runs(client, project_name="MyProject")
for run in runs:
    artifacts = optixlog.get_artifacts(client, run.run_id)
    print(f"{run.name}: {len(artifacts)} artifacts")

🔧 Installation

From PyPI

pip install http://optixlog.com/optixlog-0.1.0-py3-none-any.whl

From Source

git clone https://github.com/yourusername/optixlog-sdk.git
cd optixlog-sdk
pip install -e .

🔑 Setup

1. Get your API key from optixlog.com
2. Set environment variable:

export OPTIX_API_KEY="proj_your_key_here"

3. Optionally set default project:

export OPTIX_PROJECT="MyProject"

📖 Full Documentation

→ API_REFERENCE.md - Complete reference with:

• All functions and parameters
• Return types and error handling
• Real-world examples
• Best practices
• MPI support details

🎓 Examples

See DEMO.py for a comprehensive demonstration of all features.

🛠️ Requirements

• Python 3.8+
• requests
• numpy
• matplotlib
• pillow
• rich

🚀 What's New in v0.1.0

• ✨ One-line plot logging: log_matplotlib()
• ✨ Context managers: with optixlog.run()
• ✨ Helper functions: log_plot(), log_array_as_image()
• ✨ Input validation: Catches NaN/Inf automatically
• ✨ Return values: Every method returns status + URL
• ✨ Batch operations: log_batch(), log_images_batch()
• ✨ Query API: List and download runs programmatically
• ✨ Rich output: Beautiful colored console feedback

See CHANGELOG.md for full details.

🤝 CLI Integration

The OptixLog CLI works seamlessly with this SDK:

npm install -g optixlog-cli
optixlog init  # Create .optixlog.json config
optixlog add-logging script.py  # Auto-instrument code
optixlog runs list  # Query runs from terminal

📊 Dashboard

View all your experiments at optixlog.com

📝 License

MIT License - see LICENSE file for details

🐛 Support

• Documentation: API_REFERENCE.md
• Demo: DEMO.py
• Issues: Report bugs or request features

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Version: 0.1.0
Made with ⚡ for photonic simulation tracking