artifacta 0.1.1

Universal experiment and artifact tracking — gain insights and optimize models with confidence

Artifacta

Universal experiment and artifact tracking — gain insights and optimize models with confidence.

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✨ Key Features

• 🌐 Domain-agnostic - Track any experiment comparing parameters, data, and outcomes
• 📊 Automatic visualization - Plots discovered from logged data structure
• 🔗 Artifact tracking - Track datasets, models, code, and results with full provenance
• 🔄 Multi-run comparison - Overlay time series and curves for easy comparison
• 🎯 Hyperparameter analysis - Automatically detect and analyze parameter impact on outcomes
• 💬 AI assistant - Chat interface for experiment insights (OpenAI, Anthropic, local LLMs)

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🎨 Visual Overview

Automatic Plot Discovery

Artifacta automatically generates visualizations based on your data shape and metadata. No manual plot configuration needed.

Artifact Management

Browse and preview datasets, models, code, images, videos, and documents with built-in file viewers.

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🚀 Quick Start

Installation

Standard Installation

Prerequisites: Python 3.9+

# Clone the repository
git clone https://github.com/walkerbdev/artifacta.git
cd artifacta

# Install Python package
pip install -e .

Note: The UI is pre-built and bundled. No Node.js required.

Development Installation

Prerequisites: Python 3.9+, Node.js 16+

# Clone the repository
git clone https://github.com/walkerbdev/artifacta.git
cd artifacta

# Build UI from source
npm install && npm run build

# Install Python package
pip install -e .

Start Tracking Server

artifacta ui

The web UI will be available at http://localhost:8000 (default).

You can customize host and port:

artifacta ui --host 0.0.0.0 --port 8000

Development Mode: Run with hot-reload for UI development:

artifacta ui --dev

Log Your First Experiment

import artifacta as ds

# Initialize a run
run = ds.init(
    project="my-project",
    name="experiment-1",
    config={"learning_rate": 0.001, "batch_size": 32}
)

# Log metrics during training
for epoch in range(10):
    train_loss = train_model()  # Your training code

    ds.log("metrics", ds.Series(
        index="epoch",
        fields={
            "train_loss": [train_loss],
            "epoch": [epoch]
        }
    ))

# Log artifacts (models, plots, etc.)
run.log_artifact("model.pt", "path/to/model.pt")

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📚 Documentation

Full documentation available at: User Guide

Build and serve docs locally:

pip install artifacta[dev]
cd docs && make html
python -m http.server 8001 --directory _build/html

Then navigate to http://localhost:8001

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📊 Core Primitives

Artifacta provides rich primitives for structured logging:

• Series - Time series data (loss curves, accuracy over time)
• Curve - ROC curves, PR curves with AUC metrics
• Distribution - Histograms and distributions
• Matrix - Confusion matrices and heatmaps
• Scatter - 2D scatter plots (embeddings, parameter spaces)
• BarChart - Categorical comparisons
• Table - Structured tabular data

All primitives are automatically visualized in the Plots tab.

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💻 Web UI Features

• Plots - Auto-generated visualizations with multi-run overlay
• Sweeps - Hyperparameter analysis with parallel coordinates
• Artifacts - File browser with preview for code, images, video, audio
• Tables - Metric aggregation and comparison tables
• Lineage - Visual artifact provenance graphs
• Notebooks - Rich text lab notebook with LaTeX support
• Chat - AI assistant for experiment analysis

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💡 Examples

See examples/ for runnable examples:

• PyTorch MNIST - Image classification with autolog
• TensorFlow Regression - Time series forecasting
• A/B Testing - Conversion rate analysis with statistical tests

Additional domain examples available in tests/domains/:

• Climate modeling, Computer vision, Finance, Genomics, Physics, Robotics, and more

Run examples:

source venv/bin/activate
python examples/ab_testing.py

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🧪 Running Tests

Start the tracking server in one terminal:

source venv/bin/activate
artifacta ui

Run tests in another terminal:

source venv/bin/activate
pytest tests/