Kolosal AutoML

🚀 Pure Rust high-performance AutoML framework with web UI and CLI.

Quick Start

Using the CLI

# Build the CLI
cargo build --release

# Train a model
./target/release/kolosal train --data data.csv --target label --model random_forest

# Benchmark multiple models
./target/release/kolosal benchmark --data data.csv --target label

# Show data info
./target/release/kolosal info --data data.csv

Using the Web Server

# Start the server
./target/release/kolosal-server --port 8080

# Open http://localhost:8080 in your browser

The web UI provides:

• 📊 Data upload and sample datasets (iris, diabetes, boston, wine)
• ⚙️ Configuration for task type, model, and preprocessing
• 🚀 One-click model training with progress tracking
• 📡 Real-time system monitoring

API Examples

# Load sample dataset
curl http://localhost:8080/api/data/sample/iris

# Start training
curl -X POST http://localhost:8080/api/train \
  -H "Content-Type: application/json" \
  -d '{"target_column":"species","task_type":"classification","model_type":"random_forest"}'

# Check training status
curl http://localhost:8080/api/train/status/{job_id}

# Get system status
curl http://localhost:8080/api/system/status

Features

Feature	Description	Status
Web Server	Axum-based REST API	✅
Web UI	htmx + Alpine.js frontend	✅
CLI	Full-featured command line	✅
Data Preprocessing	Scalers, Encoders, Imputers	✅
Training Engine	Cross-validation, Metrics	✅
Models	Linear, Logistic, Trees, Random Forest	✅
HyperOptX	TPE, Random, Grid samplers	✅
Pruners	Median, Percentile, Hyperband	✅
SIMD Operations	Vectorized math operations	✅

Performance

Benchmarked against a Python/scikit-learn baseline on identical hardware (Railway deployment). Rust wins 18/18 head-to-head comparisons.

Metric	Rust	Python	Speedup
Page Load (TTFB)	29 ms	512 ms	17x
DOM Content Loaded	46 ms	3,676 ms	80x
Health Check API	135 ms	1,428 ms	11x
Iris Dataset Load	113 ms	26,808 ms	237x
Model Training (Iris)	446 ms	1,047 ms	2.3x
Single Prediction	43 ms	138 ms	3.2x
Tab Switch (UI)	101 ms	4,101 ms	41x
10 Concurrent Requests	29 ms avg	16,531 ms avg	564x

Rust API aggregate (57 endpoints): avg 60 ms, median 40 ms, 68% under 50 ms.

Full end-to-end pipeline (load + preprocess + train + predict + explain): 204 ms.

Operation	Typical Latency
StandardScaler (1M×10)	~45 ms
Random Forest fit (10K rows)	~1.8 s
Random Forest fit (50K rows)	~1.1 s
Batch Inference (10K)	~4 ms
Full AutoML Pipeline	~595 ms
Server startup	<1 s

Project Structure

kolosal_automl/
├── kolosal-core/           # Core ML library
├── kolosal-server/         # Axum web server
├── kolosal-cli/            # CLI application
├── kolosal-web/            # Web frontend assets
├── legacy/                 # Legacy code (Python bindings, etc.)
├── benches/                # Benchmarks
├── examples/               # Example code
├── docs/                   # Documentation
└── tests/                  # Integration tests

Building from Source

Prerequisites

• Rust 1.75+ (rustup)

Build

# Build all crates
cargo build --release

# Run tests
cargo test --workspace

# Run the server
cargo run --package kolosal-server

# Run the CLI
cargo run --package kolosal-cli -- --help

Build Binaries

# Optimized release build
cargo build --release

# Binaries will be in:
# - target/release/kolosal-server
# - target/release/kolosal

API Reference

REST Endpoints

Method	Endpoint	Description
POST	/api/data/upload	Upload CSV/JSON/Parquet
GET	/api/data/sample/:name	Load sample dataset
GET	/api/data/preview	Preview loaded data
GET	/api/data/info	Get dataset info
POST	/api/train	Start training job
GET	/api/train/status/:id	Get training status
POST	/api/predict	Make predictions
GET	/api/system/status	System metrics

CLI Reference

kolosal <COMMAND>

Commands:
  train       Train a model on data
  predict     Make predictions using a trained model
  preprocess  Preprocess data
  benchmark   Benchmark multiple models
  info        Show data information
  serve       Start the web server
  help        Print help

Documentation

• Changelog

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE for details.