🌟 STARK Language Documentation
AI-Native Programming Language for Production ML Deployment
Welcome to STARK
STARK is not just a programming language. It is a solution to the AI deployment crisis.
In a world where Python dominates AI research but fails in production, where inference costs spiral out of control, and where edge deployment means sacrificing model capabilities, STARK rises as the bridge between AI innovation and real-world performance.
Why STARK?
🤖 AI-Native Design
Tensor operations and ML workflows as primary abstractions, not library afterthoughts.
⚡ Production Performance
2-10x faster inference than Python with memory-safe execution.
🔗 Python Interoperability
Seamless loading of existing PyTorch/TensorFlow models.
🛡️ Memory Safety
Prevent common memory errors through ownership and borrowing.
Quick Example
// Load pre-trained model with automatic shape inference
let model = load_pytorch_model("sentiment_classifier.pt")
// Create streaming data pipeline
let text_stream: Dataset<String> = load_text_stream("reviews.jsonl")
let processed = text_stream
.map(|text| tokenize_and_embed(text)) // String -> Tensor<Float32>[512]
.batch(32) // Batch for efficiency
.prefetch(2) // Async prefetching
// Run inference with automatic batching
for batch in processed {
let predictions = model.predict(batch) // Tensor<Float32>[32, 3]
let labels = argmax(predictions, axis=1) // Tensor<Int32>[32]
// Process results
for (text, label) in zip(batch.unbatch(), labels.unbatch()) {
handle_classification(text, label)
}
}