Engineered with formal verification principles, strict type validation, and native production readiness.
Full DVC integration for reproducible ML pipelines with semantic versioning of datasets and models.
Asynchronous task processing via Redis queue with multi-worker support and automatic failover.
Gradio-powered web interface providing live queue status, job tracking, and comprehensive metadata visualization.
End-to-end automation including DVC add, push, Git untracking, and CSV inventory generation.
- Pylint score > 9.5 across all modules
MinIO S3-compatible backend for high-performance object storage.
Designed to eliminate low-level plumbing and cognitive friction, allowing engineers to build reliable solutions in minutes.
from src.scripts.api.app.worker_queue import put_in_queue, get_Status_item
# Add item to processing queue
result = put_in_queue("/app/projects/dataset/raw")
task_id = result["id"]
# Check status
status = get_Status_item(task_id)
print(status["metadata"]["status"]) # pending → processing → completed
wDVC is an integral component within the Wisrovi Binary Universe, engineered by William Steve Rodriguez Villamizar to unify high-throughput distributed systems, formal verification, and autonomous agent orchestration.