fail-fast-ml-engineering

What problem does it solve?

Design teams often ship ML pipelines with silent fallbacks, missing preflight checks, and ambiguous errors, leading to wasted compute, nondeterministic behavior, and hard-to-diagnose failures. Fail-fast ML engineering provides strong guardrails to catch issues early by enforcing explicit errors, predictable config, and upfront validation.

Core Features & Use Cases

• No Silent Fallbacks: Critical paths must raise explicit errors instead of silently defaulting to suboptimal behavior, reducing silent failures during training and inference.
• Config as Single Source of Truth: Treat config files as the authoritative source; avoid relying on checkpoint metadata or environment guesses.
• Preflight Pattern: Validate data existence, weights, and hardware availability before launching expensive computation.
• Guard Clauses & Explicit Errors: Use early-return guards with descriptive messages to surface root causes quickly.
• Assertion-Heavy Data Loading: Enforce data schema and tensor shapes at load time to prevent silent shape mismatches.
• Framework Compatibility Guards: Verify accelerator and dataloader compatibility before distributed or accelerated runs.

Quick Start

Run fail-fast-ml-engineering to integrate preflight validations and explicit error handling into your ML workflow.