Principle:Axolotl ai cloud Axolotl Configuration Validation
| Knowledge Sources | |
|---|---|
| Domains | Configuration, Validation |
| Last Updated | 2026-02-06 23:00 GMT |
Overview
A validation pattern that ensures training configurations are complete, consistent, and hardware-compatible before expensive operations begin.
Description
Configuration Validation is the process of verifying that a training configuration meets all requirements for successful execution. This goes beyond simple type checking to include semantic validation (e.g., QLoRA requires a quantization-capable model), cross-field consistency (e.g., FSDP and DeepSpeed cannot be used simultaneously), and capability checking (e.g., flash attention requires compatible GPU architecture).
The key problem it solves is fail-fast behavior: catching configuration errors before spending minutes loading a 70B model or hours downloading datasets. Axolotl uses Pydantic models to define a strict schema (AxolotlInputConfig) and then applies additional runtime validators that check hardware capabilities and cross-field constraints.
Usage
Apply this principle immediately after loading raw configuration and before any resource-intensive operations. This is critical for:
- Preventing silent misconfigurations that lead to training failures hours into a run
- Enforcing mutual exclusivity constraints (e.g., adapter types, precision settings)
- Auto-detecting hardware capabilities and adjusting config defaults accordingly
Theoretical Basis
Configuration Validation applies the Design by Contract principle: preconditions must be met before a system proceeds. It uses a layered validation approach:
- Schema Validation: Pydantic type checking and field constraints
- Cross-Field Validation: Business rules checking consistency between fields
- Capability Validation: Hardware/software environment compatibility checks
Pseudo-code:
# Abstract validation algorithm
schema_result = pydantic_validate(raw_config, AxolotlInputConfig)
capability_info = detect_hardware_capabilities()
cross_field_errors = check_consistency_rules(schema_result, capability_info)
if cross_field_errors:
raise ValidationError(cross_field_errors)
return apply_capability_defaults(schema_result, capability_info)