Principle:Axolotl ai cloud Axolotl Configuration Loading
| Knowledge Sources | |
|---|---|
| Domains | Configuration, Training_Pipeline |
| Last Updated | 2026-02-06 23:00 GMT |
Overview
A design pattern for declarative training pipeline configuration that loads, validates, and normalizes YAML-based specifications into executable training parameters.
Description
Configuration Loading is the foundational step of any ML training pipeline. Rather than hardcoding hyperparameters and data paths in code, modern frameworks use declarative configuration files (typically YAML) that specify the entire training setup: model selection, dataset paths, hyperparameters, distributed training settings, and output destinations.
The key challenge this solves is reproducibility and portability: a single configuration file fully describes a training run, enabling exact reproduction across different machines, users, and time periods. It also enables composability, where base configurations can be extended or overridden via CLI arguments.
In Axolotl, configuration loading handles: YAML parsing with environment variable resolution, remote config fetching via URLs, directory scanning for config discovery, CLI argument merging, and Pydantic-based schema validation.
Usage
Use this principle at the very start of any training pipeline when you need to:
- Load training specifications from a YAML file
- Support both local files and remote URLs as config sources
- Merge command-line overrides into a base configuration
- Validate configuration completeness before expensive operations (model loading, data prep)
Theoretical Basis
Declarative configuration follows the separation of concerns principle: code defines how to train, configuration defines what to train. This pattern is formalized in the Configuration-as-Code paradigm.
Key properties of good configuration loading:
- Idempotency: Loading the same config file always produces the same result
- Composability: Configs can be layered (base + overrides)
- Validation: Invalid configurations are rejected early with clear error messages
- Normalization: Shorthand values are expanded to canonical form
Pseudo-code:
# Abstract configuration loading algorithm
raw_config = parse_yaml(config_path)
merged_config = merge(raw_config, cli_overrides)
validated_config = validate_schema(merged_config)
normalized_config = apply_defaults(validated_config)
return normalized_config