Experiment Design
Experiment design is the planning of controlled training runs — varying one factor at a time (architecture, hyperparameter, data) with fixed seeds and baselines to draw causal conclusions.
What is Experiment Design?
Experiment design is the planning of controlled training runs — varying one factor at a time (architecture, hyperparameter, data) with fixed seeds and baselines to draw causal conclusions.
Experiment design is the planning of controlled training runs — varying one factor at a time (architecture, hyperparameter, data) with fixed seeds and baselines to draw causal conclusions.
Where is it used?
Ablation studies in every major paper (Llama, GPT, LoRA) isolate the contribution of each component; `wandb` and `mlflow` track configs, seeds, and metrics for reproducibility.
How to build it
Define a base config, use `hydra` to sweep one variable (e.g., `num_heads: [4, 8, 16]`), launch with `wandb sweep`, and compare validation loss across runs in a single dashboard.