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name: hyperparam-sweep description: Systematically search hyperparameter space. Use when tuning learning rate, batch size, or other hyperparameters.

Hyperparameter Sweep

Systematically search for optimal hyperparameters.

Step 1: Define Search Space

Common spaces: learning_rate (log_uniform 1e-5 to 1e-2), batch_size (categorical), weight_decay (log_uniform), warmup_ratio, dropout

Step 2: Choose Search Strategy

• Grid search: exhaustive, for 2-3 params with few values
• Random search: recommended default, 20-50 runs usually sufficient
• Bayesian: for expensive runs with smooth objective

Step 3: Set Budget

Max runs, max total time, early stopping configuration

Step 4: Execute Sweep

Track progress, report best so far, apply early stopping to bad runs

Step 5: Analyze Results

• Best configuration with metrics
• Parameter importance visualization
• Sensitivity analysis (high/medium/low for each param)
• Recommendations for next steps