{
  // Zip Parameter Mode Example
  // Demonstrates using parameter_mode: "zip" for paired parameter expansion
  //
  // When using Cartesian product (default): 3 datasets x 3 models = 9 jobs
  // When using zip mode: 3 pairs = 3 jobs (dataset[i] paired with model[i])
  //
  // This is useful when you have pre-determined parameter combinations rather than
  // wanting to test all possible combinations.

  "name": "zip_parameter_example",
  "description": "Example workflow demonstrating zip parameter mode for paired parameters",

  "jobs": [
    // Training jobs using zip mode - each dataset is paired with a specific model
    // Instead of 3x3=9 combinations, we get exactly 3 jobs:
    //   - cifar10 with resnet
    //   - mnist with cnn
    //   - imagenet with transformer
    {
      "name": "train_{dataset}_{model}",
      "command": "python train.py --dataset={dataset} --model={model}",
      "output_files": ["model_{dataset}_{model}"],
      "parameters": {
        "dataset": "['cifar10', 'mnist', 'imagenet']",
        "model": "['resnet', 'cnn', 'transformer']"
      },
      "parameter_mode": "zip"
    },
    // Evaluation jobs also use zip mode to match the training jobs
    {
      "name": "evaluate_{dataset}_{model}",
      "command": "python evaluate.py --model=/models/{dataset}_{model}.pt",
      "depends_on": ["train_{dataset}_{model}"],
      "input_files": ["model_{dataset}_{model}"],
      "parameters": {
        "dataset": "['cifar10', 'mnist', 'imagenet']",
        "model": "['resnet', 'cnn', 'transformer']"
      },
      "parameter_mode": "zip"
    }
  ],

  // File specifications also support zip mode
  "files": [
    {
      "name": "model_{dataset}_{model}",
      "path": "/models/{dataset}_{model}.pt",
      "parameters": {
        "dataset": "['cifar10', 'mnist', 'imagenet']",
        "model": "['resnet', 'cnn', 'transformer']"
      },
      "parameter_mode": "zip"
    }
  ]
}