{
  "context": "This criteria evaluates how well the engineer uses Modal's resource configuration capabilities to set up compute resources (CPU, memory, GPU) for serverless functions. The focus is on proper use of Modal's API for configuring function resources.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "App initialization",
      "description": "Creates a modal.App instance to group the serverless functions",
      "max_score": 10
    },
    {
      "name": "CPU configuration",
      "description": "Uses the 'cpu' parameter in @app.function() decorator to configure CPU resources for all three functions with the correct values (2 cores for preprocessing, 4 cores for training, 0.5 cores for inference)",
      "max_score": 25
    },
    {
      "name": "Memory configuration",
      "description": "Uses the 'memory' parameter in @app.function() decorator to configure memory resources for all three functions with the correct values (2048 MiB for preprocessing, 16384 MiB for training, 1024 MiB for inference)",
      "max_score": 25
    },
    {
      "name": "GPU configuration",
      "description": "Uses the 'gpu' parameter in @app.function() decorator to configure GPU resources for the training function (e.g., gpu='any', gpu='A10G', or gpu=modal.gpu.A100())",
      "max_score": 25
    },
    {
      "name": "Function definitions",
      "description": "Defines the three serverless functions (preprocess_data, train_model, run_inference) using the @app.function() decorator",
      "max_score": 10
    },
    {
      "name": "Pipeline orchestration",
      "description": "Creates a local_entrypoint using @app.local_entrypoint() decorator or implements pipeline logic that calls the three functions in sequence using .remote() method",
      "max_score": 5
    }
  ]
}