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Template Generation System

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Available templates

cirron init my-project --template pytorch

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How generation works

1. Selects the template you specified.
2. Calls createCommonMLFiles() to write the shared scaffolding (Dockerfile, requirements.txt, README, cirron.yaml, tests, .gitignore, .env.example). See Common files.
3. Calls the framework-specific generator (e.g. createPyTorchFiles()) to write src/model.py, src/data_loader.py, src/inference.py, and, for *-train templates, src/train.py.
4. Resolves the model architecture and data loader bodies via two helpers:
   • getModelCode(framework, modelType): returns the source for src/model.py.
   • getDataLoaderCode(framework, modelType): returns the source for src/data_loader.py.
5. Writes everything to disk and prints next steps.

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File layout in the CLI source

src/commands/files/
├── common.ts              # createCommonMLFiles()
├── pytorch.ts             # createPyTorchFiles, createPyTorchTrainingFiles
├── tensorflow.ts          # createTensorFlowFiles, createTensorFlowTrainingFiles
├── sklearn.ts             # createSklearnFiles, createSklearnPipelineFiles
├── data.ts                # getDataLoaderCode()
└── model.ts               # getModelCode()

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Model types

• classification: Cross-entropy loss, softmax output. PyTorch/TensorFlow generate a small MLP or CNN; sklearn uses RandomForestClassifier.
• regression: MSE loss, linear output. PyTorch/TensorFlow generate an MLP; sklearn uses RandomForestRegressor.
• computer_vision: Conv-based architecture (PyTorch nn.Conv2d stack, TensorFlow Conv2D stack).
• custom: Skipped; you write src/model.py yourself.

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Extending the system

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Adding a new model type

1. Add a branch in getModelCode():

export function getPyTorchModelCode(modelType: string): string {
  if (modelType === 'nlp') {
    return generateNLPModel();
  }
  // ... existing branches
}

2. Add the matching branch in getDataLoaderCode().
3. Add the model type to the interactive prompt list in src/commands/init.ts.

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Adding a new framework

1. Create src/commands/files/<framework>.ts exporting a create<Framework>Files() function.
2. Add framework-specific branches in getModelCode() and getDataLoaderCode().
3. Register the template name(s) in the init command’s template registry.
4. Add a docs page under apps/docs/cli/templates/.

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Generation entry point

await createCommonMLFiles(projectPath, projectName, options);

if (template === 'pytorch-train') {
  await createPyTorchTrainingFiles(projectPath, projectName, options);
} else if (template === 'pytorch') {
  await createPyTorchFiles(projectPath, projectName, options);
}
// ... etc.

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Best practices for authoring templates

• Match the runtime contract: generated src/inference.py must expose a ModelInference class with predict(input). The Cirron runtime imports it by name.
• Wire the SDK: generated training and inference code should import cirron as ci and call ci.profile() once at module top so traces flow to cirron traces. Use ci.scope("name") for explicit regions and ci.wrap(estimator) for scikit-learn models.
• Provide sensible defaults: model hyperparameters, batch size, learning rate should work on the bundled sample data without tweaking.
• Fail soft on missing data: fall back to data/sample/sample_data.csv if the user’s data path is empty.
• Pin loose dependency versions: >=X.Y.0 for the framework, exact for security-sensitive libs.

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Next Steps