synapse-specialized-actions

name

synapse-specialized-actions

description

Explains specialized Synapse action classes for specific workflows. Use when the user mentions "BaseTrainAction", "BaseExportAction", "BaseUploadAction", "BaseInferenceAction", "BaseDeploymentAction", "AddTaskDataAction", "train action", "export action", "upload action", "inference action", "deployment action", "pre-annotation", "add_task_data", "autolog", "get_dataset", "create_model", or needs workflow-specific action development help.

Specialized Action Classes

Synapse SDK provides specialized base classes for common ML workflows. Each extends BaseAction with workflow-specific helper methods and default settings.

Available Specialized Actions

Class	Category	Purpose
BaseTrainAction	NEURAL_NET	Training models
BaseExportAction	EXPORT	Exporting data
BaseUploadAction	UPLOAD	Uploading files
BaseInferenceAction	NEURAL_NET	Running inference
BaseDeploymentAction	-	Ray Serve deployment
AddTaskDataAction	PRE_ANNOTATION	Pre-annotation workflows

Quick Comparison

# Training - autolog, get_dataset, create_model
class TrainAction(BaseTrainAction[TrainParams]):
    def execute(self) -> dict:
        self.autolog('ultralytics')  # Auto-log metrics
        dataset = self.get_dataset()
        # ... train ...
        return self.create_model('./model.pt')

# Export - get_filtered_results
class ExportAction(BaseExportAction[ExportParams]):
    def get_filtered_results(self, filters: dict) -> tuple[Any, int]:
        return self.client.get_assignments(filters)

# Upload - step-based workflow required
class UploadAction(BaseUploadAction[UploadParams]):
    def setup_steps(self, registry: StepRegistry[UploadContext]) -> None:
        registry.register(InitStep())
        registry.register(UploadFilesStep())

# Inference - download_model, load_model, infer
class InferAction(BaseInferenceAction[InferParams]):
    def execute(self) -> dict:
        model = self.load_model(self.params.model_id)
        return {'predictions': self.infer(model, self.params.inputs)}

# Pre-annotation - convert_data_from_file, convert_data_from_inference
class PreAnnotateAction(AddTaskDataAction):
    def convert_data_from_file(self, primary_url, ...) -> dict:
        return {'annotations': [...]}

Execution Modes

All specialized actions (except Deployment) support two modes:

1. Simple Execute: Override execute() for straightforward workflows
2. Step-based: Override setup_steps() for complex multi-step workflows with rollback

# Simple mode
class SimpleTrainAction(BaseTrainAction[Params]):
    def execute(self) -> dict:
        return {'weights_path': '/model.pt'}

# Step-based mode
class StepTrainAction(BaseTrainAction[Params]):
    def setup_steps(self, registry: StepRegistry[TrainContext]) -> None:
        registry.register(LoadDatasetStep())
        registry.register(TrainStep())
        registry.register(UploadModelStep())

Detailed References

• references/train-action.md - Training workflows with autolog, checkpoints
• references/export-action.md - Data export workflows
• references/upload-action.md - File upload with step workflow
• references/inference-action.md - Model inference and deployment
• references/add-task-data-action.md - Pre-annotation workflows
• references/progress-categories.md - Progress category constants