ML Task Runner

The ML Task Runner is a specialized component that executes predefined machine learning tasks and workflows. It provides a structured way to run ML operations with configurable parameters and monitoring capabilities.

ML Task Runner interface and configuration

Usage Note: Select appropriate ML tasks based on your requirements. Ensure all necessary dependencies and resources are available before task execution.

Component Inputs

ML Task: The machine learning task to execute
Example: Select from available ML tasks like "Training", "Inference", "Evaluation"

Component Outputs

Tool Result: The outcome of the ML task execution
Example: Task completion status, metrics, or generated outputs

How It Works

The ML Task Runner orchestrates the execution of machine learning tasks through a systematic workflow. It handles task initialization, resource management, execution monitoring, and result collection.

Execution Process

Task selection and validation
Resource allocation
Task initialization
Execution monitoring
Result collection
Resource cleanup

Use Cases

Model Training: Execute training workflows
Inference Tasks: Run model predictions
Evaluation Jobs: Perform model assessments
Data Processing: Handle data preparation tasks
Pipeline Execution: Run ML pipelines

Implementation Example

const mlTaskRunner = new MLTaskRunner({
  mlTask: "ModelTraining",
  parameters: {
    modelType: "classification",
    dataset: "training_data.csv",
    epochs: 100
  }
});

const result = await mlTaskRunner.execute();

// Output:
// {
//   status: "completed",
//   metrics: {
//     accuracy: 0.95,
//     loss: 0.05,
//     trainingTime: "2h 30m"
//   },
//   artifacts: {
//     modelPath: "/models/trained_model.h5",
//     logsPath: "/logs/training.log"
//   }
// }

Additional Resources

Best Practices

Validate task requirements before execution
Monitor resource utilization
Implement proper error handling
Keep task configurations versioned
Regular task performance reviews

Documentation