Skynet is a service designed for advanced model management, transformation, ranking, and visualization. It supports dynamic APIs for working with machine learning models and provides extensive health checks, metrics, and documentation.

• Model Loading & Management: Load, unload, and list models dynamically via API.
• Data Transformation: Transform data using preloaded models.
• Ranking Framework: Rank model outputs with customizable parameters.
• Visualization: Generate computation graphs of loaded models.
• Health Checks: Monitor both service and model health.
• Swagger Documentation: Auto-generated OpenAPI specs with Swagger UI.
• Metrics: Comprehensive monitoring and metrics for endpoints.

• Scala 2.13. and sbt for building and running the service.
• Docker for containerization (optional).
• Dependencies are managed in the SBT build file.

• Main Entry Point: Starter.scala

  • Bootstraps the service, initializes configurations, and starts the server.
  • Uses ZIO for dependency injection and environment management.

• API Definition: Endpoints.scala

  • Defines RESTful API endpoints using Tapir.
  • Includes model management, transformation, and health check APIs.

• Routing: Routes.scala

  • Maps endpoints to route handlers and integrates Swagger for API documentation.
  • Includes middleware for metrics and error handling.

• Build Configuration: build.sbt

  • Handles project dependencies, build plugins, and Docker configurations.

• Load a Model
  PUT /models/{model}
  Loads a model from a given URI (e.g., file://, s3://).

• Unload a Model
  DELETE /models/{model}
  Unloads a model from the service.

• List All Models
  GET /models
  Lists all currently loaded models.

• Model Health Check
  GET /models/{model}/health
  Performs a test prediction to verify the model is operational.

• Transform Data
  POST /models/{model}/transform
  Transforms input data using the specified model.

• Rank Data
  POST /models/{model}/rank
  Ranks transformed data with options for grouping and averaging.

• Get Sample Data
  GET /models/{model}/sample
  Returns a sample data frame for a model.

• Model Graph
  GET /models/{model}/graph
  Visualizes the computation graph of the model.

• Service Health Check
  GET /health
  Confirms the service is operational.

sbt compile

sbt run

The service will start and provide API access via http://localhost:8080.

A Docker image can be built using:

sbt docker:publishLocal

This will create a containerized version of the service.

• ZIO: Functional effect handling.
• Tapir: API definition and OpenAPI documentation.
• Http4s: Web server and client.

• MLeap: Runtime and executor for machine learning pipelines.
• XGBoost: Predictor integration. The implementation here is much faster than MLeaps original implementation. This allows for BLAZINGLY FAST XGBoost serving!

• PureConfig: Simplified configuration management.
• Typesafe Config: Configuration library.

• Circe: JSON serialization and parsing.
• Protobuf: Support for protocol buffers.

• Logback: Logging framework.
• Scala Logging: Integration for logging in Scala.

• Swagger UI: Interactive API documentation.
• RefTree: Visualization of computation graphs.

• Prometheus: Metrics collection and integration.

Swagger UI is available at: http://localhost:8080/swagger-ui

This provides an interactive interface to explore and test the API.

Metrics are exposed at: http://localhost:8080/metrics

These include endpoint-specific metrics and overall service health data.

Feel free to contribute by submitting issues or pull requests. Follow functional programming principles and the existing code style.