G.O.D Framework

Introduction

The ai_pipeline_deployment.yaml configuration file contains the deployment settings required for managing AI pipelines in the G.O.D Framework. Written in YAML format, it defines the parameters for orchestrating pipelines, resource allocation, runtime environments, and deployment targets.

Purpose

The key objectives of ai_pipeline_deployment.yaml are:

• To specify deployment targets for AI pipelines (e.g., local, cloud-based, containerized environments).
• To provide resource definitions such as memory, CPU, and GPU settings.
• To enable parameterized control over pipeline execution and configuration.
• To simplify and standardize deployment workflows for DevOps and DataOps teams.

Structure

The configuration file uses a structured YAML format. Below is an annotated template:

# ai_pipeline_deployment.yaml

deployment:
  target: "docker"                        # Deployment target (e.g., docker, kubernetes, local)
  environments:                           # List of environments and their configurations
    - name: "production"
      memory_limit: "8GB"                 # Limit memory for production pipelines
      cpu_limit: "4"                      # Limit CPUs allocated
      gpu_support: true                   # Whether GPU support is enabled
      runtime_image: "ai_pipeline_prod"   # Docker runtime image
    - name: "development"
      memory_limit: "2GB"                 # Limit memory for development testing
      cpu_limit: "1"
      gpu_support: false
      runtime_image: "ai_pipeline_dev"

orchestration:
  schedule:                               # Orchestration schedules
    - pipeline: "data_ingestion"
      interval: "daily"                   # Run the pipeline daily
    - pipeline: "model_training"
      interval: "weekly"                  # Run the pipeline weekly

logging:
  level: "INFO"                           # Logging level (DEBUG, INFO, WARNING, etc.)
  output_path: "/var/log/pipeline_logs/"  # Directory to store logs
        

This example specifies production and development environments with unique constraints, orchestration schedules, and centralized logging configurations.

Key Fields

• deployment: Defines where and how the pipeline will run.
  • target: Specifies the type of deployment (Docker, Kubernetes, local execution, etc.).
  • environments: Contains environment-specific settings like memory limits and runtime images.
• orchestration: Provides pipeline schedules and intervals for execution.
• logging: Manages logging configurations for monitoring and debugging pipelines.

Integration with the G.O.D Framework

The ai_pipeline_deployment.yaml file integrates seamlessly with various components of the G.O.D Framework:

• ai_pipeline_orchestrator.py: Utilizes the configuration to schedule and deploy pipelines.
• ai_deployment.py: Reads environment settings to allocate resources for execution.
• logging system: Uses the configured logging fields to remain consistent across modules.

Best Practices

• Ensure that settings in the production and development environments reflect actual resource capacities.
• Use a version control system to manage changes to the YAML file across teams.
• Validate the YAML file for syntax errors before applying changes.
• Keep log outputs centralized to monitor pipeline results effectively.

Future Enhancements

• Automate environment configuration generation using a command-line tool.
• Add CI/CD hooks for validating and testing deployment configurations.
• Enable dynamic updating of parameters without restarting pipelines.