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CogFlow

CogFlow is a modular, SDK-first machine learning workflow management system built on Kubeflow Pipelines, MLflow, Kubernetes, and MinIO.

It provides a clean Python API for:

  • building production-grade ML pipelines
  • managing datasets and components
  • orchestrating federated learning workflows
  • enforcing consistent error handling and validation

CogFlow is designed for real infrastructure, not notebooks only.


Why CogFlow?

Modern ML platforms are powerful but fragmented:

  • Kubeflow Pipelines → great orchestration, weak ergonomics
  • MLflow → experiment tracking, limited workflow control
  • Kubernetes → powerful, but verbose and error-prone
  • Federated learning → no standard orchestration layer

CogFlow bridges these gaps by providing:

  • a stable Python SDK
  • safe lazy-loading of heavy dependencies
  • unified error handling
  • infrastructure-aware abstractions
  • zero circular imports

Core Features

🧩 Pipeline Orchestration

  • Lazy-loaded Kubeflow Pipelines client
  • Safe pipeline compilation and submission
  • Runtime environment injection
  • Kubernetes service lifecycle management

📦 Component Management

  • YAML-based component registry
  • MinIO-backed component storage
  • Automatic component registration
  • Runtime-safe component loading

📊 Dataset Management

  • Dataset registration and metadata retrieval
  • Secure dataset downloads
  • Silent deletes with strict error semantics
  • Pluggable storage backends

🤝 Federated Learning

  • Auto-generated FL pipelines
  • Dynamic pipeline signatures
  • Connector-based and dataspace-based workflows
  • Region-aware scheduling with node selectors

🧠 Unified Error Handling

  • Strongly typed error hierarchy
  • Context-aware exception wrapping
  • API-ready error serialization
  • Zero silent failures

Architecture Overview

CogFlow follows a layered SDK architecture:

cogflow/ ├── core/ │ ├── pipelines/ # Kubeflow orchestration & FL pipelines │ ├── datasets/ # Dataset lifecycle management │ ├── components/ # Component registry & YAML handling │ └── models/ # (future extension) │ ├── utils/ │ ├── common.py # UUIDs, paths, Kubernetes helpers │ ├── network.py # HTTP utilities with retry & streaming │ ├── storage.py # MinIO client abstraction │ └── exceptions.py # Unified error framework │ ├── config.py └── api.py

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