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IMPLEMENTATION_CHECKLIST.md

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Comprehensive Implementation Checklist

✅ Backend Architecture

Core Infrastructure

  • FastAPI main application (app/main.py)
  • Pydantic configuration management (app/core/config.py)
  • Structured logging with JSON output (app/core/logging.py)
  • Celery task configuration (app/core/tasks.py)
  • CORS middleware setup
  • Exception handlers and error responses

Data Models (Pydantic)

  • Job models (app/models/job.py)
    • JobBase, JobCreate, JobUpdate, JobResponse
    • JobListResponse, JobLogResponse, JobCancellationResponse
  • Dataset models (app/models/dataset.py)
    • DatasetBase, DatasetCreate, DatasetResponse
    • DatasetListResponse, DatasetPreview, DatasetSchema
  • Result models (app/models/result.py)
    • ResultBase, ResultResponse, ResultListResponse
    • NetworkComparison, ResultSummary, ResultMetrics

Services Layer

  • Dataset service (app/services/datasets_service.py)
    • Register, list, get, delete datasets
    • Dataset metadata management
    • Preview functionality
    • Schema validation
  • Job service (app/services/jobs_service.py)
    • Job submission and lifecycle
    • Status tracking
    • Log retrieval
    • Job cancellation
  • Inference service (app/services/inference_service.py)
    • Algorithm execution orchestration
    • Docker and Python command preparation
    • Output validation
    • Metrics computation

API Routers

  • Dataset endpoints (app/api/datasets.py)
    • POST /register - Register new dataset
    • GET / - List datasets with pagination
    • GET /{dataset_id} - Get dataset details
    • GET /{dataset_id}/preview - Preview dataset
    • PATCH /{dataset_id} - Update metadata
    • DELETE /{dataset_id} - Delete dataset
  • Job endpoints (app/api/jobs.py)
    • POST / - Submit job
    • GET / - List jobs with filters
    • GET /{job_id} - Get job status
    • GET /{job_id}/logs - Get logs
    • DELETE /{job_id} - Cancel job
  • Result endpoints (app/api/results.py)
    • GET /job/{job_id} - Get result
    • GET /job/{job_id}/summary - Get summary
    • POST /compare - Compare networks
    • GET /job/{job_id}/network/download - Download network
    • POST /job/{job_id}/export - Export results

Runner Framework

  • Base runner class (app/services/runners/utils.py)
    • BaseRunner abstract class
    • Input/output validation
    • Expression data loading
    • Network file saving
    • Correlation and MI computation
  • Generic GRN runner (app/services/runners/generic_runner.py)
    • Extensible base for all algorithms
    • Correlation-based inference
    • Parameter handling
  • Runner utilities for algorithm integration

Celery Worker Tasks

  • Celery configuration (app/core/tasks.py)
    • Broker and backend setup
    • Queue configuration
    • Task scheduling
  • Task definitions (app/workers/tasks.py)
    • run_inference_job - Main inference execution
    • compare_networks - Network comparison
    • compute_metrics - Metrics calculation
    • export_results - Multi-format export
    • cleanup_old_results - Scheduled cleanup

✅ Deployment & DevOps

  • Dockerfile with multi-stage build
  • docker-compose.yml with all services
    • FastAPI backend
    • Celery worker
    • Celery beat scheduler
    • Flower monitoring
    • Redis
  • Environment file template (.env.example)
  • Production startup script (run.py)
  • Development setup script (setup-dev.sh)
  • Configuration management via environment variables

✅ Testing & Documentation

Testing Suite

  • Pytest configuration (tests/conftest.py)
    • Test client fixture
    • Sample dataset fixture
    • Sample network fixture
    • Mock data fixtures
  • API tests (tests/test_api.py)
    • Health check
    • Root endpoint
    • Algorithm listing
    • Dataset CRUD
    • Job submission
    • Job filtering

Documentation

  • Comprehensive README
  • API documentation (Swagger/OpenAPI auto-generated)
  • Frontend integration guide (FRONTEND_INTEGRATION.md)
  • Installation and setup instructions
  • Usage examples with curl/code
  • Troubleshooting guide
  • Configuration documentation
  • Docker deployment guide

✅ Package Management

  • requirements.txt with pinned versions
  • pyproject.toml with project metadata
  • Development dependencies defined
  • Tool configurations (black, isort, mypy, pytest)

✅ Features Implemented

Algorithm Support

  • Support for all 14 Beeline algorithms
    • SCODE, SCNS, SINCERITIES, PIDC, GRNVBEM
    • GENIE3, GRNBOOST2, LEAP, JUMP3
    • PPCOR, GRISLI, SINGE, SCRIBE, SCSGL
  • Algorithm listing endpoint
  • Docker image registry configuration
  • Algorithm parameter handling

Dataset Management

  • Multiple dataset sources (local, HuggingFace, S3)
  • Dataset schema validation
  • Dataset metadata persistence
  • Dataset preview functionality
  • CSV, TSV, h5ad format support

Job Management

  • Asynchronous job submission via Celery
  • Job status tracking (pending, running, completed, failed, cancelled)
  • Real-time progress updates
  • Job cancellation support
  • Comprehensive job logging
  • Job filtering by status, dataset, algorithm

Results Management

  • Network file storage
  • Automatic metrics computation
  • Network comparison functionality
  • Multi-format export (JSON, GraphML, CSV)
  • Result caching and retrieval
  • Result summaries

Monitoring & Observability

  • Structured JSON logging
  • Log level configuration
  • Flower dashboard integration
  • Health check endpoint
  • Task status tracking via Celery

✅ Integration Ready

Frontend Integration

  • CORS configuration
  • RESTful API design
  • Comprehensive API documentation
  • Example API client implementations
  • Frontend integration guide
  • TypeScript/React component examples

Database (Prepared for)

  • SQLAlchemy ORM support
  • Alembic migration setup
  • Support for SQLite, PostgreSQL, MySQL