77 lines
3.4 KiB
Markdown
77 lines
3.4 KiB
Markdown
# GeoCrop - Sovereign MLOps Platform
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GeoCrop is a production-grade, self-hosted ML platform designed for crop-type classification in Zimbabwe. It utilizes Sentinel-2 satellite imagery (DEA STAC), computes 51 spectral/phenological features, and employs ensemble ML models to generate high-resolution Cloud Optimized GeoTIFFs (COGs).
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## 🚀 System Architecture
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The platform follows a **Sovereign MLOps** philosophy, hosting the entire lifecycle—from source control and experiment tracking to inference and GitOps—on a private K3s cluster.
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- **Frontend**: React 19 + OpenLayers/Leaflet (Portfolio & App).
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- **Backend**: FastAPI REST API + Redis/RQ Job Queue.
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- **ML Engine**: Python Inference Workers + XGBoost/CatBoost/LightGBM Ensembles.
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- **Infrastructure**:
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- **GitOps**: ArgoCD (CD) + Gitea (Source Control & CI).
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- **Experiment Tracking**: MLflow (Postgres/MinIO backend).
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- **Development**: JupyterLab (integrated with MinIO).
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- **Storage**: MinIO (S3-compatible) for datasets, models, and results.
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- **Database**: Postgres + PostGIS for spatial metadata and app state.
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## 🛠️ Building and Running
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### Development
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```bash
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# Frontend Development
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cd apps/web && npm install && npm run dev
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# API Development
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cd apps/api && pip install -r requirements.txt
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uvicorn main:app --reload
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# Worker Development
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cd apps/worker && pip install -r requirements.txt
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python worker.py --worker
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```
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### GitOps Workflow (CI/CD)
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1. **Push** code to Gitea (`git.techarvest.co.zw`).
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2. **CI**: Gitea Actions build and push Docker images to Docker Hub.
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3. **CD**: ArgoCD detects manifest changes or image updates and reconciles the cluster state.
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### Kubernetes Deployment
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```bash
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# Manual apply (if not using ArgoCD auto-sync)
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kubectl apply -k k8s/base/
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```
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## 📐 Development Conventions
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### Critical Patterns (Non-Obvious)
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- **Kubernetes Only:** Focus exclusively on resources managed by Kubernetes (pods, services, ingresses, etc.). **NEVER** modify host-level Nginx configurations (`/etc/nginx/`), CloudPanel settings, or system services outside the cluster.
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- **AOI Format:** Always use `(lon, lat, radius_m)` tuple. Longitude comes first.
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- **Season Window:** Sept 1st to May 31st (Zimbabwe Summer Season). `year=2022` implies 2022-09-01 to 2023-05-31.
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- **Feature Order:** `FEATURE_ORDER_V1` (51 features) is immutable; changing it breaks model compatibility.
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- **Storage Contract:** Use `geocrop-results` for outputs and `geocrop-models` for serialized artifacts.
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### Storage Layout (MinIO)
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- `geocrop-models/`: ML model `.pkl` files and MLflow artifacts.
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- `geocrop-baselines/`: Dynamic World COGs (`dw/zim/summer/...`).
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- `geocrop-results/`: Output COGs (`results/<job_id>/...`).
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- `geocrop-datasets/`: Training CSVs and ground-truth labels.
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## 📂 Key Files
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- `apps/web/src/App.tsx`: Main React entry point with Portfolio/App view logic.
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- `apps/worker/worker.py`: Core orchestration of the inference pipeline.
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- `k8s/base/`: GitOps manifests for all services (ArgoCD tracking root).
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- `k8s/argocd-app.yaml`: ArgoCD Application definition for GeoCrop.
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- `.gitea/workflows/build-push.yaml`: CI pipeline for Docker builds.
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## 🌐 Infrastructure (Endpoints)
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- **Frontend**: `portfolio.techarvest.co.zw`
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- **API**: `api.portfolio.techarvest.co.zw`
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- **Gitea**: `git.techarvest.co.zw`
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- **ArgoCD**: `cd.techarvest.co.zw`
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- **MLflow**: `ml.techarvest.co.zw`
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- **Jupyter**: `lab.techarvest.co.zw`
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- **Tiler**: `tiles.portfolio.techarvest.co.zw`
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- **MinIO**: `minio.portfolio.techarvest.co.zw`
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