mlflow-full/README.md

# mlflow-full

MLFlow docker container with support for Postgres. Implements extra environment variables for convenience.

## Running the Container

### Basic Usage

```bash
# Run with default settings
docker run -p 5000:5000 gitea.gt-proj.com/brian/mlflow-full:latest
```

### With PostgreSQL Backend

```bash
# Using environment variables
docker run -p 5000:5000 \
  -e MLFLOW_BACKEND_STORE_URI="postgresql://user:password@host:5432/mlflow" \
  -e MLFLOW_DEFAULT_ARTIFACT_ROOT="s3://my-bucket/mlflow-artifacts" \
  gitea.gt-proj.com/brian/mlflow-full:latest
```

### Complete Example with Docker Compose

```yaml
version: '3.8'

services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: mlflow
      POSTGRES_USER: mlflow
      POSTGRES_PASSWORD: mlflow_password
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  mlflow:
    image: gitea.gt-proj.com/brian/mlflow-full:latest
    ports:
      - "5000:5000"
    environment:
      MLFLOW_BACKEND_STORE_URI: postgresql://mlflow:mlflow_password@postgres:5432/mlflow
      MLFLOW_DEFAULT_ARTIFACT_ROOT: /mlflow/artifacts
      MLFLOW_HOST: 0.0.0.0
      MLFLOW_PORT: 5000
    volumes:
      - mlflow_artifacts:/mlflow/artifacts
    depends_on:
      - postgres

volumes:
  postgres_data:
  mlflow_artifacts:
```

### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `MLFLOW_BACKEND_STORE_URI` | Database connection string | `sqlite:///mlflow.db` |
| `MLFLOW_DEFAULT_ARTIFACT_ROOT` | Artifact storage location | `./mlruns` |
| `MLFLOW_HOST` | Host to bind the server | `0.0.0.0` |
| `MLFLOW_PORT` | Port to bind the server | `5000` |

### Advanced Examples

#### With S3 Artifact Storage

```bash
docker run -p 5000:5000 \
  -e MLFLOW_BACKEND_STORE_URI="postgresql://user:password@host:5432/mlflow" \
  -e MLFLOW_DEFAULT_ARTIFACT_ROOT="s3://my-mlflow-bucket/artifacts" \
  -e AWS_ACCESS_KEY_ID="your-access-key" \
  -e AWS_SECRET_ACCESS_KEY="your-secret-key" \
  -e AWS_DEFAULT_REGION="us-west-2" \
  gitea.gt-proj.com/brian/mlflow-full:latest
```

#### With Custom Server Arguments

```bash
docker run -p 5000:5000 \
  -e MLFLOW_BACKEND_STORE_URI="postgresql://user:password@host:5432/mlflow" \
  gitea.gt-proj.com/brian/mlflow-full:latest \
  --host 0.0.0.0 \
  --port 5000 \
  --workers 4 \
  --expose-prometheus /metrics
```

### Kubernetes Deployment

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow-server
  template:
    metadata:
      labels:
        app: mlflow-server
    spec:
      containers:
      - name: mlflow-server
        image: gitea.gt-proj.com/brian/mlflow-full:latest
        ports:
        - containerPort: 5000
        env:
        - name: MLFLOW_BACKEND_STORE_URI
          value: "postgresql://mlflow:password@postgres-service:5432/mlflow"
        - name: MLFLOW_DEFAULT_ARTIFACT_ROOT
          value: "s3://mlflow-artifacts"
---
apiVersion: v1
kind: Service
metadata:
  name: mlflow-service
spec:
  selector:
    app: mlflow-server
  ports:
  - port: 5000
    targetPort: 5000
  type: LoadBalancer
```

## Features

- 🐘 **PostgreSQL Support**: Pre-installed `psycopg2-binary` for PostgreSQL connectivity
- 🏷️ **OCI Labels**: Fully compliant with OCI image specification
- 🔄 **Auto-updates**: Daily builds when the base MLflow image is updated
- 📦 **Lightweight**: Based on official MLflow container

## Building Locally

```bash
# Clone the repository
git clone https://gitea.gt-proj.com/brian/mlflow-full.git
cd mlflow-full

# Build the image
docker build -t mlflow-full:local .

# Run your local build
docker run -p 5000:5000 mlflow-full:local
```