SnapOtter/apps/docs/guide/deployment.md

# Deployment

ashim ships as a single Docker container. The image supports **linux/amd64** (with NVIDIA CUDA) and **linux/arm64** (CPU), so it runs natively on Intel/AMD servers, Apple Silicon Macs, and ARM devices like the Raspberry Pi 4/5.

See [Docker Image](./docker-tags) for GPU setup, Docker Compose examples, and version pinning.

## Quick Start (CPU)

```yaml
# docker-compose.yml — Copy this file and run: docker compose up -d
services:
  ashim:
    image: ashimhq/ashim:latest    # or ghcr.io/ashim-hq/ashim:latest
    container_name: ashim
    ports:
      - "1349:1349"                # Web UI + API
    volumes:
      - ashim-data:/data           # Database, AI models, user files (PERSISTENT)
      - ashim-workspace:/tmp/workspace  # Temp processing files (can be tmpfs)
    environment:
      # --- Authentication ---
      - AUTH_ENABLED=true          # Set to false to disable login entirely
      - DEFAULT_USERNAME=admin     # First-run admin username
      - DEFAULT_PASSWORD=admin     # First-run admin password (you'll be forced to change it)

      # --- Limits (0 = unlimited) ---
      # - MAX_UPLOAD_SIZE_MB=0     # Per-file upload limit in MB
      # - MAX_BATCH_SIZE=0         # Max files per batch request
      # - RATE_LIMIT_PER_MIN=0     # API rate limit (0 = disabled, 100 = recommended for public)
      # - MAX_USERS=0              # Max user accounts

      # --- Networking ---
      # - TRUST_PROXY=true         # Trust X-Forwarded-For headers (set false if not behind a proxy)

      # --- Bind mount permissions ---
      # - PUID=1000                # Match your host user's UID (run: id -u)
      # - PGID=1000                # Match your host user's GID (run: id -g)
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:1349/api/v1/health"]
      interval: 30s
      timeout: 5s
      start_period: 60s
      retries: 3
    shm_size: "2gb"            # Needed for Python ML shared memory
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

volumes:
  ashim-data:       # Named volume — Docker manages permissions automatically
  ashim-workspace:
```

```bash
docker compose up -d
```

The app is then available at `http://localhost:1349`.

> **Docker Hub rate limits?** Replace `ashimhq/ashim:latest` with `ghcr.io/ashim-hq/ashim:latest` to pull from GitHub Container Registry instead. Both registries receive the same image on every release.

## Quick Start (GPU)

For NVIDIA GPU acceleration on AI tools (background removal, upscaling, face enhancement, OCR):

```yaml
# docker-compose-gpu.yml — Requires: NVIDIA GPU + nvidia-container-toolkit
# Install toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
services:
  ashim:
    image: ashimhq/ashim:latest
    container_name: ashim
    ports:
      - "1349:1349"
    volumes:
      - ashim-data:/data
      - ashim-workspace:/tmp/workspace
    environment:
      - AUTH_ENABLED=true
      - DEFAULT_USERNAME=admin
      - DEFAULT_PASSWORD=admin
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:1349/api/v1/health"]
      interval: 30s
      timeout: 5s
      start_period: 60s
      retries: 3
    shm_size: "2gb"                # Required for PyTorch CUDA shared memory
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all           # Or set to 1 for a specific GPU
              capabilities: [gpu]
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

volumes:
  ashim-data:
  ashim-workspace:
```

```bash
docker compose -f docker-compose-gpu.yml up -d
```

Check GPU detection in the logs:

```bash
docker logs ashim 2>&1 | head -20
# Look for: [INFO] GPU detected — AI tools will use CUDA acceleration
```

## Hardware Requirements

These numbers come from benchmarks run across four systems (Apple M2 Max, AMD Ryzen 5 7500F + RTX 4070, Intel i7-7600U, Docker Desktop on Windows). See `docs/HARDWARE_RECOMMENDATIONS.md` in the repo for full methodology and raw data.

### Quick Reference

| Tier | Use Case | CPU | RAM | GPU | Storage |
|------|----------|-----|-----|-----|---------|
| Minimum | Core tools, single user | 1 core | 1 GB | None | 5 GB |
| Recommended | All tools + AI on CPU | 4 cores | 4 GB | None | 20 GB |
| Full | All tools + AI on GPU | 4+ cores | 8 GB | NVIDIA 8 GB+ | 30 GB |

### Minimum (core image tools)

| Resource | Requirement |
|---|---|
| CPU | 1 core |
| RAM | 1 GB |
| Disk | 3 GB (image) + 1 GB (data volume) |
| GPU | Not required |

All 35 non-AI tools (resize, crop, rotate, convert, compress, watermark, collage, etc.) run on any hardware. Most operations complete in under 1 second even on a single core. The exception is AVIF encoding, which takes ~27s on 1 core but drops to ~5s on 4 cores.

```yaml
deploy:
  resources:
    limits:
      cpus: '1'
      memory: 1G
```

### Recommended (AI tools on CPU)

| Resource | Requirement |
|---|---|
| CPU | 4 cores |
| RAM | 4 GB |
| Disk | 3 GB (image) + 14 GB (AI models) + workspace |
| GPU | Not required (CPU fallback) |

AI tools work on CPU but are significantly slower. Some tools are practical on CPU, others are not:

| AI Tool | CPU Time | Usable? |
|---|---|---|
| blur-faces, smart-crop, red-eye-removal | 2-5s | Yes |
| remove-background | 37-41s | Marginal (long wait) |
| upscale (small image) | 22s | Marginal |
| upscale (large image) | 241s | No |
| enhance-faces, colorize, noise-removal | 30-90s | Marginal to No |

AI model download sizes:

| Bundle | Disk Size |
|---|---|
| Background removal | 3-4 GB |
| Upscale + Face enhance + Noise removal | 4-5 GB |
| Face detection | 200-300 MB |
| Object eraser + Colorize | 1-2 GB |
| OCR | 3-4 GB |
| Photo restoration | 800 MB - 1 GB |
| **All bundles** | **~14 GB** |

```yaml
deploy:
  resources:
    limits:
      cpus: '4'
      memory: 4G
```

### Full (AI tools on GPU)

| Resource | Requirement |
|---|---|
| CPU | 4+ cores |
| RAM | 8 GB |
| GPU | NVIDIA with 8+ GB VRAM (12 GB recommended) |
| Disk | 30 GB total |

GPU acceleration gives 3-13,000x speedup depending on the operation. Measured on an RTX 4070 vs Intel i7-7600U:

| AI Tool | GPU Time | CPU Time | Speedup |
|---|---|---|---|
| noise-removal (quick) | 17ms | 228s | 13,400x |
| blur-faces | 0.27s | 27s | 100x |
| upscale 2x | 6.3s | >300s (timeout) | 47x+ |
| enhance-faces (GFPGAN) | 2.3s | 28s | 12x |
| remove-background | 5-10s | 21-41s | 3-8x |
| OCR (best) | 70s | 243s | 3.5x |
| restore-photo | 31s | 90s | 2.9x |
| colorize | 10s | 13s | 1.3x |

Peak VRAM usage reaches 7.5 GB during upscale with face enhancement. A 6 GB GPU works for most AI tools individually but will fail on upscale. 8-12 GB VRAM handles everything.

```yaml
deploy:
  resources:
    limits:
      cpus: '4'
      memory: 8G
    reservations:
      devices:
        - driver: nvidia
          count: all
          capabilities: [gpu]
```

### Concurrent Users

Benchmarked with parallel resize requests on a large image (Mac M2 Max, 10 Docker CPUs):

| Concurrent Users | Avg Response Time | Errors |
|---|---|---|
| 1 | 0.28s | 0 |
| 5 | 0.54s | 0 |
| 10 | 1.08s | 0 |
| 20 | 2.10s | 0 |

The server scales linearly with no errors or crashes up to 20 concurrent requests.

### Supported Image Formats

| Format | Read | Write | Notes |
|---|---|---|---|
| JPEG | Yes | Yes | |
| PNG | Yes | Yes | |
| WebP | Yes | Yes | |
| AVIF | Yes | Yes | Encode is CPU-intensive (~5s on 4 cores for a large image) |
| GIF | Yes | Yes | Animated GIF supported |
| TIFF | Yes | Yes | Multi-page supported |
| SVG | Yes | No | Rasterized on input, sanitized for security |
| HEIC | Yes | No | Decoded via heif-dec (~0.4s) |
| HEIF | Yes | No | Very slow decode (~15s) |
| DNG (RAW) | Yes (Linux) | No | Decoded via dcraw, not available on macOS |
| PSD | Yes | No | Decoded via ImageMagick |
| HDR | Yes | No | Tone-mapped on decode |
| TGA | Yes | No | Decoded via ImageMagick |
| ICO | Yes | Yes | Via favicon tool |
| PDF | Yes | Yes | Via pdf-to-image / image-to-pdf tools |

Not supported: BMP (V4/V5 headers), JPEG XL (JXL), EXR (missing decode delegate in Docker image).

### Known Limitations

- **Content-aware resize** crashes on large images (>5 MP) due to a limitation in the caire binary. Works fine with smaller images.
- **HEIF decode** takes 13-23 seconds. HEIC (Apple's variant) is much faster at 0.3-0.9 seconds.
- **OCR Japanese** fails on CPU due to a PaddlePaddle MKLDNN bug. Works on GPU.
- **Upscale** times out on CPU for anything beyond small images. GPU required for practical use.
- **CodeFormer** face enhancement is significantly slower than GFPGAN (53s vs 2s on GPU). GFPGAN is recommended for most use cases.

## Volumes

| Mount | Purpose | Required? |
|---|---|---|
| `/data` | SQLite database, AI models, Python venv, user files | **Yes** — data loss without it |
| `/tmp/workspace` | Temporary processing files (auto-cleaned) | Recommended |

### Bind mounts vs. named volumes

**Named volumes** (recommended) — Docker manages permissions automatically:
```yaml
volumes:
  - ashim-data:/data
```

**Bind mounts** — You manage permissions. Set `PUID`/`PGID` to match your host user:
```yaml
volumes:
  - ./ashim-data:/data
environment:
  - PUID=1000    # Your host UID (run: id -u)
  - PGID=1000    # Your host GID (run: id -g)
```

## Environment Variables

| Variable | Default | Description |
|---|---|---|
| `AUTH_ENABLED` | `true` | Enable/disable login requirement |
| `DEFAULT_USERNAME` | `admin` | Initial admin username |
| `DEFAULT_PASSWORD` | `admin` | Initial admin password (forced change on first login) |
| `MAX_UPLOAD_SIZE_MB` | `0` (unlimited) | Per-file upload limit |
| `MAX_BATCH_SIZE` | `0` (unlimited) | Max files per batch request |
| `RATE_LIMIT_PER_MIN` | `0` (disabled) | API requests per minute per IP |
| `MAX_USERS` | `0` (unlimited) | Maximum user accounts |
| `TRUST_PROXY` | `true` | Trust X-Forwarded-For headers from reverse proxy |
| `PUID` | `999` | Run as this UID (for bind mount permissions) |
| `PGID` | `999` | Run as this GID (for bind mount permissions) |
| `LOG_LEVEL` | `info` | Log verbosity: fatal, error, warn, info, debug, trace |
| `CONCURRENT_JOBS` | `0` (auto) | Max parallel AI processing jobs |
| `SESSION_DURATION_HOURS` | `168` | Login session lifetime (7 days) |
| `CORS_ORIGIN` | (empty) | Comma-separated allowed origins, or empty for same-origin |

## Health Check

The container includes a built-in health check:

```bash
# Check container health status
docker inspect --format='{{.State.Health.Status}}' ashim

# Manual health check
curl http://localhost:1349/api/v1/health
# {"status":"healthy","version":"1.15.9"}
```

## Reverse Proxy

ashim sets `TRUST_PROXY=true` by default so rate limiting and logging use the real client IP from `X-Forwarded-For` headers.

### Nginx

```nginx
server {
    listen 80;
    server_name images.example.com;

    # Match MAX_UPLOAD_SIZE_MB (0 = nginx default 1M, so set high for unlimited)
    client_max_body_size 500M;

    location / {
        proxy_pass http://localhost:1349;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;

        # SSE support (batch progress, feature install progress)
        proxy_buffering off;
        proxy_read_timeout 300s;
    }
}
```

### Nginx Proxy Manager

1. Add a new Proxy Host
2. Set Domain Name to your domain
3. Set Scheme to `http`, Forward Hostname to `ashim` (or your container IP), Forward Port to `1349`
4. Enable WebSocket support
5. Under Advanced, add: `client_max_body_size 500M;` and `proxy_buffering off;`

### Traefik

```yaml
# Add these labels to the ashim service in docker-compose.yml
labels:
  - "traefik.enable=true"
  - "traefik.http.routers.ashim.rule=Host(`images.example.com`)"
  - "traefik.http.routers.ashim.entrypoints=websecure"
  - "traefik.http.routers.ashim.tls.certresolver=letsencrypt"
  - "traefik.http.services.ashim.loadbalancer.server.port=1349"
  # Increase upload limit (default 2MB is too low)
  - "traefik.http.middlewares.ashim-body.buffering.maxRequestBodyBytes=524288000"
  - "traefik.http.routers.ashim.middlewares=ashim-body"
```

### Cloudflare Tunnels

```bash
cloudflared tunnel --url http://localhost:1349
```

Note: Cloudflare has a 100 MB upload limit on free plans. Set `MAX_UPLOAD_SIZE_MB=100` to match.

## CI/CD

The GitHub repository has three workflows:

- **ci.yml** -- Runs automatically on every push and PR. Lints, typechecks, tests, builds, and validates the Docker image (without pushing).
- **release.yml** -- Triggered manually via `workflow_dispatch`. Runs semantic-release to create a version tag and GitHub release, then builds a multi-arch Docker image (amd64 + arm64) and pushes to Docker Hub (`ashimhq/ashim`) and GitHub Container Registry (`ghcr.io/ashim-hq/ashim`).
- **deploy-docs.yml** -- Builds this documentation site and deploys it to GitHub Pages on push to `main`.

To create a release, go to **Actions > Release > Run workflow** in the GitHub UI, or run:

```bash
gh workflow run release.yml
```

Semantic-release determines the version from commit history. The `latest` Docker tag always points to the most recent release.