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docs: add CUDA tag to README, getting-started, deployment, benchmarks

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- README: add CUDA docker run example alongside full and lite
- Getting started: add GPU acceleration tip with speedup numbers
- Deployment: add CUDA row to variants table
- Docker tags: expand benchmarks with warm + cold start tables
This commit is contained in:
Siddharth Kumar Sah
2026-04-05 20:35:24 +08:00
parent 9381564269
commit f1ae974de9
4 changed files with 42 additions and 11 deletions
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README.md
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@@ -45,6 +45,12 @@ Don't need AI tools? The lite image is 1.5 GB instead of 11 GB:
docker run -d -p 1349:1349 -v stirling-data:/data stirlingimage/stirling-image:lite
```
Have an NVIDIA GPU? The CUDA image accelerates background removal, upscaling, and OCR:
```bash
docker run -d -p 1349:1349 --gpus all -v stirling-data:/data stirlingimage/stirling-image:cuda
```
Open http://localhost:1349 in your browser.
**Default credentials:**
@@ -56,7 +62,7 @@ Open http://localhost:1349 in your browser.
You will be asked to change your password on first login. This is enforced for all new accounts and cannot be skipped in production.
For Docker Compose, persistent storage, and other setup options, see the [Getting Started Guide](https://stirling-image.github.io/stirling-image/guide/getting-started). For details on the full vs lite image, see [Docker Tags](https://stirling-image.github.io/stirling-image/guide/docker-tags).
For Docker Compose, persistent storage, and other setup options, see the [Getting Started Guide](https://stirling-image.github.io/stirling-image/guide/getting-started). For details on all image variants (full, lite, cuda), see [Docker Tags](https://stirling-image.github.io/stirling-image/guide/docker-tags).
## Documentation
apps/docs/guide/deployment.md
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@@ -2,12 +2,13 @@
Stirling Image ships as a single Docker container. The image supports **linux/amd64** and **linux/arm64**, so it runs natively on Intel/AMD servers, Apple Silicon Macs, and ARM devices like the Raspberry Pi 4/5.
Two variants are available:
Three variants are available:
| Variant | Tag | Size | What's included |
|---------|-----|------|-----------------|
| Full | `:latest` | ~11 GB | All tools + AI/ML (background removal, upscaling, OCR, face blur, object eraser) |
| Lite | `:lite` | ~1.5 GB | All image processing tools, no AI/ML |
| CUDA | `:cuda` | ~14 GB | Full + GPU-accelerated AI (NVIDIA only, amd64) |
See [Docker Tags](./docker-tags) for the full comparison, Docker Compose examples, and version pinning.
apps/docs/guide/docker-tags.md
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@@ -45,18 +45,32 @@ Same tools as the full image, but built with GPU-accelerated Python packages (on
Requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) on the host. Linux amd64 only.
### What gets faster with GPU
### Benchmarks
| Tool | CPU | GPU (RTX 4070) | Speedup |
|------|-----|----------------|---------|
| Background removal | 2.4s | 0.9s | 2.7x |
| Upscale 2x | 350ms | 310ms | ~1.1x |
| Upscale 4x | 910ms | 310ms | ~3x |
| OCR (PaddleOCR) | 137ms | 94ms | ~1.5x |
Tested on an NVIDIA RTX 4070 (12 GB VRAM) with a 572x1024 JPEG portrait. Both images ran on the same machine. "Warm" means the model is already loaded in memory (second request onward).
Benchmarked with a 572x1024 JPEG portrait. Larger images show bigger speedups, especially for upscaling.
#### Warm performance
Non-AI tools (resize, crop, convert, etc.) are unaffected since they use Sharp (CPU-based).
| Tool | CPU | GPU | Speedup |
|------|-----|-----|---------|
| Background removal (u2net) | 2,415ms | 879ms | 2.7x |
| Background removal (isnet) | 2,457ms | 1,137ms | 2.2x |
| Upscale 2x | 350ms | 309ms | 1.1x |
| Upscale 4x | 910ms | 310ms | 2.9x |
| OCR (PaddleOCR) | 137ms | 94ms | 1.5x |
| Face blur | 139ms | 122ms | 1.1x |
#### Cold start (first request after container start)
| Tool | CPU | GPU | Speedup |
|------|-----|-----|---------|
| Background removal | 22,286ms | 4,792ms | 4.7x |
| Upscale 2x | 3,957ms | 2,318ms | 1.7x |
| OCR (PaddleOCR) | 1,469ms | 1,090ms | 1.3x |
Cold start includes loading the model into memory. GPU cold starts are faster because CUDA parallelizes the model loading.
Larger images show bigger speedups, especially for upscaling. Non-AI tools (resize, crop, convert, etc.) are unaffected since they use Sharp (CPU-based).
### GPU health check
apps/docs/guide/getting-started.md
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@@ -24,6 +24,16 @@ stirlingimage/stirling-image:lite
All 27+ image processing tools work the same. See [Docker Tags](./docker-tags) for the full comparison.
:::
::: tip GPU acceleration
Have an NVIDIA GPU? The CUDA image auto-detects your GPU and accelerates background removal (2.7x), upscaling (3x), and OCR (1.5x):
```bash
docker run -d --gpus all -p 1349:1349 -v stirling-data:/data stirlingimage/stirling-image:cuda
```
Requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). Falls back to CPU if no GPU is found. See [Docker Tags](./docker-tags) for details and benchmarks.
:::
## Run with Docker Compose
Create a `docker-compose.yml`: