> For the complete documentation index, see [llms.txt](https://docs.viesus.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.viesus.com/use-cases/photo-lab-batch.md).

# Batch Processing

**The scenario:** A photo lab receives thousands of customer images per day across many orders. Images vary in size and quality, and every order must come back enhanced with consistent settings — without anyone editing images by hand.

***

## Recommended interface

{% hint style="info" %}
Use the [**VIESUS CLI**](/reference/cli-reference.md). It is built for unattended, high-volume batch processing on Windows or Linux servers and integrates into any script, scheduler, or orchestration tool.
{% endhint %}

The CLI reads a list of images (or a folder), applies one shared configuration to every image, and writes the enhanced files out. For interactive workloads behind a web service instead, see the [Node.js module](/use-cases/nodejs-saas.md); for PDFs, see [PDF Processing](/use-cases/pdf-photobook-workflow.md).

***

## How it works

* You define one enhancement configuration (`viesusini.json`) and reuse it across the whole lab, so results stay consistent regardless of who runs the job.
* The CLI processes a batch — a folder or an image list — in one run, writing each enhanced image to a destination folder.
* A per-image result file records the status of every image, which your monitoring can scan to confirm an order completed cleanly.
* Throughput scales by running more CLI instances in parallel — for example one per order, or one per GPU on a multi-GPU machine.

***

## What to consider

| Factor          | Guidance                                                                                                                                                                                            |
| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Hardware**    | A compatible NVIDIA GPU is recommended for AI features (AI upscaling, Artifact Removal) and speed. Standard enhancement also runs on CPU. See [System Requirements](/installation/requirements.md). |
| **Consistency** | Keep a single configuration per product type so every order is enhanced identically.                                                                                                                |
| **Throughput**  | Plan capacity against your daily volume — see [Benchmarks](/operations/benchmarks.md) and [Performance Tuning](/operations/performance-tuning.md).                                                  |
| **Scaling**     | Parallelism comes from running multiple instances, not from one giant job.                                                                                                                          |
| **Storage**     | Enhanced output can be larger than compressed input — size your output storage accordingly.                                                                                                         |
| **Licensing**   | A GUID suits servers that scale; an Activation Key suits a fixed machine. See [Licensing](/licensing/overview.md).                                                                                  |


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