> 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/docker-production.md).

# Docker Production Deployment

**The scenario:** A platform runs VIESUS-based enhancement as a containerized microservice on a GPU instance. It must restart automatically, update the license without rebuilding the image, and scale horizontally by adding more GPU instances.

***

## Recommended interface

{% hint style="info" %}
Docker isn't a separate interface — you containerize the interface you already use: the [**CLI**](/reference/cli-reference.md) for batch jobs, or the [**Node.js module**](/reference/node.js-module/overview.md) for a request-driven service.
{% endhint %}

This page covers the deployment concerns common to both. For the build details — Dockerfile, Compose, and run commands — see [Running the VIESUS CLI in Docker](/reference/docker/cli.md) and [Running the Node.js module in Docker](/reference/docker/nodejs.md).

***

## How it works

* A GPU-enabled image is built on an NVIDIA CUDA base, with the VIESUS packages and your chosen interface installed.
* The container is run with GPU access via the **NVIDIA Container Toolkit**, and the **GUID is supplied at runtime** (environment variable or secret) — never baked into the image.
* A health check lets your orchestrator know when the service is ready, and logs are emitted to stdout for aggregation.
* To scale, run **one container per GPU** and place a load balancer in front of them.

***

## What to consider

| Factor                    | Guidance                                                                                                                                        |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| **GPU access**            | Requires Docker plus the NVIDIA Container Toolkit on the host.                                                                                  |
| **GUID security**         | Inject the GUID at runtime via an environment variable or secret; rotating it is just a restart, no rebuild. Never bake it into an image layer. |
| **One GPU per container** | Run a single worker per GPU to avoid VRAM contention and out-of-memory errors.                                                                  |
| **Restart policy**        | Use auto-restart so the service recovers from rare crashes.                                                                                     |
| **Health checks**         | Allow a start-up grace period — initialization takes a few seconds — so a healthy container isn't killed prematurely.                           |
| **Image size**            | A multi-stage build keeps the runtime image small.                                                                                              |
| **Observability**         | Aggregate stdout logs and watch GPU utilisation and latency.                                                                                    |


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