> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bioptimus.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Validation & acceptance testing

> Verify a deployment produces correct, expected outputs.

Use these checks after deploying ([on-premise](/deployment/platforms/on-premise) or [SageMaker](/deployment/platforms/aws-sagemaker)) to confirm the service is healthy and producing expected results.

## 1. Service health

<CodeGroup>
  ```bash H-Optimus package theme={null}
  curl -s http://localhost:8080/ping | python3 -m json.tool
  # {"status": "ok", "models": ["h1", "tissue-seg"]}
  ```

  ```bash M-Optimus package theme={null}
  curl -s http://localhost:8080/ping | python3 -m json.tool
  # {"status": "ok", "models": ["m-optimus", "tissue-seg"]}
  ```
</CodeGroup>

## 2. Model availability (SDK)

`available_backbones()` lists the models the Bioptimus SDK can construct (read from its bundled configs) — it does not query the server. The `/ping` response in step 1 is the source of truth for what the deployed endpoint actually serves.

```python theme={null}
from bioptimus.models.backbones import Backbone
print(Backbone.available_backbones())
# ['h1', 'm-optimus', 'tissue-seg']
```

## 3. Output shape checks

| Model                       | Expected output length                                 |
| --------------------------- | ------------------------------------------------------ |
| H-Optimus (`/api/embed/h1`) | 1536                                                   |
| M-Optimus embedding         | 1536                                                   |
| M-Optimus prediction        | number of output genes (see `/api/metadata/m-optimus`) |
| Tissue segmentation         | H×W (262,144 for a 512×512 tile)                       |