> ## 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.

# backbones

Backbone feature-extractor models (H1, M-Optimus, tissue-seg).

Model specifications are loaded from YAML config files in the `configs/` directory. The `Backbone` factory wires them together with a pluggable client so that a single `Backbone(...)` call works for any backend (HTTP, AWS, GCP, …).

**Example:**

```python theme={null}
backbone = Backbone("h1", base_url="http://localhost:8080")
backbone = Backbone("h1", backend="aws", endpoint_name="prod")
```

## Backbone

```python theme={null}
class Backbone()
```

Factory for creating backbone endpoint models.

Model identity (spec, endpoint path) is read from YAML config files. The factory pairs each config with a pluggable client backend to produce a ready-to-use `EndpointModel`.

**Usage:**

```pycon theme={null}
>>> Backbone("h1", base_url="http://localhost:8080")
>>> Backbone("h1", backend="aws", endpoint_name="ep")
>>> Backbone.available_backbones()
['h1', 'm-optimus', 'tissue-seg']
```

## Backend

```python theme={null}
class Backend(str, Enum)
```

Supported inference backends.

#### available\_backbones

```python theme={null}
@classmethod
def available_backbones(cls) -> List[str]
```

Return list of all registered backbone names.