semvec/pypi-packages huggingface dataset:
kozmoserver-supervisor-explain 0.1.0.dev1
Supervisor-Explain runtime for KozmoServer
License: Apache-2.0
Requires Python: <3.12,>=3.9

# Supervisor-Explain runtime for KozmoServer

This package provides a KozmoServer runtime compatible with Supervisor-Explain.

## Usage

You can install the runtime, alongside `kozmoserver`, as:

```bash
pip install kozmoserver kozmoserver-supervisor-explain
```
kozmoserver-supervisor-detect 0.1.0.dev1
Supervisor-Detect runtime for KozmoServer
License: Apache-2.0
Requires Python: <3.12,>=3.9

# Supervisor-Detect runtime for KozmoServer

This package provides a KozmoServer runtime compatible with
[supervisor-detect](https://docs.kozmoai.io/projects/supervisor-detect/en/latest/index.html)
models.

## Usage

You can install the `kozmoserver-supervisor-detect` runtime, alongside `kozmoserver`, as:

```bash
pip install kozmoserver kozmoserver-supervisor-detect
```

For further information on how to use KozmoServer with Supervisor-Detect, you can check
out this [worked out example](../../docs/examples/supervisor-detect/README.md).

## Content Types

If no [content type](../../docs/user-guide/content-type) is present on the
request or metadata, the Supervisor-Detect runtime will try to decode the payload
as a [NumPy Array](../../docs/user-guide/content-type).
To avoid this, either send a different content type explicitly, or define the
correct one as part of your [model's
metadata](../../docs/reference/model-settings).

## Settings

The Supervisor Detect runtime exposes a couple setting flags which can be used to
customise how the runtime behaves.
These settings can be added under the `parameters.extra` section of your
`model-settings.json` file, e.g.

```{code-block} json
---
emphasize-lines: 6-8
---
{
  "name": "drift-detector",
  "implementation": "kozmoserver_supervisor_detect.SupervisorDetectRuntime",
  "parameters": {
    "uri": "./supervisor-detect-artifact/",
    "extra": {
      "batch_size": 5
    }
  }
}
```

### Reference

You can find the full reference of the accepted extra settings for the Supervisor
Detect runtime below:

```{eval-rst}

.. autopydantic_settings:: kozmoserver_supervisor_detect.runtime.SupervisorDetectSettings
```
kozmoserver-sklearn 0.1.0.dev1
Scikit-Learn runtime for KozmoServer
License: Apache-2.0
Requires Python: <3.12,>=3.9

# Scikit-Learn runtime for KozmoServer

This package provides a KozmoServer runtime compatible with Scikit-Learn.

## Usage

You can install the runtime, alongside `kozmoserver`, as:

```bash
pip install kozmoserver kozmoserver-sklearn
```

For further information on how to use KozmoServer with Scikit-Learn, you can check
out this [worked out example](../../docs/examples/sklearn/README.md).

## Content Types

If no [content type](../../docs/user-guide/content-type) is present on the
request or metadata, the Scikit-Learn runtime will try to decode the payload as
a [NumPy Array](../../docs/user-guide/content-type).
To avoid this, either send a different content type explicitly, or define the
correct one as part of your [model's
metadata](../../docs/reference/model-settings).

## Model Outputs

The Scikit-Learn inference runtime exposes a number of outputs depending on the
model type.
These outputs match to the `predict`, `predict_proba` and `transform` methods
of the Scikit-Learn model.

| Output          | Returned By Default | Availability                                                                                                         |
| --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `predict`       | ✅                  | Available on most models, but not in [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). |
| `predict_proba` | ❌                  | Only available on non-regressor models.                                                                              |
| `transform`     | ❌                  | Only available on [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html).                     |

By default, the runtime will only return the output of `predict`.
However, you are able to control which outputs you want back through the
`outputs` field of your {class}`InferenceRequest
<kozmoserver.types.InferenceRequest>` payload.

For example, to only return the model's `predict_proba` output, you could
define a payload such as:

```{code-block} json
---
emphasize-lines: 10-12
---
{
  "inputs": [
    {
      "name": "my-input",
      "datatype": "INT32",
      "shape": [2, 2],
      "data": [1, 2, 3, 4]
    }
  ],
  "outputs": [
    { "name": "predict_proba" }
  ]
}
```
kozmoserver-mllib 0.1.0.dev1
Spark MLlib runtime for KozmoServer
License: Apache-2.0
Requires Python: <3.12,>=3.9

# Spark MLlib runtime for KozmoServer

This package provides a KozmoServer runtime compatible with Spark MLlib.

## Usage

You can install the runtime, alongside `kozmoserver`, as:

```bash
pip install kozmoserver kozmoserver-mllib
```

For further information on how to use KozmoServer with Spark MLlib, you can check
out the [KozmoServer repository](https://github.com/kozmoai/kozmoserver).
kozmoserver-mlflow 0.1.0.dev1
MLflow runtime for KozmoServer
License: Apache-2.0
Requires Python: <3.12,>=3.9

# MLflow runtime for KozmoServer

This package provides a KozmoServer runtime compatible with [MLflow
models](https://www.mlflow.org/docs/latest/models.html).

## Usage

You can install the runtime, alongside `kozmoserver`, as:

```bash
pip install kozmoserver kozmoserver-mlflow
```

## Content Types

The MLflow inference runtime introduces a new `dict` content type, which
decodes an incoming V2 request as a [dictionary of
tensors](https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models).
This is useful for certain MLflow-serialised models, which will expect that the
model inputs are serialised in this format.

```{note}
The `dict` content type can be _stacked_ with other content types, like
[`np`](../../docs/user-guide/content-type).
This allows the user to use a different set of content types to decode each of
the dict entries.
```