semvec/pypi-packages huggingface dataset:
mlserver-mlflow 1.7.2rc1
MLflow runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# MLflow runtime for MLServer

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

## Usage

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

```bash
pip install mlserver mlserver-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.
```
mlserver-catboost 1.7.2rc1
Catboost runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# CatBoost runtime for MLServer

This package provides a MLServer runtime compatible with CatBoost's `CatboostClassifier`.

## Usage

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

```bash
pip install mlserver mlserver-catboost
```

For further information on how to use MLServer with CatBoost, you can check out
this [worked out example](../../docs/examples/catboost/README.md).

## Content Types

If no [content type](../../docs/user-guide/content-type) is present on the
request or metadata, the CatBoost 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).
mlserver-sklearn 1.7.2rc1
Scikit-Learn runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# Scikit-Learn runtime for MLServer

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

## Usage

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

```bash
pip install mlserver mlserver-sklearn
```

For further information on how to use MLServer 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
<mlserver.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" }
  ]
}
```
mlserver-mllib 1.7.2rc1
Spark MLlib runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# Spark MLlib runtime for MLServer

This package provides a MLServer runtime compatible with Spark MLlib.

## Usage

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

```bash
pip install mlserver mlserver-mllib
```

For further information on how to use MLServer with Spark MLlib, you can check
out the [MLServer repository](https://github.com/SeldonIO/MLServer).
mlserver-lightgbm 1.7.2rc1
LightGBM runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# LightGBM runtime for MLServer

This package provides a MLServer runtime compatible with LightGBM.

## Usage

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

```bash
pip install mlserver mlserver-lightgbm
```

For further information on how to use MLServer with LightGBM, you can check out
this [worked out example](../../docs/examples/lightgbm/README.md).

## Content Types

If no [content type](../../docs/user-guide/content-type) is present on the
request or metadata, the LightGBM 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).
mlserver-huggingface 1.7.2rc1
HuggingFace runtime for MLServer
License: Apache-2.0
Requires Python: <3.13,>=3.9

# HuggingFace runtime for MLServer

This package provides a MLServer runtime compatible with HuggingFace Transformers.

## Usage

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

```bash
pip install mlserver mlserver-huggingface
```

For further information on how to use MLServer with HuggingFace, you can check
out this [worked out example](../../docs/examples/huggingface/README.md).

## Content Types

The HuggingFace runtime will always decode the input request using its own
built-in codec.
Therefore, [content type annotations](../../docs/user-guide/content-type) at
the request level will **be ignored**.
Note that this **doesn't include [input-level content
type](../../docs/user-guide/content-type#Codecs) annotations**, which will be
respected as usual.

## Settings

The HuggingFace runtime exposes a couple extra parameters 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: 5-8
---
{
  "name": "qa",
  "implementation": "mlserver_huggingface.HuggingFaceRuntime",
  "parameters": {
    "extra": {
      "task": "question-answering",
      "optimum_model": true
    }
  }
}
```

````{note}
These settings can also be injected through environment variables prefixed with `MLSERVER_MODEL_HUGGINGFACE_`, e.g.

```bash
MLSERVER_MODEL_HUGGINGFACE_TASK="question-answering"
MLSERVER_MODEL_HUGGINGFACE_OPTIMUM_MODEL=true
```
````

### Loading models
#### Local models
It is possible to load a local model into a HuggingFace pipeline by specifying the model artefact folder path in `parameters.uri` in `model-settings.json`.

#### HuggingFace models
Models in the HuggingFace hub can be loaded by specifying their name in `parameters.extra.pretrained_model` in `model-settings.json`.

````{note}
If `parameters.extra.pretrained_model` is specified, it takes precedence over `parameters.uri`.
````

### Reference

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

```{eval-rst}

.. autopydantic_settings:: mlserver_huggingface.settings.HuggingFaceSettings
```
lightningcss 0.3.1
Python bindings for lightningcss.
License: Apache-2.0
Requires Python: >=3.7
Keywords: css, minify

[![PyPI - Project Version](https://img.shields.io/pypi/v/lightningcss)](https://pypi.org/project/lightningcss)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightningcss)](https://pypi.org/project/lightningcss)
[![GitHub - Project License](https://img.shields.io/github/license/pydsigner/python-lightningcss)](https://github.com/pydsigner/python-lightningcss)

# python-lightningcss

python-lightningcss offers PyO3 bindings to the lightningcss library. Presently
only a subset of functionality is exposed, intended for integration into a
Python-centric web application rather than a JavaScript-based workflow. If the
latter is needed, consider using the [official npm lightningcss module, parcel,
or webpack](https://lightningcss.dev/docs.html) instead.

## Installation

python-lightningcss includes wheels which can be installed for most platforms
using pip:

    $ pip install lightningcss

Alternatively, python-lightningcss may be installed directly from source:

    $ pip install git+https://github.com/pydsigner/python-lightningcss

This will require that the Rust toolchain be installed.

## Usage

```py
import lightningcss

parser_flags = lightningcss.calc_parser_flags(nesting=True)

input_css = """
.navbar {
    margin: 0;

    a {
        padding-left: 0;
        padding-right: 0;
        padding-top: 5px;
        padding-bottom: 5px;
    }
}
"""

output_css = lightningcss.process_stylesheet(
    input_css,
    filename="abc.css",
    parser_flags = parser_flags,
    browsers_list = ['defaults'],
    minify = True,
)
```

This package also supports creating a CSS bundle where all `@import` rules are
resolved into a single stylesheet:

```py
bundled_css = lightningcss.bundle_css(
    "main.css",
    filename = "main.css",
    browsers_list = ["defaults"],
    minify = False,
)
```

All resources referenced via `@import` are resolved relative to the main file. The
`filename` keyword parameter is only used for displaying error messages from the
parser. When using `browsers_list`, lightningcss will try to transpile the code
to be compatible with the specified [browserslist target](https://browsersl.ist/).