mlserver-mlflow


Namemlserver-mlflow JSON
Version 1.6.1 PyPI version JSON
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home_pageNone
SummaryMLflow runtime for MLServer
upload_time2024-09-10 15:11:03
maintainerNone
docs_urlNone
authorSeldon Technologies Ltd.
requires_python<3.12,>=3.9
licenseApache-2.0
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
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            # 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.
```

            

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    "description": "# MLflow runtime for MLServer\n\nThis package provides a MLServer runtime compatible with [MLflow\nmodels](https://www.mlflow.org/docs/latest/models.html).\n\n## Usage\n\nYou can install the runtime, alongside `mlserver`, as:\n\n```bash\npip install mlserver mlserver-mlflow\n```\n\n## Content Types\n\nThe MLflow inference runtime introduces a new `dict` content type, which\ndecodes an incoming V2 request as a [dictionary of\ntensors](https://www.mlflow.org/docs/latest/models.html#deploy-mlflow-models).\nThis is useful for certain MLflow-serialised models, which will expect that the\nmodel inputs are serialised in this format.\n\n```{note}\nThe `dict` content type can be _stacked_ with other content types, like\n[`np`](../../docs/user-guide/content-type).\nThis allows the user to use a different set of content types to decode each of\nthe dict entries.\n```\n",
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