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  • MLServer
  • Getting Started
  • User Guide
    • Content Types (and Codecs)
    • OpenAPI Support
    • Parallel Inference
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      • Seldon Core
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  • Inference Runtimes
    • SKLearn
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    • MLFlow
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    • HuggingFace
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  • Examples
    • Serving Scikit-Learn models
    • Serving XGBoost models
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    • Serving MLflow models
    • Serving a custom model
    • Serving Alibi-Detect models
    • Serving HuggingFace Transformer Models
    • Multi-Model Serving
    • Model Repository API
    • Content Type Decoding
    • Custom Conda environments in MLServer
    • Serving a custom model with JSON serialization
    • Serving models through Kafka
    • Streaming
    • Deploying a Custom Tensorflow Model with MLServer and Seldon Core
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  1. Inference Runtimes

MLFlow

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Last updated 6 months ago

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This package provides a MLServer runtime compatible with .

Usage

You can install the runtime, alongside mlserver, as:

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 . This is useful for certain MLflow-serialised models, which will expect that the model inputs are serialised in this format.

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.
MLflow models
dictionary of tensors