LogoLogo
  • MLServer
  • Getting Started
  • User Guide
    • Content Types (and Codecs)
    • OpenAPI Support
    • Parallel Inference
    • Adaptive Batching
    • Custom Inference Runtimes
    • Metrics
    • Deployment
      • Seldon Core
      • KServe
    • Streaming
  • Inference Runtimes
    • SKLearn
    • XGBoost
    • MLFlow
    • Spark MlLib
    • LightGBM
    • Catboost
    • Alibi-Detect
    • Alibi-Explain
    • HuggingFace
    • Custom
  • Reference
    • MLServer Settings
    • Model Settings
    • MLServer CLI
    • Python API
      • MLModel
      • Types
      • Codecs
      • Metrics
  • Examples
    • Serving Scikit-Learn models
    • Serving XGBoost models
    • Serving LightGBM models
    • 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
  • Changelog
Powered by GitBook
On this page

Was this helpful?

Edit on GitHub
Export as PDF
  1. Reference
  2. Python API

MLModel

The MLModel class is the base class for all custom inference runtimes. It exposes the main interface that MLServer will use to interact with ML models.

The bulk of its public interface are the {func}load() <mlserver.MLModel.load>, {func}unload() <mlserver.MLModel.unload> and {func}predict() <mlserver.MLModel.predict> methods. However, it also contains helpers with encoding / decoding of requests and responses, as well as properties to access the most common bits of the model's metadata.

When writing custom runtimes, this class should be extended to implement your own load and predict logic.

.. autoclass:: mlserver.MLModel
   :members:
   :member-order: bysource
PreviousPython APINextTypes

Last updated 5 months ago

Was this helpful?