An open source inference server for your machine learning models.

MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.

• Multi-model serving, letting users run multiple models within the same process.
• Ability to run inference in parallel for vertical scaling across multiple models through a pool of inference workers.
• Support for adaptive batching, to group inference requests together on the fly.
• Scalability with deployment in Kubernetes native frameworks, including Seldon Core and KServe (formerly known as KFServing), where MLServer is the core Python inference server used to serve machine learning models.
• Support for the standard V2 Inference Protocol on both the gRPC and REST flavours, which has been standardised and adopted by various model serving frameworks.

You can read more about the goals of this project on the inital design document.

You can install the mlserver package running:

pip install mlserver

Note that to use any of the optional inference runtimes, you'll need to install the relevant package. For example, to serve a scikit-learn model, you would need to install the mlserver-sklearn package:

pip install mlserver-sklearn

For further information on how to use MLServer, you can check any of the available examples.

Inference runtimes allow you to define how your model should be used within MLServer. You can think of them as the backend glue between MLServer and your machine learning framework of choice. You can read more about inference runtimes in their documentation page.

Out of the box, MLServer comes with a set of pre-packaged runtimes which let you interact with a subset of common frameworks. This allows you to start serving models saved in these frameworks straight away. However, it's also possible to write custom runtimes.

Out of the box, MLServer provides support for:

To see MLServer in action, check out our full list of examples. You can find below a few selected examples showcasing how you can leverage MLServer to start serving your machine learning models.

• Serving a scikit-learn model
• Serving a xgboost model
• Serving a lightgbm model
• Serving a tempo pipeline
• Serving a custom model
• Serving an alibi-detect model
• Serving a HuggingFace model
• Multi-Model Serving with multiple frameworks
• Loading / unloading models from a model repository

Both the main mlserver package and the inference runtimes packages try to follow the same versioning schema. To bump the version across all of them, you can use the ./hack/update-version.sh script.

For example:

./hack/update-version.sh 0.2.0.dev1