MLServer
An open source inference server for your machine learning models.
Overview
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 initial design document.
Usage
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
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:
| Framework | Supported | Documentation |
|---|---|---|
| Scikit-Learn | ✅ | MLServer SKLearn |
| XGBoost | ✅ | MLServer XGBoost |
| Spark MLlib | ✅ | MLServer MLlib |
| LightGBM | ✅ | MLServer LightGBM |
| CatBoost | ✅ | MLServer CatBoost |
| Tempo | ✅ | github.com/SeldonIO/tempo |
| MLflow | ✅ | MLServer MLflow |
| Alibi-Detect | ✅ | MLServer Alibi Detect |
| Alibi-Explain | ✅ | MLServer Alibi Explain |
| HuggingFace | ✅ | MLServer HuggingFace |
MLServer is licensed under the Apache License, Version 2.0. However please note that software used in conjunction with, or alongside, MLServer may be licensed under different terms. For example, Alibi Detect and Alibi Explain are both licensed under the Business Source License 1.1. For more information about the legal terms of products that are used in conjunction with or alongside MLServer, please refer to their respective documentation.
Supported Python Versions
🔴 Unsupported
🟠 Deprecated: To be removed in a future version
🟢 Supported
🔵 Untested
| Python Version | Status |
|---|---|
| 3.7 | 🔴 |
| 3.8 | 🔴 |
| 3.9 | 🟢 |
| 3.10 | 🟢 |
| 3.11 | 🟢 |
| 3.12 | 🟢 |
| 3.13 | 🔴 |
Examples
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-learnmodel - Serving a
xgboostmodel - Serving a
lightgbmmodel - Serving a
catboostmodel - Serving a
tempopipeline - Serving a custom model
- Serving an
alibi-detectmodel - Serving a
HuggingFacemodel - Multi-Model Serving with multiple frameworks
- Loading / unloading models from a model repository
Developer Guide
Versioning
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.
We generally keep the version as a placeholder for an upcoming version.
For example:
./hack/update-version.sh 0.2.0.dev1
Testing
To run all of the tests for MLServer and the runtimes, use:
make test
To run run tests for a single file, use something like:
tox -e py3 -- tests/batch_processing/test_rest.py
1 maintainer
Metadata
1 maintainer
Release files for mlserver 1.7.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlserver-1.7.1.tar.gz | 96.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlserver-1.7.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 226.3 kB
Release files / mlserver-1.7.1.tar.gz
| Download URL | mlserver-1.7.1.tar.gz |
|---|---|
| Size | 96.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e5534a1bda74275efbb86f8d58a649948f2f0703775619fe7234ab7e50c4fefd
|
|
BLAKE2b-256 checksum How to use checksums |
ca150a0cfaa6390a4222d7484938f08f0e283a2dac3d367dd1c02e45d193c4c6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.3 CPython/3.10.17 Linux/6.11.0-1015-azure
|
Release files / mlserver-1.7.1-py3-none-any.whl
| Download URL | mlserver-1.7.1-py3-none-any.whl |
|---|---|
| Size | 129.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d2ac7502915bb5311343a878aa1fb614f9b0f2436ee550a6137c1872a4dca193
|
|
BLAKE2b-256 checksum How to use checksums |
34441982c0ed416578ee09285ee7b22dbb1da87c4ae154edd39fe914984c549d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.3 CPython/3.10.17 Linux/6.11.0-1015-azure
|
