Caution

Redis-inference-optimization is no longer actively maintained or supported.

We are grateful to the redis-inference-optimization community for their interest and support. Previously, redis-inference-optimization was named RedisAI, but was renamed in Jan 2025 to reduce confusion around Redis' other AI offerings. To learn more about Redis' current AI offerings, visit the Redis website.

Redis-inference-optimization

Redis-inference-optimization is a Redis module for executing Deep Learning/Machine Learning models and managing their data. Its purpose is being a "workhorse" for model serving, by providing out-of-the-box support for popular DL/ML frameworks and unparalleled performance. Redis-inference-optimization both maximizes computation throughput and reduces latency by adhering to the principle of data locality, as well as simplifies the deployment and serving of graphs by leveraging on Redis' production-proven infrastructure.

Quickstart

Redis-inference-optimization is a Redis module. To run it you'll need a Redis server (v6.0.0 or greater), the module's shared library, and its dependencies.

The following sections describe how to get started with redis-inference-optimization.

Docker

The quickest way to try redis-inference-optimization is by launching its official Docker container images.

On a CPU only machine

docker run -p 6379:6379 redislabs/redisai:1.2.7-cpu-bionic

On a GPU machine

For GPU support you will need a machine you'll need a machine that has Nvidia driver (CUDA 11.3 and cuDNN 8.1), nvidia-container-toolkit and Docker 19.03+ installed. For detailed information, checkout nvidia-docker documentation

docker run -p 6379:6379 --gpus all -it --rm redislabs/redisai:1.2.7-gpu-bionic

Building

You can compile and build the module from its source code.

Prerequisites

• Packages: git, python3, make, wget, g++/clang, & unzip
• CMake 3.0 or higher needs to be installed.
• CUDA 11.3 and cuDNN 8.1 or higher needs to be installed if GPU support is required.
• Redis v6.0.0 or greater.

Get the Source Code

You can obtain the module's source code by cloning the project's repository using git like so:

git clone --recursive https://github.com/RedisAI/redis-inference-optimization

Switch to the project's directory with:

cd redis-inference-optimization

Building the Dependencies

Use the following script to download and build the libraries of the various redis-inference-optimization backends (TensorFlow, PyTorch, ONNXRuntime) for CPU only:

bash get_deps.sh

Alternatively, you can run the following to fetch the backends with GPU support.

bash get_deps.sh gpu

Building the Module

Once the dependencies have been built, you can build the redis-inference-optimization module with:

make -C opt clean ALL=1
make -C opt

Alternatively, run the following to build redis-inference-optimization with GPU support:

make -C opt clean ALL=1
make -C opt GPU=1

Backend Dependancy

Redis-inference-optimization currently supports PyTorch (libtorch), Tensorflow (libtensorflow), TensorFlow Lite, and ONNXRuntime as backends. This section shows the version map between redis-inference-optimization and supported backends. This extremely important since the serialization mechanism of one version might not match with another. For making sure your model will work with a given redis-inference-optimization version, check with the backend documentation about incompatible features between the version of your backend and the version redis-inference-optimization is built with.

redis-inference-optimization	PyTorch	TensorFlow	TFLite	ONNXRuntime
1.0.3	1.5.0	1.15.0	2.0.0	1.2.0
1.2.7	1.11.0	2.8.0	2.0.0	1.11.1
master	1.11.0	2.8.0	2.0.0	1.11.1

Note: Keras and TensorFlow 2.x are supported through graph freezing.

Loading the Module

To load the module upon starting the Redis server, simply use the --loadmodule command line switch, the loadmodule configuration directive or the Redis MODULE LOAD command with the path to module's library.

For example, to load the module from the project's path with a server command line switch use the following:

redis-server --loadmodule ./install-cpu/redis-inference-optimization.so

Give it a try

Once loaded, you can interact with redis-inference-optimization using redis-cli.

Client libraries

Some languages already have client libraries that provide support for redis-inference-optimization's commands. The following table lists the known ones:

Project	Language	License	Author	URL
JredisAI	Java	BSD-3	RedisLabs	Github
redisAI-py	Python	BSD-3	RedisLabs	Github
redisAI-go	Go	BSD-3	RedisLabs	Github
redisAI-js	Typescript/Javascript	BSD-3	RedisLabs	Github
redis-modules-sdk	TypeScript	BSD-3-Clause	Dani Tseitlin	Github
redis-modules-java	Java	Apache-2.0	dengliming	Github
smartredis	C++	BSD-2-Clause	Cray Labs	Github
smartredis	C	BSD-2-Clause	Cray Labs	Github
smartredis	Fortran	BSD-2-Clause	Cray Labs	Github
smartredis	Python	BSD-2-Clause	Cray Labs	Github

License

Redis-inference-optimization is licensed under your choice of the Redis Source Available License 2.0 (RSALv2) or the Server Side Public License v1 (SSPLv1).