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RedisAI Quickstart

RedisAI 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 RedisAI.

Docker

The quickest way to try RedisAI 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 11.2 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. The Developer page has more information about the design and implementation of the RedisAI module and how to contribute.

Prerequisites

  • Packages: git, python3, make, wget, g++/clang, & unzip
  • CMake 3.0 or higher needs to be installed.
  • CUDA 11.2 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/RedisAI

Switch to the project's directory with:

cd RedisAI

Building the Dependencies

Use the following script to download and build the libraries of the various RedisAI 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 RedisAI module with:

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

Alternatively, run the following to build RedisAI with GPU support:

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

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/redisai.so