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RedisAI¶

RedisAI is a Redis module for serving tensors and executing deep learning models.

Quickstart¶

Docker
Build

Docker¶

To quickly tryout RedisAI, launch an instance using docker:

docker run -p 6379:6379 -it --rm redisai/redisai

Give it a try¶

On the client, load the model

redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < examples/models/graph.pb

Then create the input tensors, run the computation graph and get the output tensor (see load_model.sh). Note the signatures: AI.TENSORSET tensor_key data_type dim1..dimN [BLOB data | VALUES val1..valN] AI.MODELRUN graph_key INPUTS input_key1 ... OUTPUTS output_key1 ...

redis-cli
> AI.TENSORSET bar FLOAT 2 VALUES 2 3
> AI.TENSORSET baz FLOAT 2 VALUES 2 3
> AI.MODELRUN foo INPUTS bar baz OUTPUTS jez
> AI.TENSORGET jez VALUES
1) FLOAT
2) 1) (integer) 2
3) 1) "4"
   2) "9"

Building¶

This will checkout and build and download the libraries for the backends (TensorFlow and PyTorch) for your platform.

bash get_deps.sh

After the dependencies are downloaded, build the module itself. Note that CMake 3.0 or higher is required.

mkdir build
cd build
cmake -DDEPS_PATH=../deps/install ..
make
cd ..

Start¶

You must have a redis-server version 4.0.9 or greater, available in most recent distributions:

redis-server --version
Redis server v=4.0.9 sha=00000000:0 malloc=libc bits=64 build=c49f4faf7c3c647a

To start redis with the RedisAI module loaded, you need to make sure the dependencies can be found by redis. One example of how to do this on Linux is:

LD_LIBRARY_PATH=<PATH_TO>/deps/install/lib redis-server --loadmodule build/redisai.so

Client libraries¶

Some languages have client libraries that provide support for RedisAI's commands:

Project	Language	License	Author	URL
JRedisAI	Java	BSD-3	RedisLabs	Github
redisai-py	Python	BSD-3	RedisLabs	Github

Full documentation of the api can be found here.