This script shows how to use RedisAI to store and serve multiple models. It uses redisai-py==0.9.0 and ml2rt as external dependencies

multipleModelsinRedisAI.py

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# Script is using input resources from redisai-example repo #
# https://github.com/RedisAI/redisai-examples               #
#                                                           #
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from pathlib import Path
import multiprocessing
import redisai
import ml2rt
import numpy as np
import json
import string


con = redisai.Client()

# Setting up first model
modelpath1 = Path('redisai-examples/models/pytorch/charrnn/charrnn_pipeline.pt')
model1 = ml2rt.load_model(modelpath1)
con.modelset('model1', 'torch', 'cpu', model1)
hidden = np.zeros((2, 1, 300), dtype=np.float32)
con.tensorset('hidden', hidden)

# Setting up second model and script
modelpath2 = Path('redisai-examples/models/pytorch/imagenet/resnet50.pt')
scriptpath2 = 'redisai-examples/models/pytorch/imagenet/data_processing_script.txt'
class_idx = json.load(open("redisai-examples/data/imagenet_classes.json"))
model2 = ml2rt.load_model(modelpath2)
script2 = ml2rt.load_script(scriptpath2)
con.modelset('model2', 'torch', 'cpu', model2)
con.scriptset('script2', 'cpu', script2)

def first_model():
    con.tensorset('prime', [5], dtype='int64', shape=(1,))
    con.modelrun('model1', ['prime', 'hidden'], ['out'])
    out1 = con.tensorget('out')
    all_characters = string.printable
    outstr = ''.join([all_characters[i] for i in out1])
    print('==========================================')
    print(f"Done first model, Output string: {outstr}")
    print('==========================================')

def second_model_with_script():
    image = np.random.random((224, 224, 3)).astype(np.uint8)
    con.tensorset('image', image)
    con.scriptrun('script2', 'pre_process_3ch', 'image', 'temp1')
    con.modelrun('model2', 'temp1', 'temp2')
    con.scriptrun('script2', 'post_process', 'temp2', 'out')
    out2 = con.tensorget('out')
    print('==========================================')
    print(f"Done second model. Output class is: {class_idx[str(out2[0])]}")
    print('==========================================')


if __name__ == '__main__':
    p1 = multiprocessing.Process(target=first_model)
    p2 = multiprocessing.Process(target=second_model_with_script)
    p1.start()
    p2.start()