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    redisai-js
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    1.0.0 • Public • Published

    license CircleCI npm version codecov Language grade: JavaScript Known Vulnerabilities

    A high performance node.js RedisAI client

    Forum Gitter

    You can use this client to query RedisAI using plain Javascript, or in a type-safe manner using Typescript, since redisai-js comes with its own type definitions built-in.

    Installation

    Installation is done using the npm install command:

    npm install --save redisai-js

    Overview

    Vanilla JS:

    Example of AI.TENSORSET and AI.TENSORGET

    var redis = require('redis');
    var redisai = require('redisai-js');
     
    (async () => {
        const nativeClient = redis.createClient();
        const aiclient = new redisai.Client(nativeClient);
        const tensorA = new redisai.Tensor(redisai.Dtype.float32, [1, 2], [3, 5]);
        const result = await aiclient.tensorset("tensorA", tensorA);
     
        // AI.TENSORSET result: OK
        console.log(`AI.TENSORSET result: ${result}`)
     
        const tensorGetReply = await aiclient.tensorget("tensorA");
     
        // AI.TENSORGET reply: datatype FLOAT shape [1,2] , data [3,5]
        console.log(`AI.TENSORGET reply: datatype ${tensorGetReply.dtype} shape [${tensorGetReply.shape}] , data [${tensorGetReply.data}]`);
     
        await aiclient.end();
    })();

    Example of AI.MODELSET and AI.MODELRUN

    var redis = require('redis');
    var redisai = require('redisai-js');
    var fs = require("fs");
     
    (async () => {
        const nativeClient = redis.createClient();
        const aiclient = new redisai.Client(nativeClient);
        const tensorA = new redisai.Tensor(redisai.Dtype.float32, [1, 2], [2, 3]);
        const tensorB = new redisai.Tensor(redisai.Dtype.float32, [1, 2], [3, 5]);
        const result_tA = await aiclient.tensorset("tA", tensorA);
        const result_tB = await aiclient.tensorset("tB", tensorB);
     
        const model_blob = fs.readFileSync("./test_data/graph.pb");
        // AI.TENSORSET tA result: OK
        console.log(`AI.TENSORSET tA result: ${result_tA}`)
        // AI.TENSORSET tB result: OK
        console.log(`AI.TENSORSET tB result: ${result_tB}`)
     
        const mymodel = new redisai.Model(redisai.Backend.TF, "CPU", ["a", "b"], ["c"], model_blob);
     
        const result_modelSet = await aiclient.modelset("mymodel", mymodel);
     
        // AI.MODELSET result: OK
        console.log(`AI.MODELSET result: ${result_modelSet}`)
     
        const result_modelRun = await aiclient.modelrun("mymodel", ["tA", "tB"], ["tC"]);
     
        console.log(`AI.MODELRUN result: ${result_modelRun}`)
        const tensorC = await aiclient.tensorget("tC");
     
        // AI.TENSORGET tC reply: datatype FLOAT shape [1,2] , data [6,15]
        console.log(`AI.TENSORGET tC reply: datatype ${tensorC.dtype} shape [${tensorC.shape}] , data [${tensorC.data}]`);
     
        await aiclient.end();
    })();

    Example of AI.SCRIPTSET and AI.SCRIPTRUN

    (async () => {
        const nativeClient = redis.createClient();
        const aiclient = new redisai.Client(nativeClient);
        const tensorA = new redisai.Tensor(redisai.Dtype.float32, [1, 2], [2, 3]);
        const tensorB = new redisai.Tensor(redisai.Dtype.float32, [1, 2], [3, 5]);
        const script_str = 'def bar(a, b):\n    return a + b\n';
     
        const result_tA = await aiclient.tensorset("tA", tensorA);
        const result_tB = await aiclient.tensorset("tB", tensorB);
        // AI.TENSORSET tA result: OK
        console.log(`AI.TENSORSET tA result: ${result_tA}`)
        // AI.TENSORSET tB result: OK
        console.log(`AI.TENSORSET tB result: ${result_tB}`)
     
        const myscript = new redisai.Script("CPU", script_str);
     
        const result_scriptSet = await aiclient.scriptset("myscript", myscript);
     
        // AI.SCRIPTSET result: OK
        console.log(`AI.SCRIPTSET result: ${result_scriptSet}`)
     
        const result_scriptRun = await aiclient.scriptrun("myscript", "bar",["tA", "tB"], ["tD"]);
     
        console.log(`AI.SCRIPTRUN result: ${result_scriptRun}`)
        const tensorD = await aiclient.tensorget("tD");
     
        // AI.TENSORGET tD reply: datatype FLOAT shape [1,2] , data [5,8]
        console.log(`AI.TENSORGET tD reply: datatype ${tensorD.dtype} shape [${tensorD.shape}] , data [${tensorD.data}]`);
     
        await aiclient.end();
    })();

    Example of AI.DAGRUN enqueuing multiple SCRIPTRUN and MODELRUN commands

    A common pattern is enqueuing multiple SCRIPTRUN and MODELRUN commands within a DAG. The following example uses ResNet-50,to classify images into 1000 object categories.

    Given that our input tensor contains each color represented as a 8-bit integer and that neural networks usually work with floating-point tensors as their input we need to cast a tensor to floating-point and normalize the values of the pixels - for that we will use pre_process_4ch function.

    To optimize the classification process we can use a post process script to return only the category position with the maximum classification - for that we will use post_process script.

    Using the DAG capabilities we've removed the necessity of storing the intermediate tensors in the keyspace. You can even run the entire process without storing the output tensor, as follows:

    var redis = require('redis');
    var redisai = require('redisai-js');
    var fs = require("fs");
     
    (async () => {
        const nativeClient = redis.createClient();
        const aiclient = new redisai.Client(nativeClient);
        const scriptFileStr = fs.readFileSync('./tests/test_data/imagenet/data_processing_script.txt').toString();
        const jsonLabels = fs.readFileSync('./tests/test_data/imagenet/imagenet_class_index.json');
        const labels = JSON.parse(jsonLabels);
     
        const dataProcessingScript = new redisai.Script('CPU', scriptFileStr);
        const resultScriptSet = await aiclient.scriptset('data_processing_script', dataProcessingScript);
        // AI.SCRIPTSET result: OK
        console.log(`AI.SCRIPTSET result: ${resultScriptSet}`)
     
        const modelBlob = fs.readFileSync('./tests/test_data/imagenet/resnet50.pb');
        const imagenetModel = new redisai.Model(Backend.TF, 'CPU', ['images'], ['output'], modelBlob);
        const resultModelSet = await aiclient.modelset('imagenet_model', imagenetModel);
        
        // AI.MODELSET result: OK
        console.log(`AI.MODELSET result: ${resultModelSet}`)
     
        const inputImage = await Jimp.read('./tests/test_data/imagenet/cat.jpg');
        const imageWidth = 224;
        const imageHeight = 224;
        const image = inputImage.cover(imageWidth, imageHeight);
        const tensor = new redisai.Tensor(Dtype.uint8, [imageWidth, imageHeight, 4], Buffer.from(image.bitmap.data));
        
        ///
        // Prepare the DAG enqueuing multiple SCRIPTRUN and MODELRUN commands
        const dag = new redisai.Dag();
        
        dag.tensorset('tensor-image', tensor);
        dag.scriptrun('data_processing_script', 'pre_process_4ch', ['tensor-image'], ['temp_key1']);
        dag.modelrun('imagenet_model', ['temp_key1'], ['temp_key2']);
        dag.scriptrun('data_processing_script', 'post_process', ['temp_key2'], ['classification']);
        dag.tensorget('classification');
     
        // Send the AI.DAGRUN command to RedisAI server
        const resultDagRun = await aiclient.dagrun_ro(null, dag);
        
        // The 5th element of the reply will be the `classification` tensor 
        const classTensor = resultDagRun[4];
        
        // Print the category in the position with the max classification
        const idx = classTensor.data[0];
     
        // 281 [ 'n02123045', 'tabby' ]
        console.log(idx, labels[idx.toString()]);
     
        await aiclient.end();
    })();

    Further examples

    The RedisAI examples repo shows more advanced examples made using redisai-js under js_client folder.

    Supported RedisAI Commands

    Command Recommended API and JSDoc
    AI.TENSORSET tensorset
    AI.TENSORGET tensorget
    AI.MODELSET modelset
    AI.MODELGET modelget
    AI.MODELDEL modeldet
    AI.MODELRUN modelrun
    AI._MODELSCAN N/A
    AI.SCRIPTSET scriptset
    AI.SCRIPTGET scriptget
    AI.SCRIPTDEL scriptdel
    AI.SCRIPTRUN scriptrun
    AI._SCRIPTSCAN N/A
    AI.DAGRUN dagrun
    AI.DAGRUN_RO dagrun_ro
    AI.INFO info and infoResetStat (for resetting stats)
    AI.CONFIG * configLoadBackend and configBackendsPath

    Running tests

    A simple test suite is provided, and can be run with:

    $ npm test

    The tests expect a Redis server with the RedisAI module loaded to be available at localhost:6379

    License

    redisai-js is distributed under the BSD3 license - see LICENSE

    Install

    npm i redisai-js

    DownloadsWeekly Downloads

    16

    Version

    1.0.0

    License

    BSD-3-Clause

    Unpacked Size

    308 kB

    Total Files

    108

    Last publish

    Collaborators

    • gkorland
    • fcosta_oliveira
    • chayimischayim