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A high-performance JavaScript client for RedisAI

Home Page: https://redisai.io

License: BSD 3-Clause "New" or "Revised" License

TypeScript 96.60% Python 3.40%
redisai redis javascript machine-learning serving-tensors

redisai-js's Issues

Support for variadic arguments on SCRIPTRUN

Further reference:
https://oss.redislabs.com/redisai/master/commands/#aiscriptrun

Example on how provide an arbitrary number of inputs after the $ sign::

redis> AI.TENSORSET mytensor1 FLOAT 1 VALUES 40
OK
redis> AI.TENSORSET mytensor2 FLOAT 1 VALUES 1
OK
redis> AI.TENSORSET mytensor3 FLOAT 1 VALUES 1
OK
redis> AI.SCRIPTRUN myscript addn INPUTS mytensor1 $ mytensor2 mytensor3 OUTPUTS result
OK
redis> AI.TENSORGET result VALUES
1) FLOAT
2) 1) (integer) 1
3) 1) "42"

Gettin a basic example going in TypeScript

I have been using redisai-py for a while. Now I am starting to integrate use of redisai into my website with redisai-js.

I am using typescript and ts-node in development. Below is the code I added following the example and the error I received.

import redis from "redis";
import redisai from "redisai-js";

(async () => {
    const nativeClient = redis.createClient({ url: "redis://redis:6379" });
    const rai = new redisai.Client(nativeClient );
})();

Error: Cannot find module './backend'
Require stack:
- /app/node_modules/redisai-js/lib/index.js
- /app/src/index.ts
at Function.Module._resolveFilename (internal/modules/cjs/loader.js:880:15)
at Function.Module._load (internal/modules/cjs/loader.js:725:27)
at Module.require (internal/modules/cjs/loader.js:952:19)
at require (internal/modules/cjs/helpers.js:88:18)
at Object.<anonymous> (/app/node_modules/redisai-js/src/index.ts:2:1)
at Module._compile (internal/modules/cjs/loader.js:1063:30)
at Module._extensions..js (internal/modules/cjs/loader.js:1092:10)
at Object.require.extensions.<computed> [as .js] (/app/node_modules/ts-node/src/index.ts:851:44)
at Module.load (internal/modules/cjs/loader.js:928:32)
at Function.Module._load (internal/modules/cjs/loader.js:769:14)
[nodemon] app crashed - waiting for file changes before starting...

Syntax Error upon import

I'm getting a syntax error doing the simplest of things:

$ npm install --save redisai-js

I copy and paste the Vanilla JS tensor example from the README.md into a file named redisai-js.js. And then I run it:

$ node redisai-js.js

And it gives me this error:

/Users/guyroyse/code/redis-modules-assessment/node_modules/redisai-js/lib/tensor.js:49
    });
    ^

SyntaxError: Unexpected token '}'
    at wrapSafe (internal/modules/cjs/loader.js:931:16)
    at Module._compile (internal/modules/cjs/loader.js:979:27)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1035:10)
    at Module.load (internal/modules/cjs/loader.js:879:32)
    at Function.Module._load (internal/modules/cjs/loader.js:724:14)
    at Module.require (internal/modules/cjs/loader.js:903:19)
    at require (internal/modules/cjs/helpers.js:74:18)
    at Object.<anonymous> (/Users/guyroyse/code/redis-modules-assessment/node_modules/redisai-js/lib/index.js:10:16)
    at Module._compile (internal/modules/cjs/loader.js:1015:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1035:10)

I pulled the repo down myself, built everything, and npm installed the folder on my machine and it worked. Seems like something wrong with our npm depoly? Maybe?

Use TF model object as parameter in new redisai.Model

As a Node.js developer i'm using the redisai-js module to connect with Redis-Ai with my node.js application. So i'm using / save / load my models in TensorflowJs format (model.json, weights.bin) so its useful create the Redis-Ai model getting start from these two files saved. Actually the code i'm used doesn't work because the model format:

    const m = await tf.loadLayersModel(`file://model/AX-model/model.json`)
    const myModel = new redisai.Model(redisai.Backend.TF, 'CPU',['a','b'], ['c'], m)
    const r = await aiclient.modelset('mlmodel', myModel)

So i'm interesting to thinking at a solution, in node.js application i'm struggle to use Redis to save the TensorflowJs models too directly in-memory db. I'd like to chose which operation get done in Node.js application and which in Redis-Ai, so for example train the model in Node.js application and predict in Redis-Ai:

     const model = tf.sequential()
     model.add(tf.layers.dense({inputShape: [1], units: 1}))
     model.add(tf.layers.dense({units: 1}))
    model.compile({
           optimizer: 'sgd',   // tf.train.adam(),
           // loss: tf.losses.meanSquaredError,
           loss: 'meanSquaredError',
           metrics: ['mse']
      })

       const myModel = new redisai.Model(redisai.Backend.TF, 'CPU',['a','b'], ['c'], model)
       const r = await aiclient.modelset('mlmodel', myModel)

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