Archived in favor of https://github.com/jlarmstrongiv/tfjs-node-lambda
First, install the package:
yarn add tensorflow-lambda
You can then use it like this:
const loadTf = require('tensorflow-lambda')
const tf = await loadTf()
// you get the same `tf` object that would get if you were doing:
// const tf = require('@tensorflow/tfjs')
tf.tensor([1, 2, 3, 4]).print()Have a look at these examples :
- object-detection (deployed with Vercel)
When not used in a lambda environment (for example, locally on your computer when you're developing), tensorflow-lambda will require @tensorflow/tfjs-node instead of deflating a pre-compiled version in /tmp.
Therefore, you need to install @tensorflow/tfjs-node to use this package locally:
yarn add @tensorflow/tfjs-node --dev
You can then use the package the same way you would use it in a lambda environment locally.
Have a look at these lines to understand how it detects if it runs in a lambda environement.
The package contains a zipped and compressed version of all the dependencies and binaries needed to run @tensorflow/tfjs-node on AWS Lambda (these dependencies are built with Github Actions).
During cold start, the files are deflated in /tmp and required in your node program.
@tensorflow/tfjs works with AWS Lambda but the main problem is that it is slow very slow when used in node. On the other hand, @tensorflow/tfjs-node is fast when used with node but it is >140mo and it does not fit under AWS Lambda's size limit (50mo) and it needs to be pre-compiled for lambda for it to work in a lambda environment.
I was looking for an easy way to use tensorflowjs with lambda and I couldn't find any, so I made this package.