TensorFlow.js is a powerful tool for machine learning in JavaScript. It can be used in Node.js applications on cloud providers such as AWS, Azure, and Google Cloud Platform.
In this post, we'll cover how to set up a Node.js application on a cloud provider and use TensorFlow.js to train a machine learning model. We'll also deploy the trained model to the cloud provider so that it can be used in a web application.
First, we'll need to set up a Node.js application on a cloud provider. We'll use the Express web framework to set up a basic web application.
If you're not familiar with Express, you can check out the getting started guide.
Once you have Express installed, we can create a file called app.js
with the following code:
const express = require('express');
const app = express();
app.get('/', (req, res) => {
res.send('Hello, world!');
});
app.listen(3000, () => {
console.log('Example app listening on port 3000!');
});
This code creates a basic Express application that responds to HTTP requests with a "Hello, world!" message.
Next, we'll need to deploy our Node.js application to a cloud provider. We'll use AWS in this example, but the process is similar for other providers.
First, create an AWS account and create a new IAM user with access to EC2. For more information on how to do this, see the AWS documentation.
Once you have an IAM user set up, you can create a new EC2 instance. For this example, we'll use a t2.micro instance.
Once the instance is running, you can SSH into it and install the dependencies for our Node.js application:
$ sudo npm install -g express
$ sudo npm install -g ejs
Next, we'll need to copy our app.js
file to the EC2 instance. We can do this using the scp command:
$ scp -i <path-to-pem-file> app.js ec2-user@<ec2-instance-ip>:~/
Once the file has been copied, we can start our Node.js application:
$ node app.js
Our Node.js application is now running on our EC2 instance!
Now that we have our Node.js application up and running, we can start using TensorFlow.js.
First, we'll need to install the TensorFlow.js Node.js package:
$ npm install @tensorflow/tfjs-node
Next, we'll create a file called train.js
with the following code:
const tf = require('@tensorflow/tfjs-node');
// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
// Train the model using the data.
model.fit(xs, ys, {epochs: 10}).then(() => {
// Use the model to do inference on a data point the model hasn't seen before:
model.predict(tf.tensor2d([5], [1, 1])).print();
});
This code defines a simple linear regression model using TensorFlow.js. We then train the model using some synthetic data. Finally, we use the model to make predictions on new data points.
To run this code, we can use the node
command:
$ node train.js
Once we have a trained TensorFlow.js model, we can deploy it to a cloud provider so that it can be used in a web application.
We'll use AWS again in this example, but the process is similar for other providers.
First, we'll need to create an Amazon S3 bucket to store our model files. For more information on how to do this, see the AWS documentation.
Next, we'll need to install the AWS CLI. For more information on how to do this, see the AWS documentation.
Once the AWS CLI is installed, we can use it to sync our local model files to our S3 bucket:
$ aws s3 sync . s3://<s3-bucket-name>
Our TensorFlow.js model is now deployed to our S3 bucket!