Setting Up an Amazon EC2 Trn1 Instance

• First, log into your AWS Management Console.
• Navigate to the EC2 dashboard and click on “Launch Instance.”
• Choose an Amazon Machine Image (AMI). For a Trn1 instance, you may want to use an AMI that’s optimized for your machine learning workloads.
• In the “Instance Type” section, select the Trn1 instance type. You may need to request a limit increase if this instance type is not available by default.
• Configure instance details such as VPC, subnet, and IAM roles as per your requirements.
• In the “Add Storage” section, specify the EBS volumes as needed.
• Add any tags if necessary for better management.
• Configure security groups to allow appropriate inbound and outbound traffic. Particularly, ensure that the Bubble app can access ports that will be opened for communication.
• Review your settings and launch the instance. Don’t forget to download the key pair if you don’t already have one.

Preparing Your Trn1 Instance

• SSH into your Trn1 instance using the key pair you downloaded.

  ssh -i "your-key-pair.pem" ec2-user@your-ec2-ip

• Update the package index:

  sudo apt-get update

• Install required dependencies such as Python, pip, or Docker depending on your machine learning stack and the services you wish to integrate with Bubble.

  sudo apt-get install python3-pip

• Setup any specific machine learning libraries. For instance:

  pip3 install tensorflow

Configuring Bubble Backend Workflows

• Log into your Bubble application.
• Navigate to the “Backend Workflows” section in the Bubble editor.
• Create a new API endpoint to interact with your Trn1 instance.
• Set up the required parameters that you would be passing to the Trn1 instance for processing. Typically, these could be inputs needed by your machine learning model.
• Configure the API endpoint to call the external API. In this case, it will be your Trn1 instance's IP or a domain pointing to it, if you set up a route.
• Use the “API Connector” plugin to make HTTP requests from Bubble to your Trn1 instance:
  • Go to Plugins > Add Plugins > API Connector.
  • Click to add a new API.
  • Set up your API details.

Creating an API on Trn1 Instance

• Within your Trn1 instance, create a simple REST API (using Flask for example) to accept requests from your Bubble app:

  from flask import Flask, request, jsonify
  
  app = Flask(name)
  @app.route('/predict', methods=['POST'])
  def predict():
      # Read the incoming data
      data = request.json
      # Perform operations, for example, using the TensorFlow model
      predictions = model.predict(data['input'])
      # Return the response
      return jsonify(predictions=predictions.tolist())
  if name == 'main':
      app.run(host='0.0.0.0', port=5000)
  

• Run your Flask app:

  python3 app.py

• Ensure the security groups allow traffic to port 5000.

Connecting Bubble to the Trn1 API

• Back in Bubble, configure the API Call in the API Connector plugin to match the endpoint you created on the Trn1 instance. For example:

  {
    "method": "post",
    "url": "http://:5000/predict",
    "headers": [],
    "body": {
      "key": "value" // Adjust your input as needed
    }
  }
  

• Set the response to be processed in Bubble accordingly, typically handling data back from your model.
• Test the endpoint from within Bubble to ensure it’s receiving and processing data correctly.

Final Integration Steps

• Integrate your Bubble app's front-end with the backend workflows you just set up.
• Make sure you have proper error handling and validations in place both in Bubble and on your Trn1 instance to ensure smooth operations.
• Deploy your changes in Bubble and thoroughly test the integration to confirm it's working as expected.