Sending Weights & Biases Alerts to WhatsApp Using Twilio

Monitoring machine learning experiments is crucial to ensure your models are trained correctly and catch any issues early. Weights & Biases (wandb) is a popular tool for tracking and visualizing machine learning experiments. In this blog post, we'll show you how to send wandb alerts to WhatsApp using Twilio so that you can receive real-time notifications on your phone.

Step 1: Set Up a Twilio Account

First, you'll need a Twilio account. Twilio is a cloud communications platform that provides APIs for sending messages to various channels, including WhatsApp.

• Sign up for a Twilio account at twilio.com Click this URL
• Set up a Twilio phone number that can send WhatsApp messages by following Twilio's WhatsApp setup guide. Click this URL
• Note down your Twilio account SID, auth token, and WhatsApp-enabled phone number.

Step 2: Install the Twilio Python SDK

To interact with Twilio's API, you'll need the Twilio Python SDK. You can install it using pip:

pip install twilio

Step 3: Set Up wandb Alerts

Next, ensure you have wandb set up in your project. If you don't have wandb installed, you can install it using pip:

pip install wandb

#Initialize wandb in your script:

import wandb

wandb.init(project="your_project_name")

Replace "your_project_name" with the name of your wandb project.

Step 4: Integrate Twilio with wandb Alerts

Now, let's write a function to send WhatsApp messages using Twilio and integrate it with wandb alerts.

#Initialize Twilio Client

First, initialize the Twilio client with your account credentials:

from twilio.rest import Client

# Twilio credentials
account_sid = 'your_account_sid'
auth_token = 'your_auth_token'
twilio_phone_number = 'whatsapp:+your_twilio_phone_number'
recipient_phone_number = 'whatsapp:+recipient_phone_number'

# Initialize Twilio client
client = Client(account_sid, auth_token)

Replace 'your_account_sid', 'your_auth_token', 'whatsapp:+your_twilio_phone_number', and 'whatsapp:+recipient_phone_number' with your actual values.

#Create a Function to Send WhatsApp Messages

Next, create a function to send a WhatsApp message:

def send_whatsapp_message(message):
    message = client.messages.create(
        body=message,
        from_=twilio_phone_number,
        to=recipient_phone_number
    )
    print(f"Message sent: {message.sid}")

Step 5: Monitor Metrics and Send Alerts

Create a function to monitor your metrics and send alerts when certain conditions are met:

def monitor_metrics():
    # Simulate monitoring a metric (replace with your actual monitoring logic)
    metric_value = wandb.run.summary.get('metric_name', 0)
    
    # Define your alert condition
    if metric_value > some_threshold:
        alert_message = f"Alert: Metric value has exceeded the threshold! Current value: {metric_value}"
        send_whatsapp_message(alert_message)
        # Optionally, log the alert to wandb
        wandb.alert(
            title="Metric Alert",
            text=alert_message,
            level=wandb.AlertLevel.WARN
        )

Replace 'metric_name' with the metric name you want to monitor and some\_threshold with the value that triggers the alert.

#Integrate Monitoring into Your Training Loop

Finally, integrate the monitor\_metrics function into your training loop or wherever appropriate in your script:

# Example training loop
for epoch in range(epochs):
    # Your training code here
    
    # Monitor metrics and send alerts
    monitor_metrics()

    # Log metrics to wandb
    wandb.log({"metric_name": metric_value})

#Conclusion

By following these steps, you can receive real-time alerts on WhatsApp whenever specific conditions are met in your wandb experiment. This setup allows you to stay informed about the progress and health of your machine-learning models, even when you're away from your computer.

Feel free to customize the alert conditions and the message content to suit your needs. Happy monitoring!

This blog post provides a comprehensive guide to setting up real-time alerts for your wandb experiments using Twilio and WhatsApp. With this setup, you can ensure that you're always in the loop with your machine learning model's performance.

Originally published on Medium.