Tutorial

Managing Micro-Burst Usage and 1-Click Models on DigitalOcean

Published on November 27, 2024

Managing Micro-Burst Usage and 1-Click Models on DigitalOcean

Introduction

When it comes to deploying AI models, performance is key. However, one of the biggest challenges is managing unpredictable spikes in demand—often called micro-bursts. These short periods of intense usage can overwhelm your infrastructure if not handled properly. However, one can easily and efficiently manage micro-bursts without sacrificing performance or user experience.

In this article, we will explore the strategies for handling micro-burst usage during model deployments and implement a basic Customer Support Chatbot with 1-click models on DigitalOcean.

What Are Micro-Bursts?

Micro-bursts refer to sudden, short-lived spikes in usage or demand on a system, typically lasting anywhere from a few milliseconds to a few seconds. These bursts can happen unpredictably and often involve a significant increase in requests, data traffic, or resource consumption. In cloud computing and AI model deployments, micro-bursts can cause momentary stress on infrastructure, leading to potential performance bottlenecks if not adequately managed.

Key Characteristics of Micro-Bursts:

High Intensity, Short Duration: They are characterized by their brief but intense nature. For example, a website might experience a surge of thousands of users trying to access a page simultaneously after an email blast or a social media post.
Unpredictable Timing: Micro-bursts often occur unexpectedly, making it challenging to predict and prepare for them in advance.
Impact on Performance: Even though micro-bursts are short-lived, they can overload servers, causing latency spikes, slower response times, or even temporary service outages if resources are not scaled quickly enough.
Common in Real-Time Applications: Applications like chatbots, gaming servers, live streaming, and financial trading platforms are particularly susceptible to micro-bursts due to their real-time nature.
Dynamic Pricing: Many a times frequent micro-bursts can lead to unpredictable and potentially high costs, especially if auto-scaling is not optimized.

Examples of Micro-Bursts:

E-commerce Flash Sales: During a limited-time sale or a product drop, thousands of users may attempt to check out simultaneously, creating a micro-burst in server requests.
AI Chatbots: A customer support chatbot may experience a sudden influx of user queries during peak hours, such as after sending out a promotional email or during high-traffic events like Black Friday.
Social Media Trends: A viral post or trending topic can lead to a micro-burst of API calls on social media platforms as users interact, like, comment, and share in real-time.
Financial Trading: Stock trading platforms can face micro-bursts during market openings, earnings announcements, or geopolitical events that trigger a surge in buy/sell orders.

Micro-bursts can be triggered by marketing campaigns, product launches, or viral content. If not handled properly, micro-bursts can lead to:

High Latency: When traffic spikes suddenly, servers may struggle to handle the increased number of requests. This can cause slower response times, leading to a poor user experience. For example, users may experience delays when loading pages or using your application, which can be frustrating and may drive them away.

System Crashes: If your infrastructure is not equipped to handle sudden surges in traffic, it can become overloaded, resulting in system failures or crashes. This means your website or app may go offline during critical moments, like a major product launch or marketing campaign, leading to potential revenue loss and damage to your brand’s reputation.

Cost Inefficiency: To avoid crashes, some companies allocate more servers or compute power than is usually needed. While this can handle traffic spikes, it leads to higher operational costs since those resources remain underutilized most of the time. This approach is not cost-effective, especially if traffic spikes are occasional rather than constant.

Prerequisites

Before we start, make sure you have:

A DigitalOcean account (sign up here if you don’t have one).
Access to the Hugging Face platform and API keys.
Must have gone through the documentation on how to create a GPU Droplet on DigitalOcean.

What are 1-Click Models on DigitalOcean?

1-Click Models are a user-friendly solution that enables quick deployment of popular AI models with minimal setup. Users can select a model on Hugging Face, choose DigitalOcean as the deployment option, and instantly launch it on DigitalOcean GPU Droplets. This streamlined process creates a dedicated inference endpoint within minutes, making it easier for developers to build and scale AI applications without extensive configuration. The models can also be deployed directly from the DigitalOcean cloud console, emphasizing simplicity and efficiency.

Key Benefits of Using 1-Click Models

1. Instant Model Deployment: Quickly deploy popular AI models like Llama 3 by Meta and Qwen with a single click on GPU Droplets powered by NVIDIA H100 GPUs.

2. Easy Setup: No need for complicated setups—just deploy and start using model endpoints right away, so you can focus on building your AI applications.

3. High Performance: These models are optimized to run efficiently on DigitalOcean’s high-speed GPU Droplets, delivering powerful performance.

4. Quick Results: Get up and running in minutes instead of days, enabling faster access to model inference and quicker time-to-value.

5. Trusted Hugging Face Partnership: All models are maintained and updated by Hugging Face, ensuring you have the latest optimizations and features available, with fully tested model endpoints.

Strategies to Handle Micro-Burst Usage with DigitalOcean

Quickly Deploy AI Models: DigitalOcean offers a simple and efficient way to deploy state-of-the-art models with minimal setup using 1-Click Models. This feature allows you to quickly spin up models without needing to manage complicated infrastructure. The setup is streamlined to remove infrastructure complexities, allowing developers to focus on building with model endpoints immediately without needing any complicated software configurations. These models are optimized to run efficiently on DigitalOcean’s high-performance hardware, ensuring minimal overhead. Customers can start using model inference endpoints within minutes, drastically reducing the time-to-value compared to traditional solutions that require extensive setup.
Autoscaling: To ensure your application can handle micro-bursts during peak traffic, you can set up autoscaling for your DigitalOcean Droplets.
DigitalOcean Kubernetes (DOKS) is a fully managed Kubernetes service that makes deploying and managing Kubernetes clusters easy. You can set up clusters using shared or dedicated CPU Drops and even powerful NVIDIA H100 GPUs (available as single or 8 GPU setups). DOKS works with standard Kubernetes tools, as well as the DigitalOcean API and CLI.

DOKS includes a Cluster Autoscaler (CA), which automatically scales the cluster up or down by adding or removing nodes based on the workload needs. You can enable autoscaling by setting minimum and maximum cluster sizes, either during the initial setup or later. This can be done easily through the DigitalOcean Control Panel or using the doctl command-line tool.

Load Balancing: Load balancing tools monitor private cloud usage and redirect excess traffic to the public cloud when predefined thresholds are met. This strategy optimizes resource utilization and maintains consistent performance. It’s especially beneficial for businesses with predictable traffic patterns, enabling proactive resource management and allocation. DigitalOcean’s load balancers are a fully managed, highly reliable network load-balancing service that efficiently distributes traffic across clusters of Droplets. This setup isolates the health of the entire backend service from any single server, ensuring consistent service availability and maintaining a seamless online presence.
Set Up Resource Alerts: Setting up resource alerts on DigitalOcean is a great way to monitor the performance of your Droplets, Kubernetes clusters, and other resources. These alerts help you stay informed about your infrastructure’s usage, enabling you to take proactive action if resource consumption crosses predefined thresholds. Resource alerts send notifications via Slack or email when Droplet metrics, like CPU usage or bandwidth, fall outside of a threshold you set. This will help you get notified in real time if your infrastructure requires scaling or optimization.
Demand Forecasting for Managing Micro-Burst Usage: Last but not least, predicting the Demand forecasting can be a good option to prevent micro-burst scenarios. Demand forecasting involves analyzing historical data and current trends to predict future cloud resource requirements. By anticipating periods of high demand, such as micro-bursts, you can proactively allocate resources, ensuring that your infrastructure scales up in advance. This reduces the risk of performance bottlenecks, minimizes latency, and improves user experience during sudden traffic spikes, all while optimizing costs by avoiding over-provisioning. It helps you stay one step ahead, ensuring readiness for unpredictable surges.

Real-World Use Case: Implementing a Customer Support Chatbot with 1-click models on DigitalOcean

Step 1: Connecting to the 1-Click Model Deployment is straightforward.

You’ll see a Bearer Token in the initial SSH message when connecting to the GPU Droplet via SSH. This token is necessary for sending requests to the Droplet’s public IP. If you’re working within the Droplet, you can send requests using localhost. Once you have the Bearer Token on your machine, you can make inferences using either cURL or Python. If you’re using the Droplet directly, the token is already stored in the environment, making it even easier to start. We highly recommend the detailed blog we have created on how to set up a 1-Click Model on DigitalOcean GPU Droplets quickly.

Step 2: Setting Up Your Development Environment
Install Required Libraries

pip install --upgrade --quiet huggingface_hub

Step 3: Building the Customer Support Chatbot

import os
from huggingface_hub import InferenceClient

client = InferenceClient(base_url="http://localhost:8080", api_key=os.getenv("BEARER_TOKEN"))

def generate_response(user_input): 
    """ 
    Generate a response using the Llama 3.1 70B Instruct - Single GPU model deployed on DigitalOcean. 
    """ 

    response= client.chat.completions.create(messages=[{"role":"user","content":f"{message}"},],temperature=0.7,top_p=0.95,max_tokens=128)
    return response.choices[0][‘message’][‘content’]
# Example interaction 
if __name__ == "__main__": 
    print("Welcome to the Customer Support Chatbot!") 
    while True: 
        user_input = input("You: ") 
        if user_input.lower() == "exit": 
            break 
        bot_response = generate_response(user_input) 
        print(f"Bot: {bot_response}")

Step 4: Running the Customer Support Chatbot

To run this demo, simply paste the code above into a blank Python file (let’s call it chatbot.py) on your 1-Click Model-enabled Cloud GPU and run the python file with python3 chatbot.py.

Conclusion

Handling micro-burst usage can be challenging. Still, DigitalOcean’s 1-Click Model simplifies the process by removing infrastructure complexities, allowing developers to focus on building and not worry about the complex setup. Thanks to a partnership with Hugging Face, these models are maintained and updated regularly, giving users access to the latest features and optimizations for creating robust AI applications. Furthermore, you can ensure optimal performance and cost-efficiency by leveraging autoscaling, load balancing, and real-time monitoring.

Next Steps

Start your journey by exploring the 1-Click Models powered by Hugging Face on DigitalOcean.
Try deploying your first 1-Click Model with a POC setup.
Experiment with different autoscaling policies to handle micro-bursts effectively.