Optimizing a Slow ML Inference API: Lessons Learned

When I started working with Inference, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i optimised a slow ml inference api. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it.

Introduction to Optimizing a Slow ML Inference API

I still recall the frustration of dealing with a slow ML inference API. The latency was unbearable, and it seemed like no matter what I did, I just couldn't get the performance I needed. But after weeks of trial and error, I finally managed to optimize the API and achieve significant improvements. In this article, I'll share my experience, the mistakes I made, and the lessons I learned along the way.

The Initial Challenges

When I first started working on the ML inference API, I was excited to see it in action. However, my enthusiasm was short-lived. The API was slow, and the latency was adding up quickly. I discovered that loading the model on every request was a major contributor to the latency, adding a whopping 3 seconds to each request. It seemed like a simple fix, but it was a crucial one. By loading the model at startup, I was able to eliminate this unnecessary overhead and significantly improve the API's performance.

The Power of Batching and Quantization

Another major breakthrough came when I implemented batching predictions. This simple technique reduced API calls by a staggering 90 percent, which not only improved performance but also reduced the load on the server. But I didn't stop there. I also experimented with model quantization using ONNX Runtime, which reduced memory usage by an impressive 60 percent without any loss of accuracy. These two optimizations combined had a significant impact on the API's performance and made it much more efficient.

The Importance of Async Endpoints

As I continued to optimize the API, I realized the importance of async endpoints. By using async endpoints in FastAPI, I was able to handle 10 times more concurrent requests, which was a major improvement. This not only improved the API's performance but also made it more scalable and reliable.

Mistakes Made and Lessons Learned

Of course, my journey to optimizing the ML inference API was not without its mistakes. One of the biggest mistakes I made was loading the model inside the prediction function, which meant it reloaded on every request. This was a costly mistake that added unnecessary latency to each request. I also failed to profile the API before optimizing, which meant I wasted time working on a step that contributed only 2 percent of the latency. Perhaps the most significant mistake I made was deploying a quantized model without running the full test suite, which could have had disastrous consequences.

Key Takeaways

So, what did I learn from this experience? First and foremost, I learned the importance of profiling before optimizing. It's crucial to measure the performance of your API before making any changes, or you may end up optimizing the wrong thing. I also learned that loading heavy artifacts at application startup, rather than at request time, can make a significant difference in performance. Finally, I learned that testing quantized models with the same rigor as the original is crucial to ensure that they work as expected.

Implementing Optimizations with FastAPI

So, how can you implement these optimizations in your own ML inference API using FastAPI? Here's an example of how you can load a model at startup using the lifespan context and create an async prediction endpoint:

from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
import onnxruntime

app = FastAPI()

class PredictionRequest(BaseModel):
    input_data: str

# Load the model at startup
@app.on_event("startup")
async def load_model():
    global session
    session = onnxruntime.InferenceSession("model.onnx")

# Create an async prediction endpoint
@app.post("/predict")
async def predict(request: PredictionRequest):
    # Use the loaded model to make predictions
    inputs = ...
    outputs = session.run(None, inputs)
    return {"prediction": outputs}

# Use async endpoints to handle concurrent requests
@app.post("/batch_predict")
async def batch_predict(requests: List[PredictionRequest]):
    # Use the loaded model to make batch predictions
    inputs = ...
    outputs = session.run(None, inputs)
    return [{"prediction": output} for output in outputs]

In this example, we load the model at startup using the @app.on_event("startup") decorator and create an async prediction endpoint using the @app.post("/predict") decorator. We also use async endpoints to handle concurrent requests and make batch predictions.

Wrapping Up

Optimizing a slow ML inference API can be a challenging task, but with the right techniques and tools, it's achievable. By implementing batching, quantization, and async endpoints, I was able to significantly improve the performance of my API. I also learned valuable lessons about the importance of profiling, loading heavy artifacts at startup, and testing quantized models. By following these tips and examples, you can optimize your own ML inference API and achieve the performance you need.

Category: MLOps

InferenceOptimizationFastAPIPerformanceMLOpsMachine LearningAPI Development

Comments

Post a Comment

Popular posts from this blog

How I Started Learning Data Science as a Beginner (My Roadmap)

Introduction When I decided to learn data science, I had no clear idea where to begin. There were many videos, courses, and opinions online, which made me confused. So I decided to follow a simple learning path instead of learning everything at once. This post shares the roadmap I followed as a beginner. Step 1: Understand What Data Science Is Before coding, I focused on understanding the meaning of data science. Data science mainly involves: • Working with data • Finding patterns • Making decisions using data Understanding this gave me clarity. Step 2: Learn Basic Python Python is the foundation of data science. I started with basic topics like: • Variables • Data types • Conditions • Loops • Functions Daily practice helped me improve slowly. Step 3: Learn Basic Statistics Statistics felt difficult at first, but only basic concepts are required. Important topics include: • Mean, median, and mode • Variance and standard deviation • Probability basics • Correlation Understanding concept...
Read more

Difference Between Artificial Intelligence, Machine Learning, and Data Science

Image
Introduction Artificial Intelligence, Machine Learning, and Data Science are often used interchangeably, but they do not mean the same thing. Many beginners feel confused while starting their learning journey because these terms are closely related. Understanding the difference between AI, ML, and Data Science is very important for students who want to build a career in technology. What is Artificial Intelligence (AI) ?  Artificial Intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence. AI systems are designed to: Think logically Make decisions Solve problems Mimic human behavior Examples of Artificial Intelligence: Voice assistants (Alexa, Siri) Chatbots Face recognition systems Self-driving cars AI is the broadest field among the threee What is Machine Learning (ML)? Machine Learning (ML) is a subset of Artificial Intelligence. In Machine Learning, computers learn patterns from data without being expli...
Read more

Lessons Learned from My First Machine Learning Model

When I started working with ML, I quickly realized there was a gap between theory and what actually happens in practice. This post is about mistakes i made while training my first ml model. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Machine Learning Mistakes I still remember the excitement of training my first machine learning model. I had spent weeks collecting and preprocessing the data, and finally, it was time to see the results. But, as it often does, reality had other plans. My model's performance was suspiciously high, and it wasn't until later that I realized the mistakes I had made. In this post, I'll share the lessons I learned from those mistakes, in the hopes that you can avoid them in your own machine learning journey. My Experience with Machine Learning As a beginner, I made a few critical errors that affected my model'...
Read more