When would you use the Hugging Face `pipeline` function versus building a manual inference pipeline?

Q: When would you use the Hugging Face `pipeline` function versus building a manual inference pipeline?

Learn the answer to "When would you use the Hugging Face `pipeline` function versus building a manual inference pipeline?" with detailed explanations, code examples, and best practices on DeployU.

The Scenario

You are an ML engineer at an e-commerce company. Your team is responsible for building a new sentiment analysis API that will be used by the customer support team to analyze customer reviews in real-time.

The API has the following requirements:

Low latency: The API must respond in under 100ms.
High throughput: The API must be able to handle at least 100 requests per second.
Cost-effective: The API must be as cheap as possible to run.

You have already fine-tuned a distilbert-base-uncased model for sentiment analysis. Now you need to decide how to implement the inference logic.

The Challenge

Should you use the high-level pipeline function or a manual inference pipeline? Justify your choice by explaining the trade-offs between the two approaches. Outline your implementation plan, including code examples and a discussion of how you would optimize the pipeline for production.

Wrong Approach

A junior engineer might default to using the `pipeline` function because it's easy. They might not consider the performance implications of using it in a high-throughput, low-latency production environment. They might also not be aware of optimization techniques like batching and using a more efficient tokenizer.

Addresses symptoms, not root cause

Right Approach

A senior engineer would know that a manual inference pipeline is the best choice for this use case. They would be able to explain that a manual pipeline provides the performance and control needed for a production environment. They would also be able to outline a clear implementation plan, including code examples and a discussion of optimization techniques.

Step 1: Analyze the Trade-offs

First, let’s analyze the trade-offs between the pipeline function and a manual inference pipeline:

Feature	`pipeline` function	Manual inference pipeline
Ease of use	Very easy to use, requires only a few lines of code.	More complex to implement, requires more boilerplate code.
Performance	Slower, not optimized for production.	Faster, can be highly optimized for production.
Flexibility	Limited, provides a high-level abstraction.	Very flexible, provides full control over the inference process.
Use cases	Prototyping, demos, and simple use cases.	Production, research, and complex use cases.

For our sentiment analysis API, performance is a critical requirement. We need to be able to handle a high volume of requests with low latency. Therefore, a manual inference pipeline is the best choice.

Step 2: Implementation Plan - Manual Inference Pipeline

Here’s how we can implement a manual inference pipeline with batching and optimization:

1. Load the model and tokenizer:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device)

2. Create a prediction function:

This function will take a list of texts as input and return a list of predictions. It will handle tokenization, inference, and post-processing.

def predict(texts):
    # Tokenize the texts
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(device)

    # Get the model output
    with torch.no_grad():
        logits = model(**inputs).logits

    # Post-process the output
    probs = logits.softmax(dim=-1)
    return probs.cpu().numpy()

3. Build a web server:

We can use a web framework like Flask or FastAPI to build a web server that will expose our prediction function as an API endpoint. We should also implement a batching mechanism to group incoming requests together and process them in a single batch.

Step 3: Optimization

Here are some techniques we can use to optimize the pipeline for production:

Batching: Processing multiple requests at once can significantly improve performance.
Quantization: Converting the model’s weights to a lower-precision format (e.g., INT8) can reduce the model’s size and speed up inference.
ONNX Runtime: The ONNX Runtime is a high-performance inference engine that can be used to run models in a variety of environments.
Compiler: Use a compiler like TorchScript to optimize the model’s code for performance.

Systematic, production-ready debugging

Practice Question

You are building a demo for a new model and want to get it up and running as quickly as possible. Which approach would you choose?

Questions