Using Hugging Face Models with RunPod

Artificial Intelligence (AI) has revolutionized how applications analyze and interact with data. One powerful aspect of AI is sentiment analysis, which allows machines to interpret and categorize emotions expressed in text. In this tutorial, you will learn how to integrate pre-trained Hugging Face models into your RunPod Serverless applications to perform sentiment analysis. By the end of this guide, you will have a fully functional AI-powered sentiment analysis function running in a serverless environment.

​

Install Required Libraries

To begin, we need to install the necessary Python libraries. Hugging Face’s transformers library provides state-of-the-art machine learning models, while the torch library supports these models.

Execute the following command in your terminal to install the required libraries:

pip install torch transformers

This command installs the torch and transformers libraries. torch is used for creating and running models, and transformers provides pre-trained models.

​

Import libraries

Next, we need to import the libraries into our Python script. Create a new Python file named sentiment_analysis.py and include the following import statements:

import runpod
from transformers import pipeline

These imports bring in the runpod SDK for serverless functions and the pipeline method from transformers, which allows us to use pre-trained models.

​

Load the Model

Loading the model in a function ensures that the model is only loaded once when the worker starts, optimizing the performance of our application. Add the following code to your sentiment_analysis.py file:

def load_model():
    return pipeline(
        "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"
    )

In this function, we use the pipeline method from transformers to load a pre-trained sentiment analysis model. The distilbert-base-uncased-finetuned-sst-2-english model is a distilled version of BERT fine-tuned for sentiment analysis tasks.

​

Define the Handler Function

We will now define the handler function that will process incoming events and use the model for sentiment analysis. Add the following code to your script:

def sentiment_analysis_handler(event):
    global model

    # Ensure the model is loaded
    if "model" not in globals():
        model = load_model()

    # Get the input text from the event
    text = event["input"].get("text")

    # Validate input
    if not text:
        return {"error": "No text provided for analysis."}

    # Perform sentiment analysis
    result = model(text)[0]

    return {"sentiment": result["label"], "score": float(result["score"])}

This function performs the following steps:

1. Ensures the model is loaded.
2. Retrieves the input text from the incoming event.
3. Validates the input to ensure text is provided.
4. Uses the loaded model to perform sentiment analysis.
5. Returns the sentiment label and score as a dictionary.

​

Start the Serverless Worker

To run our sentiment analysis function as a serverless worker, we need to start the worker using RunPod’s SDK. Add the following line at the end of your sentiment_analysis.py file:

runpod.serverless.start({"handler": sentiment_analysis_handler})

This command starts the serverless worker and specifies sentiment_analysis_handler as the handler function for incoming requests.

​

Complete Code

Here is the complete code for our sentiment analysis serverless function:

import runpod
from transformers import pipeline


def load_model():
    return pipeline(
        "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"
    )


def sentiment_analysis_handler(event):
    global model

    if "model" not in globals():
        model = load_model()

    text = event["input"].get("text")

    if not text:
        return {"error": "No text provided for analysis."}

    result = model(text)[0]

    return {"sentiment": result["label"], "score": float(result["score"])}


runpod.serverless.start({"handler": sentiment_analysis_handler})

​

Testing Locally

To test this function locally, create a file named test_input.json with the following content:

{
  "input": {
    "text": "I love using RunPod for serverless machine learning!"
  }
}

Run the following command in your terminal to test the function:

python sentiment_analysis.py --rp_server_api

You should see output similar to the following, indicating that the sentiment analysis function is working correctly:

--- Starting Serverless Worker |  Version 1.6.2 ---
INFO   | Using test_input.json as job input.
DEBUG  | Retrieved local job: {'input': {'text': 'I love using RunPod for serverless machine learning!'}, 'id': 'local_test'}
INFO   | local_test | Started.
model.safetensors: 100%|█████████████████████████| 268M/268M [00:02<00:00, 94.9MB/s]
tokenizer_config.json: 100%|██████████████████████| 48.0/48.0 [00:00<00:00, 631kB/s]
vocab.txt: 100%|█████████████████████████████████| 232k/232k [00:00<00:00, 1.86MB/s]
Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.
DEBUG  | local_test | Handler output: {'sentiment': 'POSITIVE', 'score': 0.9889019727706909}
DEBUG  | local_test | run_job return: {'output': {'sentiment': 'POSITIVE', 'score': 0.9889019727706909}}
INFO   | Job local_test completed successfully.
INFO   | Job result: {'output': {'sentiment': 'POSITIVE', 'score': 0.9889019727706909}}
INFO   | Local testing complete, exiting.

​

Conclusion

In this tutorial, you learned how to integrate a pre-trained Hugging Face model into a RunPod serverless function to perform sentiment analysis on text input.

This powerful combination enables you to create advanced AI applications in a serverless environment.

You can extend this concept to use more complex models or perform different types of inference tasks as needed.

In our final lesson, we will explore a more complex AI task: text-to-image generation.