How to run PyTorch in an Upsun application?

Building a Sentiment Analysis API

May 1, 2025·

·Reading time: 7 minutes

PyTorch is one of the most popular deep learning frameworks, particularly for natural language processing tasks. This tutorial will guide you through creating a sentiment analysis API using PyTorch and FastAPI, then deploying it on the Upsun Cloud Application Platform.

Prerequisites

An Upsun account
Basic knowledge of Python
Familiarity with REST APIs
Git installed on your local machine

Setting Up a new Python application on Upsun

Let’s start by creating a new Python application on Upsun. We’ll use Python 3.12, which is well-supported by both PyTorch and Upsun.

1. Create a new project directory

First, let’s create a new directory for our project and initialize it as a Git repository:

Terminal

mkdir pytorch-sentiment-api
cd pytorch-sentiment-api
git init

2. Configure the Upsun project

Create the necessary Upsun configuration files to define our application’s environment. First, create a .upsun directory and then a config.yaml file inside it:

Terminal

mkdir -p .upsun
touch .upsun/config.yaml

Now, open the config.yaml file and add the following configuration:

.upsun/config.yaml

# Define the applications within the project
applications:
  # Name of the application (can be anything, 'api' used here)
  api:
    # Request a container with more memory, suitable for ML models
    container_profile: HIGH_MEMORY

    # Specify the language and version for the runtime environment
    type: "python:3.12"

    # Define persistent storage mounts for the application container
    mounts:
      # Mount a persistent storage volume named 'pycache' at /app/__pycache__
      # This persists compiled Python bytecode across deployments
      "__pycache__":
        source: "storage" # Use Upsun persistent storage
        source_path: "pycache" # Subdirectory within the storage volume
      # Mount a persistent storage volume named 'cache' at /app/.cache
      # Used by tools like pip or transformers to cache downloads
      ".cache":
        source: "storage"
        source_path: "cache"

    # Configuration for how the application serves web requests
    web:
      # The command Upsun runs to start the web server
      # Uses the PORT environment variable provided by Upsun
      commands:
        start: "uvicorn main:app --host 0.0.0.0 --port $PORT"
      # Defines how Upsun communicates with the application internally
      upstream:
        socket_family: tcp # Use TCP sockets
      # Defines how specific paths are handled
      locations:
        # For the root path "/"
        "/":
          passthru: true # Pass requests directly to the application (defined by commands.start)
          
    # Commands to run during different phases of the deployment process
    hooks:
      # Commands executed during the build phase
      build: |
        set -eux # Exit on error, print commands
        pip install --upgrade pip # Ensure pip is up-to-date
        pip install -r requirements.txt # Install dependencies
        # Compile Python files to bytecode for potentially faster startup
        python -m compileall .        

# Define how incoming HTTP/HTTPS requests are routed to applications
routes:
  # Route requests for the default domain(s) configured for the environment
  "https://{default}/":
    type: upstream # Route to an application defined above
    upstream: "api:http" # Route to the 'api' application using HTTP

Creating a FastAPI application for sentiment analysis

Now, let’s create our FastAPI application that will serve as the API for our sentiment analysis model.

1. Set up the project structure

Let’s organize our project with the following structure:

pytorch-sentiment-api/
├── .upsun/
│   └── config.yaml
├── main.py
├── model.py
├── requirements.txt
└── README.md

2. Define the requirements

Create a requirements.txt file with the necessary dependencies:

requirements.txt

fastapi>=0.68.0
uvicorn>=0.15.0
--extra-index-url https://download.pytorch.org/whl/cpu
torch==2.1.0+cpu
transformers>=4.11.0
pydantic>=1.8.0

Notice how we’re using the --extra-index-url directive to specify that we want the CPU-only version of PyTorch, which is much smaller and suitable for deployment on CPU based containers.

3. Create the Model module

Let’s create a model.py file that will handle the sentiment analysis logic:

model.py

from transformers import pipeline
import torch

class SentimentModel:
    def __init__(self):
        # Verify we're using the CPU version
        print(f"CUDA available: {torch.cuda.is_available()}")
        print(f"PyTorch version: {torch.__version__}")
        
        # Load a small pretrained model for sentiment analysis
        self.classifier = pipeline(
            "sentiment-analysis",
            model="distilbert-base-uncased-finetuned-sst-2-english",
            return_all_scores=True
        )
    
    def predict(self, text):
        """
        Predict the sentiment of the given text.
        Returns the sentiment (POSITIVE/NEGATIVE) and confidence score.
        """
        result = self.classifier(text)
        
        # Process the results
        scores = result[0]
        
        # Find the label with the highest score
        highest_score = max(scores, key=lambda x: x['score'])
        sentiment = highest_score['label']
        confidence = highest_score['score']
        
        return {
            "text": text,
            "sentiment": sentiment,
            "confidence": confidence,
            "all_scores": scores
        }

4. Create the FastAPI application

Now, let’s create the main.py file for our FastAPI application:

main.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import os
import time
from model import SentimentModel

# Create the FastAPI app
app = FastAPI(
    title="Sentiment Analysis API",
    description="An API for sentiment analysis using PyTorch and transformers",
    version="1.0.0"
)

# Load the model (this might take a moment)
print("Loading sentiment analysis model...")
model = None

# Define request and response models
class SentimentRequest(BaseModel):
    text: str

class SentimentResponse(BaseModel):
    text: str
    sentiment: str
    confidence: float

@app.on_event("startup")
async def startup_event():
    global model
    model = SentimentModel()
    print("Model loaded successfully!")

@app.get("/")
def read_root():
    return {"message": "Welcome to the Sentiment Analysis API"}

@app.post("/classification", response_model=SentimentResponse)
async def classify_sentiment(request: SentimentRequest):
    if not model:
        raise HTTPException(status_code=503, detail="Model not loaded yet")
    
    if not request.text or len(request.text.strip()) == 0:
        raise HTTPException(status_code=400, detail="Empty text provided")
    
    try:
        # Perform the prediction
        result = model.predict(request.text)
        
        # Return the response
        return {
            "text": request.text,
            "sentiment": result["sentiment"],
            "confidence": result["confidence"]
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during prediction: {str(e)}")

@app.get("/health")
def health_check():
    return {"status": "healthy", "model_loaded": model is not None}

Deploying the application to Upsun

Now that we have our application ready, let’s deploy it to Upsun.

1. Initialize Git repository

Make sure all files are added to Git:

Terminal

git add .
git commit -m "Initial commit of PyTorch sentiment analysis API"

2. Create a new Upsun project

You can create a new Upsun project through the Upsun Console or using the Upsun CLI:

Terminal

upsun project:create # and follow the prompts!

The Upsun remote should be set automatically on the repository. If it’s not, use

Terminal

upsun project:set {project_id}

3. Push to Upsun

Deploy your application by pushing your code to Upsun:

Terminal

upsun push -y

Upsun will automatically build your application according to the configuration files we created. This process includes installing dependencies, compiling Python files, and starting the FastAPI server.

4. Access your application

Once the deployment is complete, you can access your application at the URL provided by the Upsun console or with upsun url --primary. You should see the welcome message when you visit the root endpoint.

Testing the Sentiment Analysis API

Now that our application is deployed, let’s test it by sending a request to the sentiment analysis endpoint.

Using cURL

Terminal

curl -X POST $(upsun url --primary --pipe)classification \
     -H "Content-Type: application/json" \
     -d '{"text": "I really enjoyed this movie, it was fantastic!"}'

You should receive a response like:

{
  "text": "I really enjoyed this movie, it was fantastic!",
  "sentiment": "POSITIVE",
  "confidence": 0.9978765249252319
}

Using the Swagger UI

FastAPI automatically generates interactive API documentation. You can access it by navigating to /docs on your application URL:

Terminal

open $(upsun url --primary --pipe)docs

This will open the Swagger UI where you can test the API through a user-friendly interface.

Swagger

Advanced considerations

Model size and disk space

The DistilBERT model we’re using is relatively small, but if you’re working with larger models, you might need to increase the disk space and the memory allocated in the Upsun configuration.

Performance optimization

For better performance, you might want to:

Pre-load the model during the build phase
Use model quantization to reduce size
Consider implementing caching for frequently requested inputs

Conclusion

In this tutorial, you’ve learned how to:

Set up a Python application on Upsun
Create a FastAPI app with a sentiment analysis endpoint
Configure PyTorch (CPU version) for efficient deployment
Deploy and test your application

Upsun provides an excellent platform for hosting CPU-based PyTorch applications, with its flexible configuration options and robust scaling capabilities. This approach allows you to deliver machine learning functionality through APIs or in your application without managing complex infrastructure.

Happy coding and machine learning with Upsun!

Last updated on May 1, 2025