Hosting Hugging Face Transformers on Serverless GPUs
Hugging Face Transformers have become the go-to library for natural language processing tasks, offering thousands of pre-trained models for tasks like text classification, question answering, and text generation. However, deploying these models in production, especially for inference at scale, can be challenging due to their computational requirements. Serverless GPU platforms provide an elegant solution, offering the perfect balance of performance, scalability, and cost-efficiency.
Note: This guide focuses on deploying Hugging Face models for inference. For fine-tuning models on serverless GPUs, check out our Fine-Tuning Guide.
Why Serverless GPUs for Hugging Face Models?
Serverless GPU platforms offer several advantages for hosting Hugging Face models:
Cost-Effective Scaling
Pay only for the inference time you use, with automatic scaling to handle traffic spikes without over-provisioning resources.
Reduced Latency
Global distribution options bring your models closer to end-users, reducing inference latency.
Simplified Operations
No need to manage Kubernetes clusters or GPU drivers – focus on your models and applications.
Top Platforms for Serverless Hugging Face Deployment
| Platform | Cold Start | Max Memory | GPU Options | Pricing |
|---|---|---|---|---|
| AWS Lambda with EFS | 1-3s (warm) | 10GB | NVIDIA T4 | Per request + GB-sec |
| Google Cloud Run | 100ms-1s (warm) | 8GB | NVIDIA T4, L4 | vCPU-sec + memory |
| Hugging Face Inference API | ~500ms | N/A | Various | Per token/request |
| Banana.dev | 2-10s (cold) | 40GB | A100, A10G | Per second |
Deploying with AWS Lambda and EFS
Let’s walk through deploying a Hugging Face model using AWS Lambda with EFS for model storage, which is cost-effective for models up to 10GB.
1. Set Up EFS for Model Storage
First, create an EFS filesystem and mount target in your VPC:
# Create EFS filesystem
aws efs create-file-system --creation-token huggingface-models
--performance-mode generalPurpose
--tags Key=Name,Value=huggingface-models
# Create mount target in each subnet
aws efs create-mount-target
--file-system-id fs-12345678
--subnet-id subnet-12345678
--security-groups sg-12345678
# Create access point for Lambda
aws efs create-access-point
--file-system-id fs-12345678
--posix-user Uid=1000,Gid=1000
--root-directory "Path=/models,Create={OwnerUid=1000,OwnerGid=1000,Permissions=755}"2. Create a Lambda Layer with Dependencies
Create a directory structure and install the required packages:
mkdir -p python/lib/python3.9/site-packages
cd python/lib/python3.9/site-packages
# Install dependencies to the directory
pip install
torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
transformers==4.30.0
sentencepiece==0.1.99
-t .
# Create zip file
cd ../../..
zip -r ../hf-transformers-layer.zip .3. Deploy the Lambda Function
Create a Lambda function that loads the model from EFS:
import os
import json
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Initialize model and tokenizer outside the handler for warm starts
MODEL_PATH = "/mnt/efs/models/distilbert-base-uncased-finetuned-sst-2-english"
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
model.eval()
print("Model loaded successfully")
except Exception as e:
print(f"Error loading model: {str(e)}")
raise
def lambda_handler(event, context):
try:
# Get input text from the event
text = event.get('text', '')
if not text:
return {
'statusCode': 400,
'body': json.dumps({'error': 'No text provided'})
}
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
# Get predictions
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
confidence, predicted_class = torch.max(predictions, dim=1)
return {
'statusCode': 200,
'body': {
'prediction': model.config.id2label[predicted_class.item()],
'confidence': confidence.item(),
'model': model.config._name_or_path
}
}
except Exception as e:
return {
'statusCode': 500,
'body': json.dumps({'error': str(e)})
}Important: Make sure your Lambda function has sufficient memory (at least 3GB) and timeout settings appropriate for your model size.
Optimizing Performance
To get the best performance from your serverless Hugging Face deployment:
1. Model Optimization
- Use
optimumto optimize models for inference - Quantize models to FP16 or INT8 for faster inference
- Consider using smaller, distilled models when possible
# Optimize a model with Optimum
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
# Convert to ONNX format
ort_model = ORTModelForSequenceClassification.from_pretrained(
model_id,
export=True,
provider="CUDAExecutionProvider"
)
# Save optimized model
ort_model.save_pretrained("./optimized_model")
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained("./optimized_model")2. Cold Start Mitigation
- Use provisioned concurrency to keep functions warm
- Implement a warming strategy with scheduled events
- Consider using larger memory sizes for faster model loading
Monitoring and Scaling
Monitor your serverless deployment with these key metrics:
- Invocation Count: Number of times your function is invoked
- Duration: Execution time of each invocation
- Concurrent Executions: Ensure you’re not hitting account limits
- Errors: Monitor for model loading or inference errors
Cost Considerations
Serverless GPU pricing can vary significantly based on:
- Model size and complexity
- Request volume and concurrency
- Inference time per request
- Data transfer costs
For high-traffic applications, consider a hybrid approach with dedicated GPU instances for baseline traffic and serverless for handling spikes.
Conclusion
Hosting Hugging Face Transformers on serverless GPU platforms provides an excellent balance of performance, scalability, and cost-efficiency for many use cases. By following the patterns and optimizations outlined in this guide, you can deploy production-ready NLP models that scale with your application’s needs.
Remember to continuously monitor your deployment and adjust configurations based on actual usage patterns to optimize both performance and cost.
