ChatGPT answer to: Implement me a hyper-optimized service for serving predictions by transformer models

gistfile1.txt

from fastapi import FastAPI
import transformers
import torch

app = FastAPI()

# Load the tokenizer and model
tokenizer = transformers.AutoTokenizer.from_pretrained("model_name")
model = transformers.AutoModelForTokenClassification.from_pretrained("model_name")

# Use torch.jit.trace to create a torch.jit.ScriptModule from the model
traced_model = torch.jit.trace(model, example_inputs=inputs)

# Check if a CUDA-enabled GPU is available
if torch.cuda.is_available():
    # If a GPU is available, move the model and input tensors to the GPU
    traced_model = traced_model.to("cuda")
    inputs = inputs.to("cuda")

@app.post("/predict")
def predict(input: str):
    # Tokenize the input
    tokens = tokenizer.encode(input)

    # Convert the tokens to their numeric IDs
    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # Create a tensor with the input IDs
    inputs = torch.tensor([input_ids])

    # If a GPU is available, move the input tensor to the GPU
    if torch.cuda.is_available():
        inputs = inputs.to("cuda")

    # Prevent PyTorch from tracking the gradients of the model's weights
    with torch.no_grad():
        # Use the traced model to generate predictions
        predictions = traced_model(inputs)

    # Get the first prediction
    first_prediction = predictions[0]

    # If a GPU is available, move the prediction tensor back to the CPU
    if torch.cuda.is_available():
        first_prediction = first_prediction.to("cpu")

    # Convert the prediction tensor to a list of predicted labels
    predicted_labels = [tokenizer.decode(g) for g in torch.argmax(first_prediction, axis=1)]

    # Return the predicted labels as the response
    return {"labels": predicted_labels}