Built with developer experience in mind, Tensorkube simplifies the process of deploying serverless GPU apps. In this guide, we will walk you through the process of deploying SpeechT5 on it.

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Prerequisites

Before you begin, ensure you have the configured Tensorkube on your AWS account. If you haven’t done that yet, follow the Getting Started guide.

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Deploying SpeechT5 on Tensorfuse

Each tensorkube deployment requires two things - your code and your environment (as a Dockerfile). While deploying machine learning models, it is beneficial if your model is also a part of your container image. This reduces cold-start times by a significant margin.

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Code files

We will write a small FastAPI app that loads the model and serves predictions. The FastAPI app will have two endpoints - /readiness and /tts. Remember that the /readiness endpoint is used by Tensorkube to check the health of your deployments.
tts_deploy.py
import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset

app = FastAPI()
device = 0 if torch.cuda.is_available() else -1

processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")

class AppInput(BaseModel):
    text: str

@app.get("/")
async def root():
    is_cuda_available = torch.cuda.is_available()
    return {
        "message": "Hello World",
        "cuda_available": is_cuda_available,
    }

@app.get("/readiness")
async def readiness():
    return {"status": "ready"}

# endpoint for text to speech
@app.post("/tts")
async def generate_text(input: AppInput):
    inputs = processor(text=input.text, return_tensors="pt")
    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)

    return {"speech": speech.numpy().tolist()}

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Environment files (Dockerfile)

Next, create your requirements.txt file
requirements.txt
torch
transformers
datasets
fastapi
uvicorn
pydantic
SentencePiece
And finally, a Dockerfile for your FastAPI app. Given below is a simple Dockerfile that you can use:
Dockerfile
# Use the nvidia cuda base image
FROM nvidia/cuda:12.1.1-devel-ubuntu22.04

RUN apt-get update && apt-get install -y \
    python3.10 \
    python3.10-dev \
    python3-pip \
    && rm -rf /var/lib/apt/lists/*

RUN ln -s /usr/bin/python3.10 /usr/bin/python

WORKDIR /app
COPY . .
RUN pip3 install -r requirements.txt

EXPOSE 80
CMD ["uvicorn", "tts_deploy:app", "--host", "0.0.0.0", "--port", "80"]

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Deploying the app

SpeechT5 is now ready to be deployed on Tensorkube. Navigate to your project root and run the following command: 
tensorkube deploy --gpus 1 --gpu-type a10g
Speech T5 is now deployed on your AWS account. You can access your app at the URL provided in the output or using the following command: 
tensorkube list deployments
followed by 
tensorkube get deployment <deployment-id>
And that’s it! You have successfully deployed SpeechT5 on serverless GPUs using Tensorkube. 🚀