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# !pip install torchvision pydantic | ||
import base64 | ||
import io | ||
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import torch | ||
import torchvision | ||
from PIL import Image | ||
from pydantic import BaseModel | ||
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import lightning as L | ||
from lightning.app.components.serve import Image as InputImage | ||
from lightning.app.components.serve import PythonServer | ||
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class PyTorchServer(PythonServer): | ||
def __init__(self): | ||
super().__init__( | ||
input_type=InputImage, | ||
output_type=OutputData, | ||
cloud_compute=L.CloudCompute("gpu"), | ||
) | ||
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def setup(self): | ||
self._model = torchvision.models.resnet18(pretrained=True) | ||
self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | ||
self._model.to(self._device) | ||
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def predict(self, request): | ||
image = base64.b64decode(request.image.encode("utf-8")) | ||
image = Image.open(io.BytesIO(image)) | ||
transforms = torchvision.transforms.Compose( | ||
[ | ||
torchvision.transforms.Resize(224), | ||
torchvision.transforms.ToTensor(), | ||
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), | ||
] | ||
) | ||
image = transforms(image) | ||
image = image.to(self._device) | ||
prediction = self._model(image.unsqueeze(0)) | ||
return {"prediction": prediction.argmax().item()} | ||
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class OutputData(BaseModel): | ||
prediction: int | ||
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app = L.LightningApp(PyTorchServer()) | ||
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# TODO: name confusion LoadBalancer vs. AutoScaler | ||
# from lightning.app.components import LoadBalancer | ||
# component = LoadBalancer( | ||
# PyTorchServer, | ||
# num_replicas=4, | ||
# balance_function="predict", | ||
# auto_scale=False, | ||
# ) | ||
# app = L.LightningApp(component) |