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Description:

modelinferencefastapi 0.0.1a0

model-inference-fastapi
Running inference on ML model over http using fastapi
Installation
pip install model-inference-fastapi

Usage

Create a model service file model_service.py implementing the ModelServiceBase interface like so.
from typing import Dict, List, Optional
from model_inference_fastapi.model_service_base import ModelServiceBase

class ModelService(ModelServiceBase):
__model = None

@staticmethod
def validate(data: Optional[List[Dict]] = None, image_files: Optional[List[bytes]] = None) -> Optional[str]:
return

@staticmethod
def predict(data: Optional[List[Dict]] = None, image_files: Optional[List[bytes]] = None) -> List[Dict]:
return [{"status": "Ready to Go!!!"}]

For instance if you are trying to deploy an image classification model (
e.g. ResNet50) using torch. You will need
the following model_service.py example in pytorch.
import json
from typing import Dict, List, Optional

import torch
from torchvision.models import resnet50
from torchvision.transforms import Resize, Compose, CenterCrop, ToTensor, Normalize

from model_inference_fastapi.model_service_base import ModelServiceBase
from model_inference_fastapi.utils import convert_file_to_pil_image

# derived from https://keras.io/api/applications/
class ModelService(ModelServiceBase):
__model = resnet50(weights="IMAGENET1K_V2")
__transform = Compose([
Resize(size=256),
CenterCrop(size=224),
ToTensor(),
Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])

with open("imagenet1000_clsidx_to_labels.txt") as f:
__class_names = eval(f.read())

@staticmethod
def validate(*, data: Optional[List[Dict]] = None, image_files: Optional[List[bytes]] = None):
if len(image_files) != 1:
return "There must be at least one image file in image_files"

@staticmethod
def predict(*, data: Optional[List[Dict]] = None, image_files: Optional[List[bytes]] = None) -> List[Dict]:
test_image_tensor = ModelService.__transform(convert_file_to_pil_image(image_files[0]))
test_image_tensor = test_image_tensor.view(1, 3, 224, 224)

with torch.no_grad():
ModelService.__model.eval()
# Model outputs log probabilities
out = ModelService.__model(test_image_tensor)
ps = torch.exp(out)
top_k, top_class = ps.topk(1, dim=1)
print("Output class : ", ModelService.__class_names[top_class.cpu().numpy()[0][0]])

return [{"class": ModelService.__class_names[top_class.cpu().numpy()[0][0]]}]

Run Server
uvicorn model_inference_fastapi.main:app

Visit the API testing page powered by FastAPI here http://127.0.0.1:8000/docs/.

Navigate to the /predict/ AKA Prediction Route dropdown
Click the Try it out button
For the image_files array click Add string item button
Choose a file to upload
Click execute
Scroll down to see the server response

Testing model_service.py
Here is a sample test file for image classification model_service.py
# replace this import with import for you own `model_service.py`
from model_inference_fastapi.model_service import ModelService

def test_predict():
with open('/path/to/image.jpg', 'rb') as file:
prediction = ModelService.predict(image_files=[file.read()])
assert prediction[0]["class"] == "dog"

Using the docker image
See example dir for more info.

create a Dockerfile like so

FROM robotstech/model-inference-fastapi

EXPOSE 8000
WORKDIR /app

COPY requirements.txt ./requirements.txt

RUN pip install --upgrade pip
RUN pip install -r requirements.txt

# move model_service.py
COPY ./model_service.py ./model_inference_fastapi/

# move data or other resources to root dir of model-inference
COPY ./imagenet1000_clsidx_to_labels.txt .

The key info here is to move the model_service.py and data/resource to the right directory