FastAPI Docker Quick and Easy Setup | Deploy FastAPI with Docker in minutes along with TensorFlow
FastAPI Docker Example file with TensorFlow, keras and uvicorn demonstration video. This video is about running a simple fastAPI based python application using docker. Which is simply containerizing the FASTAPI App with Docker. I demonstrated in this video how simple it is to run a python based FastAPI app to run using the docker image. Here is the Dockerfile used in this video
Use the official Python base image
FROM python:3.11.6-slim
Set environment variables to prevent Python from writing .pyc files and buffering stdout/stderr
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
Upgrade pip and install Python dependencies
RUN pip install --upgrade pip
RUN pip install opencv-python-headless tensorflow==2.14.0 keras==2.14.0 fastapi==0.110.0 uvicorn==0.22.0
Set the working directory
WORKDIR /app
Copy the FastAPI app into the container
COPY app.py /app
Expose the port that the app will run on
EXPOSE 8000
Command to run the FastAPI app using Uvicorn
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
and here is the app.py file
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
return {"item_id": item_id, "q": q}
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