What is the correct way to run a Python Flask API on Docker with Miniconda and access it on localhost?
Question:
How to access Flask API running on localhost docker container?
I created miniconda docker image with Flask API on it.
Dockerfile is:
FROM continuumio/miniconda3
# Install base utilities
RUN apt-get update
&& apt-get install -y wget
&& rm -rf /var/lib/apt/lists/*
COPY api /root/api
RUN echo "Running $(conda --version)"
RUN conda update conda
RUN conda create -n api python=3.9
RUN echo "conda activate api" >> ~/.bashrc
SHELL ["/bin/bash", "--login", "-c"]
RUN conda activate api
RUN conda install flask requests
ENTRYPOINT ["conda", "run", "-n", "api", "python", "/root/api/main.py"]
Flask API uses port 5000. I’ve tried to add EXPOSE 5000 to dockerfile, but I didn’t find any difference.
It builds without error, but I’m still not sure everything is correct. So I run it locally on my PC to test. But I can’t access it. I’ve tested http://172.17.0.2/api and http://localhost/api, but it didn’t respond. Also I’ve tried to run main.py in container terminal, but it says "Port 5000 is in use by another program".
So here is what I would like to ask:
- How to properly run Flask API on Linux? Am I doing it right?
- How to properly run Flask API in Docker? Am I doing it right?
- How to access Flask API running on localhost Docker container (same PC)? I just can’t understand what ip/address my API gets.
Answers:
Here is a best solution I found.
Dockerfile:
FROM tensorflow/tensorflow:2.12.0-gpu
# Install dependencies
RUN pip install tensorflow-hub <whatever else you need>
# Copy application code
COPY /my-app-code /app
WORKDIR /app
# Expose port 5000 for Flask
EXPOSE 5000
# Set entrypoint
ENTRYPOINT ["python", "main.py"]
In your .py with Flask app you should run your application this way:
app.run(host="0.0.0.0", port="5000")
I use tensorflow image, because otherwise I’ll miss nvidia drivers and containter won’t see GPU. I don’t use nvidia container because this could have version conflict with tensorflow version I’m using.
I had to move from miniconda as it brings lot of complexity. However it’s possible to run Flask API on it. Here is working dockerfile:
FROM continuumio/miniconda3
# Install base utilities
RUN apt-get update
&& apt-get install -y wget
&& rm -rf /var/lib/apt/lists/*
# flask app code is in "my-app-code" folder near with dockerfile
COPY /my-app-code /app
RUN echo "Running $(conda --version)"
RUN conda update conda
RUN conda create -n app-env python=3.9
RUN echo "conda activate app-env" >> ~/.bashrc
SHELL ["/bin/bash", "--login", "-c"]
RUN conda activate app-env
RUN conda install flask requests
EXPOSE 5000
ENTRYPOINT ["conda", "run", "-n", "app-env", "python", "/app/main.py"]
Don’t forget to add host and port to your app.run as showed above.
How to access Flask API running on localhost docker container?
I created miniconda docker image with Flask API on it.
Dockerfile is:
FROM continuumio/miniconda3
# Install base utilities
RUN apt-get update
&& apt-get install -y wget
&& rm -rf /var/lib/apt/lists/*
COPY api /root/api
RUN echo "Running $(conda --version)"
RUN conda update conda
RUN conda create -n api python=3.9
RUN echo "conda activate api" >> ~/.bashrc
SHELL ["/bin/bash", "--login", "-c"]
RUN conda activate api
RUN conda install flask requests
ENTRYPOINT ["conda", "run", "-n", "api", "python", "/root/api/main.py"]
Flask API uses port 5000. I’ve tried to add EXPOSE 5000 to dockerfile, but I didn’t find any difference.
It builds without error, but I’m still not sure everything is correct. So I run it locally on my PC to test. But I can’t access it. I’ve tested http://172.17.0.2/api and http://localhost/api, but it didn’t respond. Also I’ve tried to run main.py in container terminal, but it says "Port 5000 is in use by another program".
So here is what I would like to ask:
- How to properly run Flask API on Linux? Am I doing it right?
- How to properly run Flask API in Docker? Am I doing it right?
- How to access Flask API running on localhost Docker container (same PC)? I just can’t understand what ip/address my API gets.
Here is a best solution I found.
Dockerfile:
FROM tensorflow/tensorflow:2.12.0-gpu
# Install dependencies
RUN pip install tensorflow-hub <whatever else you need>
# Copy application code
COPY /my-app-code /app
WORKDIR /app
# Expose port 5000 for Flask
EXPOSE 5000
# Set entrypoint
ENTRYPOINT ["python", "main.py"]
In your .py with Flask app you should run your application this way:
app.run(host="0.0.0.0", port="5000")
I use tensorflow image, because otherwise I’ll miss nvidia drivers and containter won’t see GPU. I don’t use nvidia container because this could have version conflict with tensorflow version I’m using.
I had to move from miniconda as it brings lot of complexity. However it’s possible to run Flask API on it. Here is working dockerfile:
FROM continuumio/miniconda3
# Install base utilities
RUN apt-get update
&& apt-get install -y wget
&& rm -rf /var/lib/apt/lists/*
# flask app code is in "my-app-code" folder near with dockerfile
COPY /my-app-code /app
RUN echo "Running $(conda --version)"
RUN conda update conda
RUN conda create -n app-env python=3.9
RUN echo "conda activate app-env" >> ~/.bashrc
SHELL ["/bin/bash", "--login", "-c"]
RUN conda activate app-env
RUN conda install flask requests
EXPOSE 5000
ENTRYPOINT ["conda", "run", "-n", "app-env", "python", "/app/main.py"]
Don’t forget to add host and port to your app.run as showed above.