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10.4 Running everything locally with Docker-compose

Slides

Docker Compose is a tool that help us to define and share multi-container applications. With Compose, we can create a YAML file to define the services (in our case it is gateway service and clothing-model model) and with a single command, we can spin everything up or tear it all down. Docker compose is very useful the test the applications locally.

Instead to mapping the volumn, port, and then running the docker in the terminal for our tf-serving model (clothing-model), we want to create dockker image and put everything in there. For this we want to create docker image by the name image-model.dockerfile:

FROM tensorflow/serving:2.7.0

# Copy model in the image
COPY clothing-model /models/clothing-model/1
# Specify environmental variable
ENV MODEL_NAME="clothing-model"

To build the image we also need to specify the dockerfile name along with the tag, for example, docker build -t clothing-model:xception-v4-001 -f image-model.dockerfile .

Since we have created the dockerfile to the image we can simply run the image with docker run -it --rm -p 8500:8500 clothing-model:xception-v4-001

Similarly we can do the same thing for our gateway service. The file name is image-gateway.dockerfile:

FROM python:3.8.12-slim

RUN pip install pipenv

# Create working directory in docker image
WORKDIR /app

# Copy Pipfile and Pipfile.lock files in working dir
COPY ["Pipfile", "Pipfile.lock", "./"]

# Install required packages using pipenv
RUN pipenv install --system --deploy

# Copy gateway and protobuf scripts in the working dir
COPY ["gateway.py", "protobuf.py", "./"]

EXPOSE 9696

ENTRYPOINT ["gunicorn", "--bind=0.0.0.0:9696", "gateway:app"]

Build image: docker build -t clothing-model-gateway:001 -f image-gateway.dockerfile . Run image: docker run -it --rm -p 9696:9696 clothing-gateway:001

Upon running these two containers and test for prediction, we should expect connection error. This is because the gateway service is unable to communicate with tf-serving. In order to connect the two containers and work simultaneously we need docker compose. Docker compose require yaml file which will be executed when running the commands from docker compose, usually the file is names as docker-compose.yaml:

version: "3.9"
services:
  clothing-model: # tf-serving model
    image: zoomcamp-10-model:xception-v4-001
  gateway: # flask gateway service
    image: zoomcamp-10-gateway:002 # new version
    environment:
      - TF_SERVING_HOST=clothing-model:8500 # look for clothing model and port 8500
    ports: # map host machine with gateway
      - "9696:9696"

Now we also need to make slight changes in the gateway.py to make the environment variable configurable and assign it to the host. This can be done using host = os.getenv('TF_SERVING_HOST', 'localhost:8500')

Running the command docker-compose up will establish this connection between both images and everything is configured properly we should have the request predictions.

Useful commands

  • docker-compose up: run docker compose
  • docker-compose up -d: run docker compose in detached mode
  • docker ps: to see the running containers
  • docker-compose down: stop the docker compose

Notes

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