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Module4.md

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Module 4: Build a docker container

Now we have tested the application, it's time to build a docker container!

To build a docker container, we need to create a Dockerfile, a text document that contains all the commands to assemble an image. See more reference on Dockerfile here

Instructions

  1. Create a file in the Cloud9 IDE by going to File --> New File

  2. Paste in below as the content for the Dockerfile:

    FROM python:3.6.3
COPY simulator.py simulator.py
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
CMD ["python",  "simulator.py", "--iterations", "10"]

    Take a moment to review at how a simple Docker file is written:

    FROM <image>:<tag> Sets the base image. Must be first instruction in Dockerfile.

    COPY <src> <dest> Copies new files or directories from <src> and adds them to the filesystem of the container at the path <dest>.

    RUN <command> Executes any commands in a new layer on top of the current image and commit the results.

    CMD [“exec”, “param1”, “param2”] Sets the command to be executed when running the image.

  3. Save the file as Dockerfile in the src/ folder - Make sure to captalize the D!

  4. In the terminal:

    Ensure you are in the src/ directory:

    cd ~/environment/aws-batch-monte-carlo-workshop/src

    Build the docker image with tag monte-carlo-simulator

    docker build -t monte-carlo-simulator .

  5. You should now see your newly built docker image by running:

    docker images

  6. You can run the docker image locally by

    docker run -t monte-carlo-simulator

    When you don't specify additional commands, the container will run the CMD argument specified in our Dockerfile by default. When it's done executing all the commands, the container will exit.

  7. You can override the command the container runs by doing docker run -t monte-carlo-simulator <command to run instead of default>

    For example, the below will run the ls command in the container

    docker run -t monte-carlo-simulator ls

    The below will run the python simulator.py --iterations 20 --stock AAPL command in the container

    docker run -t monte-carlo-simulator python simulator.py --iterations 20 --stock AAPL

    Or the below will run the container and give you interactive shell access to it:

    docker run -it monte-carlo-simulator /bin/bash

    For example:

    To exit out of the interactive bash, type:

    exit

  8. You may notice if try running the docker container with a s3 bucket, the run will fail:

     docker run  -i monte-carlo-simulator  python simulator.py --iterations 40 --s3_bucket <your-bucket-name>

    this is because the S3 SDK is trying to look for credentials to sign the request with. Typically in a dev environment, your credentials are stored in ~/.aws folder, but your container doesn't have access to it.

    Add the -v ~/.aws:/root/.aws flag to the command like below to mount the ~/.aws folder from the host machine into the /root/.aws folder in the container:

     docker run  -v ~/.aws:/root/.aws  -it monte-carlo-simulator  python simulator.py --iterations 50 --s3_bucket <your-bucket-name>

    Luckily, when you run the container using AWS Batch, the credential problem is automatically handled by assigning an IAM role to the container. See more info on that here

Next step

Move on to Module 5: Push the docker container to Amazon ECR