inboard 0.9.1

Docker images and utilities to power your Python APIs and help you ship faster.

🚢 inboard 🐳

Docker images and utilities to power your Python APIs and help you ship faster.

Table of Contents

• Description
• Instructions
  • Quickstart
  • Pull images
  • Use images in a Dockerfile
  • Run containers
• Configuration
  • General
  • Gunicorn and Uvicorn
  • Logging
• Development
  • Installation
  • Code style
  • Testing with pytest
  • GitHub Actions workflows
  • Running the development server
  • Building development images
  • Running development containers
  • Configuring Docker for GitHub Container Registry

Description

This repository provides Docker images and a PyPI package with useful utilities for Python web servers. It runs Uvicorn with Gunicorn, and can be used to build applications with Starlette and FastAPI.

I built this project to use as a production Python web server layer. I was working on several different software applications, and wanted a way to centrally manage the web server layer, so I didn't have to configure the server separately for each application. I also found it difficult to keep up with all the changes to the associated Python packages, including Uvicorn, Starlette, and FastAPI. I realized that I needed to abstract the web server layer into a separate project, so that when working on software applications, I could simply focus on building the applications themselves. This project is the result. It's been very helpful to me, and I hope it's helpful to you also.

This project was inspired in part by tiangolo/uvicorn-gunicorn-docker, but has the following advantages:

• One repo. The tiangolo/uvicorn-gunicorn images are in at least three separate repos (tiangolo/uvicorn-gunicorn-docker, tiangolo/uvicorn-gunicorn-fastapi-docker, and tiangolo/uvicorn-gunicorn-starlette-docker), with large amounts of code duplication, making maintenance difficult for an already-busy maintainer. This repo combines three into one.
• One Dockerfile. This repo leverages multi-stage builds to produce multiple Docker images from one Dockerfile.
• One Python requirements file. This repo uses Poetry with Poetry Extras for dependency management with a single pyproject.toml.
• One logging configuration. Logging a Uvicorn+Gunicorn+Starlette/FastAPI stack is unnecessarily complicated. Uvicorn and Gunicorn use different logging configurations, and it can be difficult to unify the log streams. In this repo, Uvicorn, Gunicorn, and FastAPI log streams are propagated to the root logger, and handled by the custom root logging config. Developers can also supply their own custom logging configurations.
• One programming language. Pure Python with no shell scripts.
• One platform. You're already on GitHub. Why not pull Docker images from GitHub Container Registry?

The PyPI package is useful if you want to use or extend any of the inboard Python modules, such as the logging configuration.

Instructions

Quickstart

Get started with Docker, pull and run an image, and try an API endpoint.

docker pull ghcr.io/br3ndonland/inboard
docker run -d -p 80:80 ghcr.io/br3ndonland/inboard
http :80  # HTTPie: https://httpie.io/

Pull images

Docker images are stored in GitHub Container Registry (GHCR), which is a Docker registry like Docker Hub. Public Docker images can be pulled anonymously from ghcr.io. Simply running docker pull ghcr.io/br3ndonland/inboard will pull the latest FastAPI image (Docker uses the latest tag by default). If specific versions of inboard or Python are desired, append the version numbers to the specified Docker tags as shown below (new in inboard version 0.6.0).

# Pull latest FastAPI image
docker pull ghcr.io/br3ndonland/inboard

# Pull latest version of each image
docker pull ghcr.io/br3ndonland/inboard:base
docker pull ghcr.io/br3ndonland/inboard:fastapi
docker pull ghcr.io/br3ndonland/inboard:starlette

# Pull image from specific release
docker pull ghcr.io/br3ndonland/inboard:base-0.6.0
docker pull ghcr.io/br3ndonland/inboard:fastapi-0.6.0
docker pull ghcr.io/br3ndonland/inboard:starlette-0.6.0

# Pull image with specific Python version
docker pull ghcr.io/br3ndonland/inboard:base-python3.8
docker pull ghcr.io/br3ndonland/inboard:fastapi-python3.8
docker pull ghcr.io/br3ndonland/inboard:starlette-python3.8

# Pull image from specific release and with specific Python version
docker pull ghcr.io/br3ndonland/inboard:base-0.6.0-python3.8
docker pull ghcr.io/br3ndonland/inboard:fastapi-0.6.0-python3.8
docker pull ghcr.io/br3ndonland/inboard:starlette-0.6.0-python3.8

If authentication to GHCR is needed, follow the instructions below.

Use images in a Dockerfile

For a Poetry project with the following directory structure:

• repo
  • package
    • main.py
    • prestart.py
  • Dockerfile
  • poetry.lock
  • pyproject.toml

The Dockerfile could look like this:

FROM ghcr.io/br3ndonland/inboard:fastapi

# Install Python requirements
COPY poetry.lock pyproject.toml /app/
WORKDIR /app/
RUN . $POETRY_HOME/env && poetry install --no-dev --no-interaction --no-root

# Install Python app
COPY package /app/package
ENV APP_MODULE=package.main:app
# RUN command already included in base image

Organizing the Dockerfile this way helps leverage the Docker build cache. Files and commands that change most frequently are added last to the Dockerfile. Next time the image is built, Docker will skip any layers that didn't change, speeding up builds.

For a standard pip install:

• repo
  • package
    • main.py
    • prestart.py
  • Dockerfile
  • requirements.txt

FROM ghcr.io/br3ndonland/inboard:fastapi

# Install Python requirements
COPY requirements.txt /app/
WORKDIR /app/
RUN python -m pip install -r requirements.txt

# Install Python app
COPY package /app/package
ENV APP_MODULE=package.main:app
# RUN command already included in base image

The image could then be built with:

cd /path/to/repo
docker build . -t imagename:latest

The final argument is the Docker image name (imagename in this example). Replace with your image name.

Run containers

Run container:

docker run -d -p 80:80 imagename

Run container with mounted volume and Uvicorn reloading for development:

cd /path/to/repo
docker run -d -p 80:80 \
  -e "LOG_LEVEL=debug" -e "PROCESS_MANAGER=uvicorn" -e "WITH_RELOAD=true" \
  -v $(pwd)/package:/app/package imagename

Details on the docker run command:

• -e "PROCESS_MANAGER=uvicorn" -e "WITH_RELOAD=true" will instruct start.py to run Uvicorn with reloading and without Gunicorn. The Gunicorn configuration won't apply, but these environment variables will still work as described:
  • APP_MODULE
  • HOST
  • PORT
  • LOG_COLORS
  • LOG_FORMAT
  • LOG_LEVEL
  • RELOAD_DIRS
  • WITH_RELOAD
• -v $(pwd)/package:/app/package: the specified directory (/path/to/repo/package in this example) will be mounted as a volume inside of the container at /app/package. When files in the working directory change, Docker and Uvicorn will sync the files to the running Docker container.

Hit an API endpoint:

docker pull ghcr.io/br3ndonland/inboard:fastapi
docker run -d -p 80:80 ghcr.io/br3ndonland/inboard:fastapi
http :80  # HTTPie: https://httpie.io/

HTTP/1.1 200 OK
content-length: 17
content-type: application/json
date: Wed, 02 Sep 2020 00:31:01 GMT
server: uvicorn

{
    "Hello": "World"
}

Configuration

To set environment variables when starting the Docker image:

docker run -d -p 80:80 -e APP_MODULE="package.custom.module:api" -e WORKERS_PER_CORE="2" myimage

To set environment variables within a Dockerfile:

FROM ghcr.io/br3ndonland/inboard:fastapi
ENV APP_MODULE="package.custom.module:api" WORKERS_PER_CORE="2"

General

• APP_MODULE: Python module with app instance. Note that the base image sets the environment variable PYTHONPATH=/app, so the module name will be relative to /app unless you supply a custom PYTHONPATH.

  • Default: The appropriate app module from inboard.

  • Custom: For a module at /app/package/custom/module.py and app instance object api, APP_MODULE="package.custom.module:api"

    # /app/package/custom/module.py
    from fastapi import FastAPI
    
    api = FastAPI()
    
    @api.get("/")
    def read_root():
        return {"message": "Hello World!"}
    

• PRE_START_PATH: Path to a pre-start script. Add a file prestart.py or prestart.sh to the application directory, and copy the directory into the Docker image as described (for a project with the Python application in repo/package, COPY package /app/package). The container will automatically detect and run the prestart script before starting the web server.

  • Default: "/app/inboard/prestart.py" (the default file provided with the Docker image)
  • Custom:
  • PRE_START_PATH="/app/package/custom_script.sh"
  • PRE_START_PATH= (set to an empty value) to disable

• PYTHONPATH: Python's search path for module files.

  • Default: PYTHONPATH="/app"
  • Custom: PYTHONPATH="/app/custom"

Gunicorn and Uvicorn

• GUNICORN_CONF: Path to a Gunicorn configuration file. The Gunicorn command-line accepts file paths instead of module paths.
  • Default:
    • "/app/inboard/gunicorn_conf.py" (the default file provided with the Docker image)
  • Custom:
    • GUNICORN_CONF="/app/package/custom_gunicorn_conf.py"
• Gunicorn worker processes: The number of Gunicorn worker processes to run is determined based on the MAX_WORKERS, WEB_CONCURRENCY, and WORKERS_PER_CORE environment variables, with a default of 1 worker per CPU core and a default minimum of 2. This is the "performance auto-tuning" feature described in tiangolo/uvicorn-gunicorn-docker.
  • MAX_WORKERS: Maximum number of workers, independent of number of CPU cores.
    • Default: not set (unlimited)
    • Custom: MAX_WORKERS="24"
  • WEB_CONCURRENCY: Total number of workers, independent of number of CPU cores.
    • Default: not set
    • Custom: WEB_CONCURRENCY="4"
  • WORKERS_PER_CORE: Number of Gunicorn workers per CPU core. Overridden if WEB_CONCURRENCY is set.
    • Default: 1
    • Custom:
      • WORKERS_PER_CORE="2": Run 2 worker processes per core (8 worker processes on a server with 4 cores).
      • WORKERS_PER_CORE="0.5" (floating point values permitted): Run 1 worker process for every 2 cores (2 worker processes on a server with 4 cores).
  • Notes:
    • The default number of workers is the number of CPU cores multiplied by the value of the environment variable WORKERS_PER_CORE (which defaults to 1). On a machine with only 1 CPU core, the default minimum number of workers is 2 to avoid poor performance and blocking, as explained in the release notes for tiangolo/uvicorn-gunicorn-docker 0.3.0.
    • If both MAX_WORKERS and WEB_CONCURRENCY are set, the least of the two will be used as the total number of workers.
    • If either MAX_WORKERS or WEB_CONCURRENCY are set to 1, the total number of workers will be 1, overriding the default minimum of 2.
• PROCESS_MANAGER: Manager for Uvicorn worker processes. As described in the Uvicorn docs, "Uvicorn includes a Gunicorn worker class allowing you to run ASGI applications, with all of Uvicorn's performance benefits, while also giving you Gunicorn's fully-featured process management."
  • Default: "gunicorn" (run Uvicorn with Gunicorn as the process manager)
  • Custom: "uvicorn" (run Uvicorn alone for local development)
• WORKER_CLASS: Uvicorn worker class for Gunicorn to use.
  • Default: uvicorn.workers.UvicornWorker
  • Custom: For the alternate Uvicorn worker, WORKER_CLASS="uvicorn.workers.UvicornH11Worker" (TODO: the H11 worker is provided for PyPy and hasn't yet been tested)
• TIMEOUT: Workers silent for more than this many seconds are killed and restarted.
  • Default: "120"
  • Custom: TIMEOUT="20"
• GRACEFUL_TIMEOUT: Number of seconds to wait for workers to finish serving requests before restart.
  • Default: "120"
  • Custom: GRACEFUL_TIMEOUT="20"
• KEEP_ALIVE: Number of seconds to wait for requests on a Keep-Alive connection.
  • Default: "5"
  • Custom: KEEP_ALIVE="20"
• HOST: Host IP address (inside of the container) where Gunicorn will listen for requests.
  • Default: "0.0.0.0"
  • Custom: TODO
• PORT: Port the container should listen on.
  • Default: "80"
  • Custom: PORT="8080"
• BIND: The actual host and port passed to Gunicorn.
  • Default: HOST:PORT ("0.0.0.0:80")
  • Custom: BIND="0.0.0.0:8080" (if custom BIND is set, overrides HOST and PORT)
• GUNICORN_CMD_ARGS: Additional command-line arguments for Gunicorn. Gunicorn looks for the GUNICORN_CMD_ARGS environment variable automatically, and gives these settings precedence over other environment variables and Gunicorn config files.
  • Custom: To use a custom TLS certificate, copy or mount the certificate and private key into the Docker image, and set --keyfile and --certfile to the location of the files.

    docker run -d -p 443:443 \
      -e GUNICORN_CMD_ARGS="--keyfile=/secrets/key.pem --certfile=/secrets/cert.pem" \
      -e PORT=443 myimage
    

• WITH_RELOAD: Configure the Uvicorn auto-reload setting.
  • Default: "false" (don't auto-reload when files change)
  • Custom: "true" (watch files with watchgod and auto-reload when files change). Auto-reloading is useful for local development. Watchgod was added as an optional dependency in Uvicorn 0.11.4, and is included with inboard.
• RELOAD_DIRS: Directories and files to watch for changes with watchgod, formatted as comma-separated string. On the command-line, this Uvicorn setting is configured by passing --reload-dir, and can be passed multiple times, with one directory each. However, when running Uvicorn programmatically, uvicorn.run accepts a list of strings (uvicorn.run(reload_dirs=["dir1", "dir2"])), so inboard will parse the environment variable, send the list to Uvicorn, and watchgod will watch each directory or file specified.
  • Default: watch all directories under project root.
  • Custom:
    • "inboard" (one directory)
    • "inboard, tests" (two directories)
    • "inboard, tests, Dockerfile" (two directories and a file)

Logging

• LOGGING_CONF: Python module containing a logging configuration dictionary object named LOGGING_CONFIG. Can be either a module path (inboard.logging_conf) or a file path (/app/inboard/logging_conf.py). The LOGGING_CONFIG dictionary will be loaded and passed to logging.config.dictConfig(). See br3ndonland/inboard#3 for more details on logging setup.

  • Default: "inboard.logging_conf" (the default module provided with inboard)

  • Custom: For a logging config module at /app/package/custom_logging.py, LOGGING_CONF="package.custom_logging" or LOGGING_CONF="/app/package/custom_logging.py".

  • If inboard is installed from PyPI with poetry add inboard or pip install inboard, the logging configuration can be easily extended or overridden. For example:

    # /app/package/custom_logging.py: set with LOGGING_CONF=package.custom_logging
    import logging
    import os
    
    from inboard import LOGGING_CONFIG
    
    # add a custom logging format: set with LOG_FORMAT=mycustomformat
    LOGGING_CONFIG["formatters"]["mycustomformat"] = {
        "format": "[%(name)s] %(levelname)s %(message)s"
    }
    
    
    class MyFormatterClass(logging.Formatter):
        """Define a custom logging format class."""
    
        def __init__(self) -> None:
            super().__init__(fmt="[%(name)s] %(levelname)s %(message)s")
    
    
    # use a custom logging format class: set with LOG_FORMAT=mycustomclass
    LOGGING_CONFIG["formatters"]["mycustomclass"] = {
        "()": "package.custom_logging.MyFormatterClass",
    }
    
    # only show access logs when running Uvicorn with LOG_LEVEL=debug
    LOGGING_CONFIG["loggers"]["gunicorn.access"] = {"propagate": False}
    LOGGING_CONFIG["loggers"]["uvicorn.access"] = {
        "propagate": str(os.getenv("LOG_LEVEL")) == "debug"
    }
    
    # don't propagate boto3 logs
    LOGGING_CONFIG["loggers"]["boto3"] = {"propagate": False}
    LOGGING_CONFIG["loggers"]["botocore"] = {"propagate": False}
    LOGGING_CONFIG["loggers"]["s3transfer"] = {"propagate": False}
    

• LOG_COLORS: Whether or not to color log messages. Currently only supported for LOG_FORMAT="uvicorn".

  • Default:
    • Auto-detected based on sys.stdout.isatty().
  • Custom:
    • LOG_COLORS="true"
    • LOG_COLORS="false"

• LOG_FORMAT: Python logging format.

  • Default:
    • "simple": Simply the log level and message.
  • Custom:
    • "verbose": The most informative format, with the first 80 characters providing metadata, and the remainder supplying the log message.
    • "gunicorn": Gunicorn's default format.
    • "uvicorn": Uvicorn's default format, similar to simple, with support for LOG_COLORS. Note that Uvicorn's access formatter is not supported here, because it frequently throws errors related to ASGI scope.

  # simple
  INFO       Started server process [19012]
  # verbose
  2020-08-19 21:07:31 -0400      19012      uvicorn.error   main            INFO       Started server process [19012]
  # gunicorn
  [2020-08-19 21:07:31 -0400] [19012] [INFO] Started server process [19012]
  # uvicorn (can also be colored)
  INFO:     Started server process [19012]
  

• LOG_LEVEL: Log level for Gunicorn or Uvicorn.

  • Default: "info"
  • Custom (organized from greatest to least amount of logging):
    • LOG_LEVEL="debug"
    • LOG_LEVEL="info"
    • LOG_LEVEL="warning"
    • LOG_LEVEL="error"
    • LOG_LEVEL="critical"

• ACCESS_LOG: Access log file to which to write.

  • Default: "-" (stdout, print in Docker logs)
  • Custom:
    • ACCESS_LOG="./path/to/accesslogfile.txt"
    • ACCESS_LOG= (set to an empty value) to disable

• ERROR_LOG: Error log file to which to write.

  • Default: "-" (stdout, print in Docker logs)
  • Custom:
    • ERROR_LOG="./path/to/errorlogfile.txt"
    • ERROR_LOG= (set to an empty value) to disable

For more information on Python logging configuration, see the Python logging how-to, Python logging cookbook, Python logging module docs, and Python logging.config module docs. Also consider Loguru, an alternative logging module with many improvements over the standard library logging module.

Development

See CONTRIBUTING.md for general information on how to contribute.

Installation

• Install Poetry (see the Poetry docs and CONTRIBUTING.md for instructions)
• Install project with all dependencies: poetry install -E fastapi

Code style

• Python code is formatted with Black. Configuration for Black is stored in pyproject.toml.

• Python imports are organized automatically with isort.

  • The isort package organizes imports in three sections:
    1. Standard library
    2. Dependencies
    3. Project
  • Within each of those groups, import statements occur first, then from statements, in alphabetical order.
  • You can run isort from the command line with poetry run isort ..
  • Configuration for isort is stored in pyproject.toml.

• Other web code (JSON, Markdown, YAML) is formatted with Prettier.

• Code style is enforced with pre-commit, which runs Git hooks.

  • Configuration is stored in .pre-commit-config.yaml.

  • Pre-commit can run locally before each commit (hence "pre-commit"), or on different Git events like pre-push.

  • Pre-commit is installed in the Poetry environment. To use:

    # after running `poetry install`
    path/to/inboard
    ❯ poetry shell
    
    # install hooks that run before each commit
    path/to/inboard
    .venv ❯ pre-commit install
    
    # and/or install hooks that run before each push
    path/to/inboard
    .venv ❯ pre-commit install --hook-type pre-push
    

  • Pre-commit is also useful as a CI tool. The hooks GitHub Actions workflow runs pre-commit hooks with GitHub Actions.

Testing with pytest

• Tests are in the tests/ directory.
• Run tests by invoking pytest from the command-line within the Poetry environment in the root directory of the repo.
• pytest features used include:
  • capturing stdout with capfd
  • fixtures
  • monkeypatch
  • parametrize
  • temporary directories and files (tmp_path and tmp_dir)
• pytest plugins include:
  • pytest-cov
  • pytest-mock
• pytest configuration is in pyproject.toml.
• FastAPI testing and Starlette testing rely on the Starlette TestClient.
• Test coverage results are reported when invoking pytest from the command-line. To see interactive HTML coverage reports, invoke pytest with pytest --cov-report=html.
• Test coverage reports are generated within GitHub Actions workflows by pytest-cov with coverage.py, and uploaded to Codecov using codecov/codecov-action. Codecov is then integrated into pull requests with the Codecov GitHub app.

GitHub Actions workflows

GitHub Actions is a continuous integration/continuous deployment (CI/CD) service that runs on GitHub repos. It replaces other services like Travis CI. Actions are grouped into workflows and stored in .github/workflows. See Getting the Gist of GitHub Actions for more info.

Running the development server

The easiest way to get started is to run the development server locally with the VSCode debugger. The debugger config is stored in launch.json. After installing the Poetry environment as described above, start the debugger. Uvicorn enables hot-reloading and addition of debug breakpoints while the server is running. The Microsoft VSCode Python extension also offers a FastAPI debugger configuration, added in version 2020.12.0, which has been customized and included in launch.json. To use it, simply select the FastAPI config and start the debugger.

As explained in the VSCode docs, if developing on Linux, note that non-root users may not be able to expose ports less than 1024.

Building development images

To build the Docker images for each stage:

git clone [e-mail]:br3ndonland/inboard.git

cd inboard

docker build . --rm --target base -t localhost/br3ndonland/inboard:base && \
docker build . --rm --target fastapi -t localhost/br3ndonland/inboard:fastapi && \
docker build . --rm --target starlette -t localhost/br3ndonland/inboard:starlette

Running development containers

# Run Docker container with Uvicorn and reloading
cd inboard

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" -e "PROCESS_MANAGER=uvicorn" -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:base

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" -e "PROCESS_MANAGER=uvicorn" -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:fastapi

docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  -e "LOG_LEVEL=debug" -e "PROCESS_MANAGER=uvicorn" -e "WITH_RELOAD=true" \
  -v $(pwd)/inboard:/app/inboard localhost/br3ndonland/inboard:starlette

# Run Docker container with Gunicorn and Uvicorn
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  localhost/br3ndonland/inboard:base
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  localhost/br3ndonland/inboard:fastapi
docker run -d -p 80:80 \
  -e "BASIC_AUTH_USERNAME=test_user" -e "BASIC_AUTH_PASSWORD=r4ndom_bUt_memorable" \
  localhost/br3ndonland/inboard:starlette

# Test HTTP Basic auth when running the FastAPI or Starlette images:
http :80/status -a test_user:r4ndom_bUt_memorable

Change the port numbers to run multiple containers simultaneously (-p 81:80).

Configuring Docker for GitHub Container Registry

If authentication is needed, follow the instructions in the GitHub docs on configuring Docker for use with GHCR. You'll need to create a personal access token (PAT). On GitHub, navigate to Settings -> Developer settings -> Personal access tokens (github.com/settings/tokens), then click "Generate new token." The token should have read:packages scope. You can then copy the token and use it with docker login:

# create PAT in GitHub and copy to clipboard

# transfer PAT from clipboard to file
pbpaste > pat-ghcr.txt

# log in with file
cat pat-ghcr.txt | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

If you don't want to store your PAT in plain text, encrypt it with PGP instead. GPG or Keybase can be used for this. Here's how to do it with Keybase:

# create PAT in GitHub and copy to clipboard

# transfer PAT from clipboard to encrypted file
pbpaste | keybase encrypt -o pat-ghcr.asc $YOUR_USERNAME

# decrypt and log in
keybase decrypt -i pat-ghcr.asc | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

# can also use keybase pgp encrypt and keybase pgp decrypt, but must export PGP key