Cache-Efficient Dockerfile Guidelines with docker buildx (or buildKit)
Under the hood, we use buildkit (or docker buildx) to build docker images. This allows us to take advantage of advanced caching mechanisms to improve build times and reduce resource consumption. In this guide, we’ll provide some guidelines for creating cache-efficient Dockerfiles.
Introduction
Building a cache-efficient Dockerfile is crucial for improving the build time and reducing resource consumption. Docker Buildx and BuildKit provide advanced features that enhance caching mechanisms. This document provides guidelines for creating such Dockerfiles.
Check out the Docker buildx documentation for more information.
General Guidelines
1. Minimize Layers
Each RUN
, COPY
, or ADD
instruction creates a new layer. Minimize the number of layers by combining commands.
Bad Example:
RUN apt-get updateRUN apt-get install -y curl
Good Example:
RUN apt-get update && apt-get install -y curl
2. Leverage Layer Caching
Order instructions from least to most frequently changing to maximize layer caching.
Example:
# Install dependencies (changes less frequently)COPY requirements.txt /app/RUN pip install -r requirements.txt
# Copy application code (changes more frequently)COPY . /app
3. Use --mount=type=cache
Utilize BuildKit’s --mount=type=cache
to cache directories across builds.
Example:
FROM python:3.9
# Use BuildKit cache for pipRUN --mount=type=cache,target=/root/.cache/pip \ pip install --upgrade pip
COPY requirements.txt /app/RUN --mount=type=cache,target=/root/.cache/pip \ pip install -r requirements.txt
COPY . /app
4. Multi-Stage Builds
Use multi-stage builds to reduce the final image size by copying only the necessary artifacts from intermediate stages.
Example:
FROM python:3.9 AS builderWORKDIR /appCOPY . .RUN pip install --upgrade pip \ && pip install -r requirements.txt
FROM python:3.9-slimCOPY --from=builder /app /appWORKDIR /appENTRYPOINT ["python", "app.py"]
5. Clean Up After Installations
Remove unnecessary files and caches after installing packages to keep the image size small.
Example:
RUN apt-get update && apt-get install -y \ build-essential \ && rm -rf /var/lib/apt/lists/*
6. Use .dockerignore
Specify files and directories to ignore during the build process to avoid unnecessary files in the build context.
Example:
__pycache__*.pyc*.pyo
Example Dockerfile
Here is an example of a cache-efficient Dockerfile using the principles outlined above:
FROM python:3.9 AS baseWORKDIR /app
# Install dependenciesCOPY requirements.txt ./RUN --mount=type=cache,target=/root/.cache/pip \ pip install --upgrade pip \ && pip install -r requirements.txt
# Copy source filesCOPY . .
# Build the applicationRUN python setup.py build
# Production imageFROM python:3.9-slimCOPY --from=base /app /appWORKDIR /appENTRYPOINT ["python", "app.py"]
fal Platform Specific Gotchas
When deploying your application on the fal platform, you don’t need to worry about enabling Docker Buildx or BuildKit. We take care of it for you. However, you can follow the guidelines mentioned above to create efficient Dockerfiles that will help speed up the build process and reduce resource consumption.
1. Interacting with the local filesystem
COPY
and ADD
(from local filesystem) are not supported as of now to copy files into the container
from the host. Instead you can use fal’s fal.toolkit
to upload files and
refer them in the container using links.
json_url = File.from_path("my-file.json", repository="cdn").url
dockerfile_str = f"""FROM python:3.11-slimRUN apt-get update && apt-get install -y curlRUN curl '{json_url}' > my-file.json"""
or you can use ADD
to directly download the file from the URL:
json_url = File.from_path("requirements.txt", repository="cdn").url
dockerfile_str = f"""FROM python:3.11-slimADD {json_url} /app/requirements.txtWORKDIR /appRUN pip install -r requirements.txt"""
Conclusion
By following these guidelines, you can create Dockerfiles that build efficiently and take full advantage of Docker Buildx and BuildKit’s caching capabilities. This will lead to faster build times and reduced resource usage.
For more detailed information, refer to the Docker documentation.