Optimize Container Images

Container optimization is key to achieving faster cold starts, reducing deployment sizes, and improving overall application performance. This guide covers Dockerfile optimization techniques, layer caching strategies, multi-stage builds, and build performance improvements to help you create efficient containers for your fal.ai applications.

Overview

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.

Note

Ensure you have Docker Buildx and BuildKit enabled in your Docker environment if you want to test your containers locally. Otherwise, you don’t need to worry about it. fal platform takes care of it for you when you deploy your application using container support.

Check out the Docker buildx documentation for more information.

General Guidelines

Note

Please also refer to the Dockerfile best practices for detailed information on Dockerfile best practices.

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 update
RUN 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:

1.3-labs

FROM python:3.9

# Use BuildKit cache for pip
RUN --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:

1.3

FROM python:3.9 AS builder
WORKDIR /app
COPY . .
RUN pip install --upgrade pip \
 && pip install -r requirements.txt

FROM python:3.9-slim
COPY --from=builder /app /app
WORKDIR /app
ENTRYPOINT ["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.

Caution

As of now, fal does not support .dockerignore files. Since we don’t allow using COPY and ADD from the host filesystem, you can ignore this step. However, we plan to add support for this in the near future. Stay tuned!

See below for more information.

Example:

__pycache__
*.pyc
*.pyo

Complete Example

Here is an example of a cache-efficient Dockerfile using the principles outlined above:

1.3

FROM python:3.9 AS base
WORKDIR /app

# Install dependencies
COPY requirements.txt ./
RUN --mount=type=cache,target=/root/.cache/pip \
    pip install --upgrade pip \
 && pip install -r requirements.txt

# Copy source files
COPY . .

# Build the application
RUN python setup.py build

# Production image
FROM python:3.9-slim
COPY --from=base /app /app
WORKDIR /app
ENTRYPOINT ["python", "app.py"]

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.