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Docker Build Fails on ARM — Architecture Mismatch Between Build and Deploy

Symptom

• exec /bin/sh: exec format error when running container on server
• Image built on M1/M2 Mac, fails on AWS EC2 Linux (x86_64)
• WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64)
• CI/CD fails: base image has no arm64 variant
• Works locally (Mac), fails in production (Linux)

Root Cause

Modern Macs use Apple Silicon (ARM / linux/arm64). Most cloud servers run x86_64 (linux/amd64). Docker images are architecture-specific. Building on ARM produces ARM-only images that can’t run on x86_64 servers — and vice versa.

Fix

Option 1: Build for target architecture explicitly

# Build for amd64 specifically (even on ARM Mac)
docker build --platform linux/amd64 -t my-agent:latest .

# Or specify in docker-compose.yml
# docker-compose.yml:
# services:
#   agent:
#     build:
#       context: .
#       platforms:
#         - linux/amd64

Option 2: Build multi-platform image with buildx

# Set up buildx builder with multi-platform support
docker buildx create --name multiarch --driver docker-container --use
docker buildx inspect --bootstrap

# Build and push multi-arch image (supports both ARM and AMD64)
docker buildx build \
    --platform linux/amd64,linux/arm64 \
    --tag your-registry/my-agent:latest \
    --push \
    .

# Pull the right architecture automatically on any platform
docker pull your-registry/my-agent:latest  # Gets arm64 on Mac, amd64 on Linux

Option 3: Fix Dockerfile to use multi-arch base images

# BAD — single arch base image
FROM ubuntu:22.04-amd64  # Only works on x86_64

# GOOD — multi-arch image (Docker pulls correct arch automatically)
FROM ubuntu:22.04  # Has linux/amd64, linux/arm64, linux/arm/v7 variants

# GOOD — Python multi-arch
FROM python:3.12-slim  # Supports amd64, arm64, arm/v7

# For AI/ML workloads — check platform-specific builds
# PyTorch has separate wheels for CUDA (amd64) vs MPS (arm64)
FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime  # amd64 + CUDA only
# vs
FROM python:3.12-slim  # Then install appropriate torch variant at runtime

Option 4: Conditional dependencies by platform in Dockerfile

FROM python:3.12-slim

# Install platform-specific dependencies
ARG TARGETPLATFORM
ARG TARGETARCH

RUN echo "Building for: $TARGETPLATFORM ($TARGETARCH)"

# Example: different packages for ARM vs AMD64
RUN if [ "$TARGETARCH" = "amd64" ]; then \
        pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu; \
    elif [ "$TARGETARCH" = "arm64" ]; then \
        pip install torch==2.1.0; \
    fi

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . /app
WORKDIR /app
CMD ["python", "agent.py"]

Option 5: GitHub Actions multi-arch CI/CD

# .github/workflows/docker.yml
name: Build and push multi-arch Docker image

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up QEMU (enables cross-platform builds)
        uses: docker/setup-qemu-action@v3

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: $
          password: $

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          platforms: linux/amd64,linux/arm64
          push: true
          tags: yourname/my-agent:latest
          cache-from: type=gha
          cache-to: type=gha,mode=max

Option 6: Check and diagnose architecture issues

# Check what architecture your image was built for
docker inspect my-agent:latest | jq '.[0].Architecture'
# "arm64" or "amd64"

# Check what your current machine is
uname -m
# "arm64" (Apple Silicon) or "x86_64" (Intel/AMD)

# Check available platforms for a Docker Hub image
docker manifest inspect python:3.12-slim | jq '.manifests[].platform'

# Run amd64 image on ARM Mac (using emulation — slower but works for testing)
docker run --platform linux/amd64 my-agent:latest

Architecture Quick Reference

Machine	Architecture	Docker platform
Apple M1/M2/M3/M4	ARM64	linux/arm64
Intel Mac	x86_64	linux/amd64
AWS EC2 (most)	x86_64	linux/amd64
AWS Graviton	ARM64	linux/arm64
Raspberry Pi 4	ARM64/ARM	linux/arm64 or linux/arm/v7
Google Cloud (most)	x86_64	linux/amd64

Expected Token Savings

Debugging “exec format error” without knowing about architecture: ~6,000 tokens Using --platform linux/amd64 from the start: 0 wasted

Environment

• Teams developing on Apple Silicon and deploying to x86 cloud infrastructure
• Source: direct experience; extremely common since Apple Silicon adoption

Wasting tokens on this error?

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