docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"

  if [[ $(uname -s) == MSYS_NT* ]]; then

services, that scale well. It is not about just retrofitting monolithic applications onto modern container based compute environment. A cloud-native application is portable and resilient by design, and can scale horizontally by simply replicating. Modern orchestration platforms like Kubernetes, DC/OS make replicating and managing containers in huge clusters easier than ever.

While containers provide isolated application execution environment, orchestration platforms allow seamless scaling...

    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af

    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository

/// info

If you are using containers, for example with Docker or Kubernetes, I'll tell you more about that in the next chapter: [FastAPI in Containers - Docker](docker.md){.internal-link target=_blank}.

In particular, when running on **Kubernetes** you will probably **not** want to use workers and instead run **a single Uvicorn process per container**, but I'll tell you about it later in that chapter.

///

TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/ml_build_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1

TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux_2_27_x86_64

## Passos anteriores antes de iniciar e contêineres { #previous-steps-before-starting-and-containers }

Se você estiver usando contêineres (por exemplo, Docker, Kubernetes), existem duas abordagens principais que você pode usar.

### Contêineres Múltiplos { #multiple-containers }

## Предварительные шаги перед запуском и контейнеры { #previous-steps-before-starting-and-containers }

Если вы используете контейнеры (например, Docker, Kubernetes), есть два основных подхода.

### Несколько контейнеров { #multiple-containers }

Docker Compose allows defining and running single host, multi-container Docker applications.