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

      * In 1.4, either that key or this key: `pods.beta.kubernetes.io/init-containers`,can be used.
    * When you GET an object, you will see both annotation keys with the same values.
      You can safely roll back from 1.4 to 1.3, and things with init-containers
      will still work (pods, deployments, etc).
    * If you are running 1.3, only use the alpha annotation, or it may be lost when
      rolling forward.

* **Running on startup**
* ***Restarts***
* Replication (the number of processes running)
* **Memory**
* **Previous steps before starting**

## Containers and Docker { #containers-and-docker }

In the next chapter about [FastAPI in Containers - Docker](docker.md){.internal-link target=_blank} I'll explain some strategies you could use to handle the other **deployment concepts**.

## Additional volumes to minio container
extraVolumes: []

## Additional volumeMounts to minio container
extraVolumeMounts: []

## Additional sidecar containers
extraContainers: []

## Internal port number for MinIO S3 API container
## Change service.port to change external port number
minioAPIPort: "9000"

## Internal port number for MinIO Browser Console container

                                                "profile dependencyManagement"))
                                .filter(container -> container.element != null)));

        return dependencyContainers
                .map(container -> fixDuplicateDependenciesInSection(container.element, context, container.sectionName))
                .reduce(false, Boolean::logicalOr);
    }

    private static class DependencyContainer {

Two alpha features were also added in this release.

The RuntimeClass alpha feature enables a node to surface multiple runtime options to support a variety of workload types.  Examples include native linux containers, and “sandboxed” containers that isolate the container from the host kernel.

## 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 }

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

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

/// tip

Don't worry if some of these items about **containers**, Docker, or Kubernetes don't make a lot of sense yet.