import org.mockserver.socket.tls.KeyStoreFactory
import org.testcontainers.containers.MockServerContainer
import org.testcontainers.junit.jupiter.Container
import org.testcontainers.junit.jupiter.Testcontainers
import org.testcontainers.utility.DockerImageName

@Testcontainers
class BasicMockServerTest {
  @Container
  val mockServer: MockServerContainer = MockServerContainer(MOCKSERVER_IMAGE)

  val client =

import org.mockserver.proxyconfiguration.ProxyConfiguration
import org.mockserver.socket.tls.KeyStoreFactory
import org.testcontainers.containers.MockServerContainer
import org.testcontainers.junit.jupiter.Container
import org.testcontainers.junit.jupiter.Testcontainers

@Testcontainers
class BasicProxyTest {
  @Container
  val mockServer: MockServerContainer =
    MockServerContainer(MOCKSERVER_IMAGE)
      .withNetworkAliases("mockserver")

# Introduction

This document outlines how to make hotfix binaries and containers for MinIO?. The main focus in this article is about how to backport patches to a specific branch and finally building binaries/containers.

## Pre-pre requisite

- A working knowledge of MinIO codebase and its various components.
- A working knowledge of AWS S3 API behaviors and corner cases.

## Pre-requisite for backporting any fixes

        title: Update the RBE images to the latest container versions
        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        reviewers: mihaimaruseac,learning-to-play,nitins17
        body: |
          This PR was created by a GitHub Actions workflow to update all the SIG Build-based RBE containers to the most recent containers. See:

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...

- github.com/containerd/cgroups: [0dbf7f0 → v1.0.1](https://github.com/containerd/cgroups/compare/0dbf7f0...v1.0.1)
- github.com/containerd/containerd: [v1.4.4 → v1.4.11](https://github.com/containerd/containerd/compare/v1.4.4...v1.4.11)
- github.com/containerd/continuity: [aaeac12 → v0.1.0](https://github.com/containerd/continuity/compare/aaeac12...v0.1.0)

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

* **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**.

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

OkHttp Container Tests
======================

This module contains tests against other services