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In order to run this toolchain, the following packages have to be installed. Please note that CPU tests can be run on Mac OS and Ubuntu, while GPU tests may only be executed under Ubuntu. Unfortunately, Windows builds and tests are being done without Docker and are thus not covered by this guide.

• Docker
• docker-compose
• Python3
• Optional: Nvidia-Docker (Ubuntu only, for GPU tests) 
• Optional: GPU with Cuda Compute Capability ≥ 3.0
• Disk space: at least 100GB (150GB recommended)
• Code and Python dependencies, which are defined in ci/requirements.txt 

pip3 install -r ci/requirements.txt --user

1.1. EC2 instances with automated setup

If you plan to use an EC2 instance to reproduce the test results, you can set it up your instance with the automated setup documented in MXNet Developer setup on AWS EC2

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In this case, the stash is labelled as mkldnn_gpu. The easiest way to map this to a build-step, is by opening the corresponding the Jenkinsfile and searching for    pack_lib('mkldnn_gpu'  In this case, you will find a block like the following:

def compile_unix_mkldnn_gpu() {
  return ['GPU: MKLDNN': {
    node(NODE_LINUX_CPU) {
      ws('workspace/build-mkldnn-gpu') {
        timeout(time: max_time, unit: 'MINUTES') {
          utils.init_git()
          utils.docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_mkldnn', false)
          utils.pack_lib('mkldnn_gpu', mx_mkldnn_lib, true)
        }
      }
    }
  }]
}

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