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Run an Experiment on Remote Machines

NNI can run one experiment on multiple remote machines through SSH, called remote mode. It's like a lightweight training platform. In this mode, NNI can be started from your computer, and dispatch trials to remote machines in parallel.

The OS of remote machines supports Linux, Windows 10, and Windows Server 2019.

Requirements

  • Make sure the default environment of remote machines meets requirements of your trial code. If the default environment does not meet the requirements, the setup script can be added into command field of NNI config.
  • Make sure remote machines can be accessed through SSH from the machine which runs nnictl command. It supports both password and key authentication of SSH. For advanced usages, please refer to machineList part of configuration.
  • Make sure the NNI version on each machine is consistent.
  • Make sure the command of Trial is compatible with remote OSes, if you want to use remote Linux and Windows together. For example, the default python 3.x executable called python3 on Linux, and python on Windows.

Linux

Windows

  • Follow installation to install NNI on the remote machine.

  • Install and start OpenSSH Server.

    1. Open Settings app on Windows.
    2. Click Apps, then click Optional features.
    3. Click Add a feature, search and select OpenSSH Server, and then click Install.
    4. Once it's installed, run below command to start and set to automatic start.
    sc config sshd start=auto
net start sshd

  • Make sure remote account is administrator, so that it can stop running trials.

  • Make sure there is no welcome message more than default, since it causes ssh2 failed in NodeJs. For example, if you're using Data Science VM on Azure, it needs to remove extra echo commands in C:\dsvm\tools\setup\welcome.bat.

    The output like below is ok, when opening a new command window.

    Microsoft Windows [Version 10.0.17763.1192]
(c) 2018 Microsoft Corporation. All rights reserved.

(py37_default) C:\Users\AzureUser>

Run an experiment

e.g. there are three machines, which can be logged in with username and password.

IP Username Password
10.1.1.1 bob bob123
10.1.1.2 bob bob123
10.1.1.3 bob bob123

Install and run NNI on one of those three machines or another machine, which has network access to them.

Use examples/trials/mnist-annotation as the example. Below is content of examples/trials/mnist-annotation/config_remote.yml:

authorName: default
experimentName: example_mnist
trialConcurrency: 1
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai
trainingServicePlatform: remote
# search space file
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: true
tuner:
  #choice: TPE, Random, Anneal, Evolution, BatchTuner
  #SMAC (SMAC should be installed through nnictl)
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
trial:
  command: python3 mnist.py
  codeDir: .
  gpuNum: 0
#machineList can be empty if the platform is local
machineList:
  - ip: 10.1.1.1
    username: bob
    passwd: bob123
    #port can be skip if using default ssh port 22
    #port: 22
  - ip: 10.1.1.2
    username: bob
    passwd: bob123
  - ip: 10.1.1.3
    username: bob
    passwd: bob123

Files in codeDir will be uploaded to remote machines automatically. You can run below command on Windows, Linux, or macOS to spawn trials on remote Linux machines:

nnictl create --config examples/trials/mnist-annotation/config_remote.yml

Configure python environment

By default, commands and scripts will be executed in the default environment in remote machine. If there are multiple python virtual environments in your remote machine, and you want to run experiments in a specific environment, then use preCommand to specify a python environment on your remote machine.

Use examples/trials/mnist-tfv2 as the example. Below is content of examples/trials/mnist-tfv2/config_remote.yml:

authorName: default
experimentName: example_mnist
trialConcurrency: 1
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai
trainingServicePlatform: remote
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
  #SMAC (SMAC should be installed through nnictl)
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
trial:
  command: python3 mnist.py
  codeDir: .
  gpuNum: 0
#machineList can be empty if the platform is local
machineList:
  - ip: ${replace_to_your_remote_machine_ip}
    username: ${replace_to_your_remote_machine_username}
    sshKeyPath: ${replace_to_your_remote_machine_sshKeyPath}
    # Pre-command will be executed before the remote machine executes other commands.
    # Below is an example of specifying python environment.
    # If you want to execute multiple commands, please use "&&" to connect them.
    # preCommand: source ${replace_to_absolute_path_recommended_here}/bin/activate
    # preCommand: source ${replace_to_conda_path}/bin/activate ${replace_to_conda_env_name}
    preCommand: export PATH=${replace_to_python_environment_path_in_your_remote_machine}:$PATH

The preCommand will be executed before the remote machine executes other commands. So you can configure python environment path like this:

# Linux remote machine
preCommand: export PATH=${replace_to_python_environment_path_in_your_remote_machine}:$PATH
# Windows remote machine
preCommand: set path=${replace_to_python_environment_path_in_your_remote_machine};%path%

Or if you want to activate the virtualenv environment:

# Linux remote machine
preCommand: source ${replace_to_absolute_path_recommended_here}/bin/activate
# Windows remote machine
preCommand: ${replace_to_absolute_path_recommended_here}\\scripts\\activate

Or if you want to activate the conda environment:

# Linux remote machine
preCommand: source ${replace_to_conda_path}/bin/activate ${replace_to_conda_env_name}
# Windows remote machine
preCommand: call activate ${replace_to_conda_env_name}

If you want multiple commands to be executed, you can use && to connect these commands:

preCommand: command1 && command2 && command3

Note: Because preCommand will execute before other commands each time, it is strongly not recommended to set preCommand that will make changes to system, i.e. mkdir or touch.