Remote Training Service

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.


  1. 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.

  2. 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 usage, please refer to RemoteConfig in reference for detailed usage.

  3. Make sure the NNI version on each machine is consistent. Follow the install guide here to install NNI.

  4. 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.

In addition, there are several steps for Windows server.

  1. 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
  1. Make sure remote account is administrator, so that it can stop running trials.

  2. 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>


Use examples/trials/mnist-pytorch as the example. Suppose there are two machines, which can be logged in with username and password or key authentication of SSH. Here is a template configuration specification.

searchSpaceFile: search_space.json
trialCommand: python3
trialGpuNumber: 0
trialConcurrency: 4
maxTrialNumber: 20
  name: TPE
    optimize_mode: maximize
  platform: remote
    - host:
      user: alice
      ssh_key_file: ~/.ssh/id_rsa
    - host:
      port: 10022
      user: bob
      password: bob123

The example configuration is saved in examples/trials/mnist-pytorch/config_remote.yml.

You can run below command on Windows, Linux, or macOS to spawn trials on remote Linux machines:

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


If you are planning to use remote machines or clusters as your training service, to avoid too much pressure on network, NNI limits the number of files to 2000 and total size to 300MB. If your trial code directory contains too many files, you can choose which files and subfolders should be excluded by adding a .nniignore file that works like a .gitignore file. For more details on how to write this file, see the git documentation.

Example: config_detailed.yml and .nniignore

More features

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 pythonPath to specify a python environment on your remote machine.

For example, with anaconda you can specify:

pythonPath: /home/bob/.conda/envs/ENV-NAME/bin

Configure shared storage

Remote training service support shared storage, which can help use your own storage during using NNI. Follow the guide here to learn how to use shared storage.

Monitor via TensorBoard

Remote training service support trial visualization via TensorBoard. Follow the guide Visualize Trial with TensorBoard to learn how to use TensorBoard.