RemoteMachineMode.md

# 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](../Tutorial/ExperimentConfig.md).

* 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

* Follow [installation](../Tutorial/InstallationLinux.md) to install NNI on the remote machine.

### Windows

* Follow [installation](../Tutorial/InstallationWin.md) 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.

  ```bat
  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.

  ```text
  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`:

```yaml
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:

```bash
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`:

```yaml
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:

```yaml
# 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:

```yaml
# 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:

```yaml
# 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:

```yaml
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`.