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Get Started with NNI

Installation

  • Dependencies

    python >= 3.5
git
wget

    python pip should also be correctly installed. You could use "python3 -m pip -v" to check in Linux.

    • Note: we don't support virtual environment in current releases.

  • Install NNI through pip

    python3 -m pip install --user --upgrade nni

  • Install NNI through source code

    git clone -b v0.5 https://github.com/Microsoft/nni.git
cd nni
source install.sh

Quick start: run a customized experiment

An experiment is to run multiple trial jobs, each trial job tries a configuration which includes a specific neural architecture (or model) and hyper-parameter values. To run an experiment through NNI, you should:

  • Provide a runnable trial
  • Provide or choose a tuner
  • Provide a yaml experiment configure file
  • (optional) Provide or choose an assessor

Prepare trial: Let's use a simple trial example, e.g. mnist, provided by NNI. After you installed NNI, NNI examples have been put in ~/nni/examples, run ls ~/nni/examples/trials to see all the trial examples. You can simply execute the following command to run the NNI mnist example:

python3 ~/nni/examples/trials/mnist-annotation/mnist.py

This command will be filled in the yaml configure file below. Please refer to here for how to write your own trial.

Prepare tuner: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to here, but for simplicity, here we choose a tuner provided by NNI as below:

tuner:
  builtinTunerName: TPE
    classArgs:
      optimize_mode: maximize

builtinTunerName is used to specify a tuner in NNI, classArgs are the arguments pass to the tuner, optimization_mode is to indicate whether you want to maximize or minimize your trial's result.

Prepare configure file: Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the yaml configure file. NNI provides a demo configure file for each trial example, cat ~/nni/examples/trials/mnist-annotation/config.yml to see it. Its content is basically shown below:

authorName: your_name
experimentName: auto_mnist

# how many trials could be concurrently running
trialConcurrency: 2

# maximum experiment running duration
maxExecDuration: 3h

# empty means never stop
maxTrialNum: 100

# choice: local, remote, pai
trainingServicePlatform: local

# choice: true, false  
useAnnotation: true
tuner:
  builtinTunerName: TPE
  classArgs:
    optimize_mode: maximize
trial:
  command: python mnist.py
  codeDir: ~/nni/examples/trials/mnist-annotation
  gpuNum: 0

Here useAnnotation is true because this trial example uses our python annotation (refer to here for details). For trial, we should provide trialCommand which is the command to run the trial, provide trialCodeDir where the trial code is. The command will be executed in this directory. We should also provide how many GPUs a trial requires.

With all these steps done, we can run the experiment with the following command:

  nnictl create --config ~/nni/examples/trials/mnist-annotation/config.yml

You can refer to here for more usage guide of nnictl command line tool.

View experiment results

The experiment has been running now, NNI provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features. The WebUI is opened by default by nnictl create.

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