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Tutorial: Create and Run an Experiment on local with NNI API

In this tutorial, we will use the example in [~/examples/trials/mnist] to explain how to create and run an experiment on local with NNI API.

Before starts

You have an implementation for MNIST classifer using convolutional layers, the Python code is in mnist_before.py.

Step 1 - Update model codes

To enable NNI API, make the following changes:

1.1 Declare NNI API
    Include `import nni` in your trial code to use NNI APIs. 

1.2 Get predefined parameters
    Use the following code snippet: 

        RECEIVED_PARAMS = nni.get_next_parameter()

    to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example: 

        {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}

1.3 Report NNI results
    Use the API: 

        `nni.report_intermediate_result(accuracy)` 
    
    to send `accuracy` to assessor.
    
    Use the API:

        `nni.report_final_result(accuracy)` 
        
    to send `accuracy` to tuner.

We had made the changes and saved it to mnist.py.

NOTE:

accuracy - The `accuracy` could be any python object, but  if you use NNI built-in tuner/assessor, `accuracy` should be a numerical variable (e.g. float, int).
assessor - The assessor will decide which trial should early stop based on the history performance of trial (intermediate result of one trial).
tuner    - The tuner will generate next parameters/architecture based on the explore history (final result of all trials).

Step 2 - Define SearchSpace

The hyper-parameters used in Step 1.2 - Get predefined parameters is defined in a search_space.json file like below:

{
    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
}

Refer to SearchSpaceSpec.md to learn more about search space.

Step 3 - Define Experiment

3.1 enable NNI API mode

To enable NNI API mode, you need to set useAnnotation to false and provide the path of SearchSpace file (you just defined in step 1):

useAnnotation: false
searchSpacePath: /path/to/your/search_space.json

To run an experiment in NNI, you only needed:

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

Prepare trial:

A set of examples can be found in ~/nni/examples after your installation, run ls ~/nni/examples/trials to see all the trial examples.

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:

  python ~/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 (the spec of builtin tuners can be found here), 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: 1

# maximum experiment running duration
maxExecDuration: 3h

# empty means never stop
maxTrialNum: 100

# choice: local, remote  
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. Oher than nnictl, NNI also provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features.

Using multiple local GPUs to speed up search

The following steps assume that you have 4 NVIDIA GPUs installed at local and tensorflow with GPU support. The demo enables 4 concurrent trail jobs and each trail job uses 1 GPU.

Prepare configure file: NNI provides a demo configuration file for the setting above, cat ~/nni/examples/trials/mnist-annotation/config_gpu.yml to see it. The trailConcurrency and gpuNum are different from the basic configure file:

...

# how many trials could be concurrently running
trialConcurrency: 4

...

trial:
  command: python mnist.py
  codeDir: ~/nni/examples/trials/mnist-annotation
  gpuNum: 1

We can run the experiment with the following command:

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

You can use nnictl command line tool or WebUI to trace the training progress. nvidia_smi command line tool can also help you to monitor the GPU usage during training.