How to Launch an Experiment from Python

Overview

Since v2.0, NNI provides a new way to launch the experiments. Before that, you need to configure the experiment in the YAML configuration file and then use the nnictl command to launch the experiment. Now, you can also configure and run experiments directly in the Python file. If you are familiar with Python programming, this will undoubtedly bring you more convenience.

Run a New Experiment

After successfully installing nni and prepare the trial code, you can start the experiment with a Python script in the following 2 steps.

Step 1 - Initialize an experiment instance and configure it

from nni.experiment import Experiment
experiment = Experiment('local')

Now, you have a Experiment instance, and this experiment will launch trials on your local machine due to training_service='local'.

See all training services supported in NNI.

experiment.config.experiment_name = 'MNIST example'
experiment.config.trial_concurrency = 2
experiment.config.max_trial_number = 10
experiment.config.search_space = search_space
experiment.config.trial_command = 'python3 mnist.py'
experiment.config.trial_code_directory = Path(__file__).parent
experiment.config.tuner.name = 'TPE'
experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
experiment.config.training_service.use_active_gpu = True

Use the form like experiment.config.foo = 'bar' to configure your experiment.

See all real builtin tuners supported in NNI.

See configuration reference for more detailed usage of these fields.

Step 2 - Just run

experiment.run(port=8080)

Now, you have successfully launched an NNI experiment. And you can type localhost:8080 in your browser to observe your experiment in real time.

In this way, experiment will run in the foreground and will automatically exit when the experiment finished.

Note

If you want to run an experiment in an interactive way, use start() in Step 2. If you launch the experiment in Python script, please use run(), as start() is designed for the interactive scenarios.

Example

Below is an example for this new launching approach. You can find this code in mnist-tfv2/launch.py.

from pathlib import Path

from nni.experiment import Experiment

search_space = {
    "dropout_rate": { "_type": "uniform", "_value": [0.5, 0.9] },
    "conv_size": { "_type": "choice", "_value": [2, 3, 5, 7] },
    "hidden_size": { "_type": "choice", "_value": [124, 512, 1024] },
    "batch_size": { "_type": "choice", "_value": [16, 32] },
    "learning_rate": { "_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1] }
}

experiment = Experiment('local')
experiment.config.experiment_name = 'MNIST example'
experiment.config.trial_concurrency = 2
experiment.config.max_trial_number = 10
experiment.config.search_space = search_space
experiment.config.trial_command = 'python3 mnist.py'
experiment.config.trial_code_directory = Path(__file__).parent
experiment.config.tuner.name = 'TPE'
experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
experiment.config.training_service.use_active_gpu = True

experiment.run(8080)

Start and Manage a New Experiment

NNI migrates the API in NNI Client to this new launching approach. Launch the experiment by start() instead of run(), then you can use these APIs in interactive mode.

Please refer to example usage and code file python_api_start.ipynb.

Note

run() polls the experiment status and will automatically call stop() when the experiment finished. start() just launched a new experiment, so you need to manually stop the experiment by calling stop().

Connect and Manage an Exist Experiment

If you launch an experiment by nnictl and also want to use these APIs, you can use Experiment.connect() to connect to an existing experiment.

Please refer to example usage and code file python_api_connect.ipynb.

Note

You can use stop() to stop the experiment when connecting to an existing experiment.

Resume/View and Manage a Stopped Experiment

You can use Experiment.resume() and Experiment.view() to resume and view a stopped experiment, these functions behave like nnictl resume and nnictl view.

If you want to manage the experiment, set wait_completion as False and the functions will return an Experiment instance. For more parameters, please refer to API reference.

API Reference

Detailed usage could be found here.

Experiment

Experiment Config

Algorithm Config

Training Service Config

Local Config

Remote Config

Openpai Config

AML Config

Shared Storage Config

Nfs Config

Azure Blob Config