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.
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.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¶
- ExperimentConfig.training_service: Union[nni.experiment.config.common.TrainingServiceConfig, List[nni.experiment.config.common.TrainingServiceConfig]]¶