# FIXME: For demonstration only. It should not be here
from pathlib import Path
from nni.experiment import Experiment
from nni.algorithms.hpo.hyperopt_tuner import HyperoptTuner
tuner = HyperoptTuner('tpe')
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(tuner, 'local')
experiment.config.experiment_name = 'test'
experiment.config.trial_concurrency = 2
experiment.config.max_trial_number = 5
experiment.config.search_space = search_space
experiment.config.trial_command = 'python3 mnist.py'
experiment.config.trial_code_directory = Path(__file__).parent
experiment.config.training_service.use_active_gpu = True
experiment.run(8081)
# Define search space
search_space = {
'features': {
'_type': 'choice',
'_value': [128, 256, 512, 1024]
},
'lr': {
'_type': 'loguniform',
'_value': [0.0001, 0.1]
},
'momentum': {
'_type': 'uniform',
'_value': [0, 1]
},
}
# Configure experiment
experiment = Experiment('local')
experiment.config.trial_command = 'python model.py'
experiment.config.trial_code_directory = Path(__file__).parent
experiment.config.search_space = search_space
experiment.config.tuner.name = 'Random'
experiment.config.max_trial_number = 10
experiment.config.trial_concurrency = 2
# Run it!
experiment.run(port=8080, wait_completion=False)
print('Experiment is running. Press Ctrl-C to quit.')
signal.pause()
},
"batch_size": {
"_type": "choice",
"_value": [16, 32]
},
"learning_rate": {
"_type": "choice",
"_value": [0.0001, 0.001, 0.01, 0.1]
}
}
experiment = Experiment(['local', 'remote'])
experiment.config.experiment_name = 'test'
experiment.config.trial_concurrency = 3
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[0].use_active_gpu = True
experiment.config.training_service[1].reuse_mode = True
rm_conf = RemoteMachineConfig()
rm_conf.host = '10.1.1.1'
rm_conf.user = '******'
rm_conf.password = '******'
rm_conf.port = 22
experiment.config.training_service[1].machine_list = [rm_conf]
experiment.run(26780, debug=True)
# If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
# the experiment will run forever until you press Ctrl-C.
#
# .. note::
#
# ``max_trial_number`` is set to 10 here for a fast example.
# In real world it should be set to a larger number.
# With default config TPE tuner requires 20 trials to warm up.
# %%
# Step 4: Run the experiment
# --------------------------
# Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
#
# You can use the web portal to view experiment status: http://localhost:8080.
experiment.run(8080)
# %%
# After the experiment is done
# ----------------------------
# Everything is done and it is safe to exit now. The following are optional.
#
# If you are using standard Python instead of Jupyter Notebook,
# you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
# allowing you to view the web portal after the experiment is done.
# input('Press enter to quit')
experiment.stop()
# %%
# :meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,