def setUp(self):
self.loggers = {
"online": CometLoggerCallback(),
"offline": CometLoggerCallback(online=False)
}
self.trial = MockTrial({"p1": 1}, "trial_1", 1, "artifact")
def test_kwargs_passthrough(self, experiment):
"""Test that additional keyword arguments to CometLoggerCallback get
passed through to comet_ml.Experiment on log_trial_start
"""
experiment_kwargs = {"kwarg_1": "val_1"}
logger = CometLoggerCallback(**experiment_kwargs)
trial = MockTrial({"parameter": 1}, "trial2", 1, "artifact")
logger.log_trial_start(trial)
# These are the default kwargs that get passed to create the experiment
expected_kwargs = {kwarg: False for kwarg in logger._exclude_autolog}
expected_kwargs.update(experiment_kwargs)
experiment.assert_called_with(**expected_kwargs)
def setUp(self):
self.logger = CometLoggerCallback()
self.trials = [
MockTrial({"p1": 1}, "trial_1", 1, "artifact"),
MockTrial({"p1": 2}, "trial_2", 2, "artifact"),
MockTrial({"p1": 2}, "trial_3", 3, "artifact"),
]
def tune_function(api_key=None, project_name=None):
analysis = tune.run(
train_function,
name="comet",
metric="loss",
mode="min",
callbacks=[
CometLoggerCallback(api_key=api_key,
project_name=project_name,
tags=["comet_example"])
],
config={
"mean": tune.grid_search([1, 2, 3]),
"sd": tune.uniform(0.2, 0.8)
},
)
return analysis.best_config
def test_configure_experiment_defaults(self):
"""Test CometLoggerCallback._configure_experiment_defaults."""
logger = self.logger
# Test that autologging features are properly disabled
exclude = CometLoggerCallback._exclude_autolog
for option in exclude:
self.assertFalse(logger.experiment_kwargs.get(option))
del logger
# Don't disable logging if user overwrites defaults by passing in args
for include_option in exclude:
# This unpacks to become e.g. CometLoggerCallback(log_env_cpu=True)
logger = CometLoggerCallback(**{include_option: True})
for option in exclude:
if option == include_option:
self.assertTrue(logger.experiment_kwargs.get(option))
else:
self.assertFalse(logger.experiment_kwargs.get(option))
def train_model(train_dataset: ray.data.Dataset, comet_project: str) -> Result:
"""Train a simple XGBoost model and return the result."""
trainer = XGBoostTrainer(
scaling_config={"num_workers": 2},
params={"tree_method": "auto"},
label_column="target",
datasets={"train": train_dataset},
num_boost_round=10,
run_config=RunConfig(callbacks=[
# This is the part needed to enable logging to Comet ML.
# It assumes Comet ML can find a valid API (e.g. by setting
# the ``COMET_API_KEY`` environment variable).
CometLoggerCallback(
project_name=comet_project,
save_checkpoints=True,
)
]),
)
result = trainer.fit()
return result
def setUp(self):
self.logger = CometLoggerCallback()
self.trials = [
MockTrial({"p1": 1}, "trial_1", 1, "artifact"),
MockTrial({"p1": 2}, "trial_2", 1, "artifact")
]
self.result = {
"config": {
"p1": 1
},
"node_ip": "0.0.0.0",
"hostname": "hostname_val",
"pid": "1234",
"date": "2000-01-01",
"experiment_id": "1234",
"trial_id": 1,
"experiment_tag": "tag1",
"hist_stats/episode_reward": [1, 0, 1, -1, 0, 1],
"hist_stats/episode_lengths": [1, 2, 3, 4, 5, 6],
"metric1": 0.8,
"metric2": 1,
"metric3": None,
"training_iteration": 0
}
def setUp(self):
self.logger = CometLoggerCallback()
