def test_mlflow_bad_metric_name_handling(dirname):
import mlflow
true_values = [123.0, 23.4, 333.4]
with MLflowLogger(os.path.join(dirname, "mlruns")) as mlflow_logger:
active_run = mlflow.active_run()
handler = OutputHandler(tag="training", metric_names="all")
engine = Engine(lambda e, b: None)
engine.state = State(metrics={
"metric:0 in %": 123.0,
"metric 0": 1000.0,
})
with pytest.warns(UserWarning, match=r"MLflowLogger output_handler encountered an invalid metric name"):
engine.state.epoch = 1
handler(engine, mlflow_logger, event_name=Events.EPOCH_COMPLETED)
for i, v in enumerate(true_values):
engine.state.epoch += 1
engine.state.metrics['metric 0'] = v
handler(engine, mlflow_logger, event_name=Events.EPOCH_COMPLETED)
from mlflow.tracking import MlflowClient
client = MlflowClient(tracking_uri=os.path.join(dirname, "mlruns"))
stored_values = client.get_metric_history(active_run.info.run_id, "training metric 0")
for t, s in zip([1000.0, ] + true_values, stored_values):
assert t == s.value
def test_autolog_early_stopping_callback():
mlflow.paddle.autolog()
early_stopping = paddle.callbacks.EarlyStopping("loss",
mode="min",
patience=1,
min_delta=0)
with mlflow.start_run() as run:
train_model(callbacks=[early_stopping])
client = MlflowClient()
data = client.get_run(run.info.run_id).data
for param_key in ["monitor", "patience", "min_delta", "baseline"]:
assert param_key in data.params
assert data.params[param_key] == str(getattr(early_stopping,
param_key))
for metric_key in ["stopped_epoch", "best_value"]:
assert metric_key in data.metrics
assert float(data.metrics[metric_key]) == getattr(
early_stopping, metric_key)
for metric_key in ["loss", "step"]:
assert metric_key in data.metrics
metric_history = client.get_metric_history(run.info.run_id, metric_key)
assert len(metric_history) == NUM_EPOCHS
def test_autolog_logs_expected_data():
mlflow.paddle.autolog()
with mlflow.start_run() as run:
train_model()
client = MlflowClient()
data = client.get_run(run.info.run_id).data
# Testing params are logged
for param_key, expected_param_value in [("optimizer_name", "Adam"),
("learning_rate", "0.01")]:
assert param_key in data.params
assert data.params[param_key] == expected_param_value
# Testing metrics are logged
for metric_key in [
"batch_size", "loss", "step", "eval_batch_size", "eval_loss",
"eval_step"
]:
assert metric_key in data.metrics
metric_history = client.get_metric_history(run.info.run_id, metric_key)
assert len(metric_history) == NUM_EPOCHS
# Testing model_summary.txt is saved
artifacts = client.list_artifacts(run.info.run_id)
assert any(x.path == "model_summary.txt" for x in artifacts)
def test_mlflow(ray_start_4_cpus, tmp_path):
config = TestConfig()
params = {"p1": "p1"}
temp_dir = tmp_path
num_workers = 4
def train_func(config):
train.report(episode_reward_mean=4)
train.report(episode_reward_mean=5)
train.report(episode_reward_mean=6)
return 1
callback = MLflowLoggerCallback(experiment_name="test_exp",
logdir=temp_dir)
trainer = Trainer(config, num_workers=num_workers)
trainer.start()
trainer.run(train_func, config=params, callbacks=[callback])
from mlflow.tracking import MlflowClient
client = MlflowClient(
tracking_uri=callback.mlflow_util._mlflow.get_tracking_uri())
experiment_id = client.get_experiment_by_name("test_exp").experiment_id
all_runs = callback.mlflow_util._mlflow.search_runs(
experiment_ids=[experiment_id])
assert len(all_runs) == 1
# all_runs is a pandas dataframe.
all_runs = all_runs.to_dict(orient="records")
run_id = all_runs[0]["run_id"]
run = client.get_run(run_id)
assert run.data.params == params
assert ("episode_reward_mean" in run.data.metrics
and run.data.metrics["episode_reward_mean"] == 6.0)
assert (TRAINING_ITERATION in run.data.metrics
and run.data.metrics[TRAINING_ITERATION] == 3.0)
metric_history = client.get_metric_history(run_id=run_id,
key="episode_reward_mean")
assert len(metric_history) == 3
iterations = [metric.step for metric in metric_history]
assert iterations == [1, 2, 3]
rewards = [metric.value for metric in metric_history]
assert rewards == [4, 5, 6]
def test_integration(dirname):
n_epochs = 5
data = list(range(50))
losses = torch.rand(n_epochs * len(data))
losses_iter = iter(losses)
def update_fn(engine, batch):
return next(losses_iter)
trainer = Engine(update_fn)
mlflow_logger = MLflowLogger(tracking_uri=os.path.join(dirname, "mlruns"))
true_values = []
def dummy_handler(engine, logger, event_name):
global_step = engine.state.get_event_attrib_value(event_name)
v = global_step * 0.1
true_values.append(v)
logger.log_metrics({"{}".format("test_value"): v}, step=global_step)
mlflow_logger.attach(trainer,
log_handler=dummy_handler,
event_name=Events.EPOCH_COMPLETED)
import mlflow
active_run = mlflow.active_run()
trainer.run(data, max_epochs=n_epochs)
mlflow_logger.close()
from mlflow.tracking import MlflowClient
client = MlflowClient(tracking_uri=os.path.join(dirname, "mlruns"))
stored_values = client.get_metric_history(active_run.info.run_id,
"test_value")
for t, s in zip(true_values, stored_values):
assert pytest.approx(t) == s.value
def _load(self) -> MetricsDict:
"""Load MlflowMetricDataSet.
Returns:
Dict[str, Union[int, float]]: Dictionary with MLflow metrics dataset.
"""
client = MlflowClient()
run_id = self.run_id
all_metrics = client._tracking_client.store.get_all_metrics(run_uuid=run_id)
dataset_metrics = filter(self._is_dataset_metric, all_metrics)
dataset = reduce(
lambda xs, x: self._update_metric(
# get_all_metrics returns last saved values per metric key.
# All values are required here.
client.get_metric_history(run_id, x.key),
xs,
),
dataset_metrics,
{},
)
return dataset
def _load(self):
self._validate_run_id()
mlflow_client = MlflowClient()
metric_history = mlflow_client.get_metric_history(
run_id=self.run_id,
key=self.key) # gets active run if no run_id was given
# the metric history is always a list of mlflow.entities.metric.Metric
# we want the value of the last one stored because this dataset only deal with one single metric
step = self._load_args.get("step")
if step is None:
# we take the last value recorded
metric_value = metric_history[-1].value
else:
# we should take the last historical value with the given step
# (it is possible to have several values with the same step)
metric_value = next(metric.value
for metric in reversed(metric_history)
if metric.step == step)
return metric_value
def _save(self, data: float):
if self._logging_activated:
self._validate_run_id()
run_id = (
self.run_id
) # we access it once instead of calling self.run_id everywhere to avoid looking or an active run each time
mlflow_client = MlflowClient()
# get the metric history if it has been saved previously to ensure
# to retrieve the right data
# reminder: this is True even if no run_id was originally specified but a run is active
metric_history = (mlflow_client.get_metric_history(
run_id=run_id, key=self.key) if self._exists() else [])
save_args = deepcopy(self._save_args)
step = save_args.pop("step", None)
if step is None:
if self.mode == "overwrite":
step = max([metric.step for metric in metric_history],
default=0)
elif self.mode == "append":
# I put a max([]) default to -1 so that default "step" equals 0
step = (max([metric.step for metric in metric_history],
default=-1) + 1)
else:
raise ValueError(
f"save_args['mode'] must be one of {self.SUPPORTED_SAVE_MODES}, got '{self.mode}' instead."
)
mlflow_client.log_metric(
run_id=run_id,
key=self.key,
value=data,
step=step,
**save_args,
)
def assert_are_metrics_logged(
data: Dict[str, Union[float, List[float]]],
client: MlflowClient,
run_id: str,
prefix: Optional[str] = None,
) -> bool:
"""Helper function which checks if given metrics where logged.
Args:
data: (Dict[str, Union[float, List[float]]]): Logged metrics.
client: (MlflowClient): MLflow client instance.
run_id: (str): id of run where data was logged.
prefix: (Optional[str])
"""
for key in data.keys():
metric_key = f"{prefix}.{key}" if prefix else key
metric = client.get_metric_history(run_id, metric_key)
data_len = len(data[key]) if isinstance(data[key], list) else 1
assert len(metric) == data_len
for idx, item in enumerate(metric):
data_value = (data[key][idx]["value"] if isinstance(
data[key], list) else data[key]["value"])
assert item.value == data_value and item.key == metric_key
assert True
def _load(self):
self._validate_run_id()
mode = self._load_args.get("mode", "list")
mlflow_client = MlflowClient()
metric_history = mlflow_client.get_metric_history(self.run_id,
key=self.key)
if mode == "list":
simplified_history = [metric.value for metric in metric_history]
elif mode == "dict":
simplified_history = {
metric.step: metric.value
for metric in metric_history
}
elif mode == "history":
# history is a list of dict whom keys are "log_metric" arguments. The following is equivalent to dict mode:
# [{"step": 0, "value": 0.1}, {"step": 1, "value": 0.2}, {"step": 2, "value": 0.3}]
simplified_history = [{
"step": metric.step,
"value": metric.value,
"timestamp": metric.timestamp,
} for metric in metric_history]
return simplified_history
class MlflowHelper:
def __init__(
self,
tracking_uri: str = "http://localhost:5000",
local_mlflow_dir_prefix: str = "../gsim01/mlruns/",
experiment_name: str = "Domain Guided Monitoring",
experiment_id: Optional[str] = "1",
pkl_file: Optional[Path] = None,
):
self.mlflow_client = MlflowClient(tracking_uri=tracking_uri)
self.experiment_id = experiment_id if experiment_id is not None else self.mlflow_client.get_experiment_by_name(experiment_name).experiment_id
self.local_mlflow_dir = local_mlflow_dir_prefix + str(self.experiment_id) + "/"
if pkl_file is not None and pkl_file.exists():
self.run_df = pd.read_pickle("mlflow_run_df.pkl")
print("Initialized with", len(self.run_df), "MLFlow runs from pkl")
else:
self.run_df = pd.DataFrame(columns=["info_run_id"])
self.metric_history_names: Set[str] = set()
def query_valid_runs(self,
pkl_file: Optional[Path] = None,
valid_sequence_types: List[str] = ['mimic', 'huawei_logs'],
filter_string_suffix: Optional[str] = " and params.ModelConfigrnn_type = 'gru'"):
for sequence_type in valid_sequence_types:
filter_string = "tags.sequence_type = '" + sequence_type + "'"
if filter_string_suffix is not None:
filter_string = filter_string + filter_string_suffix
self.query_runs(filter_string=filter_string)
print("Queried", len(self.run_df), "runs from MLFlow for", sequence_type)
if pkl_file is not None:
self.run_df.to_pickle(pkl_file)
def query_runs(self, filter_string: Optional[str] = None, pkl_file: Optional[Path] = None,):
runs = self.mlflow_client.search_runs(
experiment_ids=[self.experiment_id], max_results=10000, filter_string=filter_string,
)
for run in tqdm(runs, desc="Querying data per run..."):
self._handle_run(run)
if pkl_file is not None:
self.run_df.to_pickle(pkl_file)
def _handle_run(self, run):
if (
len(self.run_df) > 0
and run.info.run_id in set(self.run_df["info_run_id"])
and run.info.status == "FINISHED"
and len(
self.run_df[
(self.run_df["info_run_id"] == run.info.run_id)
& (self.run_df["info_status"] == run.info.status)
]
)
== 1
):
return
if not run.info.status == "FINISHED" and not run.info.run_id in set(
self.run_df["info_run_id"]
):
return
run_dict = {
(k + "_" + sk): v
for k, sd in run.to_dictionary().items()
for sk, v in sd.items()
}
final_run_dict = {
(k + "_" + sk): v
for k, sd in run_dict.items()
if type(sd) == type(dict())
for sk, v in sd.items()
}
final_run_dict.update(
{k: v for k, v in run_dict.items() if not (type(v) == type(dict()))}
)
if (
final_run_dict.get("data_tags_model_type", "") == "causal"
and final_run_dict.get(
"data_params_KnowledgeConfigadd_causality_prefix", "False"
)
== "True"
):
final_run_dict["data_tags_model_type"] = "causal2"
if (
(final_run_dict.get("data_tags_model_type", "") == "causal"
or final_run_dict.get("data_tags_model_type", "") == "causal2")
and final_run_dict.get("data_tags_sequence_type", "") == "huawei_logs"
and final_run_dict.get("data_params_HuaweiPreprocessorConfiglog_only_causality", "") == "True"
):
final_run_dict["data_tags_model_type"] = final_run_dict["data_tags_model_type"] + "_logonly"
if (
final_run_dict.get("data_tags_model_type", "") == "text"
and final_run_dict.get(
"data_params_KnowledgeConfigbuild_text_hierarchy", "False"
)
== "True"
):
final_run_dict["data_tags_model_type"] = "text_hierarchy"
if (
final_run_dict.get("data_tags_model_type", "") == "gram"
and final_run_dict.get("data_tags_sequence_type", "") == "huawei_logs"
and final_run_dict.get("data_params_KnowledgeConfigadd_causality_prefix")
and final_run_dict.get(
"data_params_HuaweiPreprocessorConfiguse_log_hierarchy", "False"
)
== "True"
):
final_run_dict["data_tags_model_type"] = "gram_logs"
self.run_df = self.run_df.append(
final_run_dict, ignore_index=True
).drop_duplicates(subset=["info_run_id"], keep="last", ignore_index=True)
def mimic_run_df(
self, include_noise: bool = False, include_refinements: bool = False,
risk_prediction: bool = False,
valid_x_columns: List[str]=["level_0"],
valid_y_columns: List[str]=["level_3"],
) -> pd.DataFrame:
mimic_run_df = self.run_df[
(self.run_df["data_tags_sequence_type"] == "mimic")
& (self.run_df["data_params_ModelConfigrnn_type"] == "gru")
& (self.run_df["data_params_SequenceConfigtest_percentage"].fillna("").astype(str) == "0.2")
& (self.run_df["data_params_ModelConfigbest_model_metric"] == "val_loss")
& (self.run_df["info_status"] == "FINISHED")
& (self.run_df["data_params_ModelConfigrnn_dim"] == "200")
& (self.run_df["data_params_ModelConfigoptimizer"].fillna("adam") == "adam")
& (self.run_df["data_params_ModelConfigdropout_rate"].fillna("0.0").astype(str) == "0.5")
& (self.run_df["data_params_ModelConfigrnn_dropout"].fillna("0.0").astype(str) == "0.0")
& (self.run_df["data_params_ModelConfigkernel_regularizer_scope"].fillna("[]") == "[]")
& (self.run_df["data_params_SequenceConfigpredict_full_y_sequence_wide"].astype(str).fillna("") == "True")
& (
(
(self.run_df["data_params_SequenceConfigy_sequence_column_name"].astype(str) == "level_3")
& (self.run_df["data_params_ExperimentConfigbatch_size"].astype(str).fillna("") == "128")
) |
(
(self.run_df["data_params_SequenceConfigy_sequence_column_name"].astype(str) == "level_2")
& (self.run_df["data_params_ExperimentConfigbatch_size"].astype(str).fillna("") == "16")
)
)
& (self.run_df["data_params_MimicPreprocessorConfigreplace_keys"].fillna("[]") == "[]")
]
if risk_prediction:
mimic_run_df = mimic_run_df[
(mimic_run_df["data_tags_task_type"] == "risk_prediction") &
(mimic_run_df["data_params_ModelConfigfinal_activation_function"] == "sigmoid")
]
else:
mimic_run_df = mimic_run_df[
(mimic_run_df["data_params_ModelConfigfinal_activation_function"] == "softmax")
& (mimic_run_df["data_params_SequenceConfigflatten_y"] == "True")
]
if len(valid_x_columns) > 0:
mimic_run_df = mimic_run_df[
mimic_run_df["data_params_SequenceConfigx_sequence_column_name"].apply(lambda x: x in valid_x_columns)
]
if len(valid_y_columns) > 0:
mimic_run_df = mimic_run_df[
mimic_run_df["data_params_SequenceConfigy_sequence_column_name"].apply(lambda x: x in valid_y_columns)
]
if not include_noise:
mimic_run_df = mimic_run_df[
(mimic_run_df["data_tags_noise_type"].fillna("").apply(len) == 0)
]
if not include_refinements:
mimic_run_df = mimic_run_df[
(mimic_run_df["data_tags_refinement_type"].fillna("") == "")
]
return mimic_run_df
def huawei_run_df(
self, include_noise: bool = False, include_refinements: bool = False,
risk_prediction: bool = False,
valid_x_columns: List[str]=["log_cluster_template", "fine_log_cluster_template"],
valid_y_columns: List[str]=["attributes"],
include_drain_hierarchy: bool=False,
) -> pd.DataFrame:
huawei_run_df = self.run_df[
(self.run_df["data_tags_sequence_type"] == "huawei_logs")
& (self.run_df["data_params_ModelConfigrnn_type"] == "gru")
& (self.run_df["data_params_SequenceConfigtest_percentage"].fillna("").astype(str) == "0.1")
& (self.run_df["data_params_ModelConfigbest_model_metric"] == "val_loss")
& (self.run_df["info_status"] == "FINISHED")
& (self.run_df["data_params_ModelConfigrnn_dim"] == "200")
& (self.run_df["data_params_ModelConfigoptimizer"].fillna("adam") == "adam")
& (self.run_df["data_params_ModelConfigdropout_rate"].fillna("0.0").astype(str) == "0.5")
& (self.run_df["data_params_ModelConfigrnn_dropout"].fillna("0.0").astype(str) == "0.0")
& (self.run_df["data_params_ModelConfigkernel_regularizer_scope"].fillna("[]") == "[]")
& (self.run_df["data_params_ExperimentConfigbatch_size"].astype(str).fillna("") == "128")
& (
(self.run_df["data_params_HuaweiPreprocessorConfigfine_drain_log_st"].astype(str).fillna("") == "0.75")
| (self.run_df["data_params_HuaweiPreprocessorConfigdrain_log_st"].astype(str).fillna("") == "0.75")
)
& (
(self.run_df["data_params_HuaweiPreprocessorConfigfine_drain_log_depth"].astype(str).fillna("") == "10")
| (self.run_df["data_params_HuaweiPreprocessorConfigdrain_log_depth"].astype(str).fillna("") == "10")
)
& (
(~ (
(self.run_df["data_params_SequenceConfigx_sequence_column_name"].astype(str).fillna("") == "coarse_log_cluster_template")
| (self.run_df["data_params_SequenceConfigy_sequence_column_name"].astype(str).fillna("") == "coarse_log_cluster_template")
| (self.run_df["data_params_HuaweiPreprocessorConfigdrain_log_sts"].fillna("[]").astype(str).apply(len) > 2)
)) | (
(self.run_df["data_params_HuaweiPreprocessorConfigcoarse_drain_log_st"].astype(str).fillna("") == "0.2")
& (self.run_df["data_params_HuaweiPreprocessorConfigcoarse_drain_log_depth"].astype(str).fillna("") == "4")
)
)
]
if risk_prediction:
huawei_run_df = huawei_run_df[
(huawei_run_df["data_tags_task_type"] == "risk_prediction") &
(huawei_run_df["data_params_ModelConfigfinal_activation_function"] == "sigmoid")
]
else:
huawei_run_df = huawei_run_df[
(huawei_run_df["data_params_ModelConfigfinal_activation_function"] == "softmax")
& (huawei_run_df["data_params_SequenceConfigflatten_y"] == "True")
]
if len(valid_x_columns) > 0:
huawei_run_df = huawei_run_df[
huawei_run_df["data_params_SequenceConfigx_sequence_column_name"].apply(lambda x: x in valid_x_columns)
]
if len(valid_y_columns) > 0:
huawei_run_df = huawei_run_df[
huawei_run_df["data_params_SequenceConfigy_sequence_column_name"].apply(lambda x: x in valid_y_columns)
]
if not include_noise:
huawei_run_df = huawei_run_df[
(huawei_run_df["data_tags_noise_type"].fillna("").apply(len) == 0)
]
if not include_refinements:
huawei_run_df = huawei_run_df[
(huawei_run_df["data_tags_refinement_type"].fillna("") == "")
& (huawei_run_df["data_params_HuaweiPreprocessorConfigmin_causality"].fillna(0.0).astype(str) == "0.01")
]
if not include_drain_hierarchy:
huawei_run_df = huawei_run_df[
huawei_run_df["data_params_HuaweiPreprocessorConfigdrain_log_sts"].fillna("[]").astype(str).apply(len) <= 2
]
return huawei_run_df
def _load_metrics_from_local(self, run_id: str) -> Optional[Dict[str, List[float]]]:
local_run_dir = Path(self.local_mlflow_dir + "/" + run_id + "/metrics/")
if not local_run_dir.exists() or not local_run_dir.is_dir():
return None
metric_dict: Dict[str, List[float]] = {}
for metric_file in local_run_dir.iterdir():
metric = metric_file.name
metric_history = pd.read_csv(metric_file, sep=" ", names=["time", "value", "step"]).to_dict(orient='index')
metric_dict[metric+"_history"] = [x["value"] for x in sorted(metric_history.values(), key=lambda x: x["step"])]
metric_dict[metric+"_times"] = [x["time"] for x in sorted(metric_history.values(), key=lambda x: x["step"])]
return metric_dict
def _load_metrics_from_remote(self, run_id: str) -> Dict[str, List[float]]:
run = self.mlflow_client.get_run(run_id)
metric_dict: Dict[str, Any] = {}
for metric in run.data.metrics.keys():
metric_history = self.mlflow_client.get_metric_history(
run.info.run_id, metric
)
metric_dict[metric + "_history"] = [
metric.value
for metric in sorted(metric_history, key=lambda x: x.step)
]
metric_dict[metric + "_times"] = [
metric.time
for metric in sorted(metric_history, key=lambda x: x.step)
]
return metric_dict
def load_metric_history_for_ids(
self, run_ids: Set[str],
):
metric_records = []
for run_id in tqdm(run_ids, desc="Querying metrics for runs"):
metric_dict = self._load_metrics_from_local(run_id=run_id)
if metric_dict is None:
metric_dict = self._load_metrics_from_remote(run_id=run_id)
for metric, metric_history in metric_dict.items():
for epoch in range(len(metric_history)):
metric_records.append({
"run_id": run_id,
metric: metric_history[epoch],
"epoch": epoch,
})
return pd.merge(
pd.DataFrame.from_records(metric_records), self.run_df, left_on="run_id", right_on="info_run_id", how="left"
)
def load_training_times_for_ids(
self, run_ids: Set[str], reference_metric_name: str = "val_loss_times"
):
metric_records = []
for run_id in tqdm(run_ids, desc="Querying metrics for runs"):
metric_dict = self._load_metrics_from_local(run_id=run_id)
if metric_dict is None or reference_metric_name not in metric_dict:
metric_dict = self._load_metrics_from_remote(run_id=run_id)
if reference_metric_name not in metric_dict:
print("Error! Reference Metric not in metric_dict", reference_metric_name, run_id)
continue
times = [int(x) for x in metric_dict[reference_metric_name]]
metric_records.append({
"run_id": run_id,
"num_epochs": len(times),
"total_duration": max(times) - min(times),
"avg_per_epoch": (max(times) - min(times)) / len(times),
})
return pd.merge(
pd.DataFrame.from_records(metric_records), self.run_df, left_on="run_id", right_on="info_run_id", how="inner"
)
def load_best_metrics_for_ids(
self, run_ids: Set[str], best_metric_name: str = "val_loss_history"
):
metric_records = []
for run_id in tqdm(run_ids, desc="Querying metrics for runs"):
metric_dict = self._load_metrics_from_local(run_id=run_id)
if metric_dict is None or best_metric_name not in metric_dict:
metric_dict = self._load_metrics_from_remote(run_id=run_id)
if best_metric_name not in metric_dict:
print("Error! Best Metric not in metric_dict", best_metric_name, run_id)
continue
best_epoch = [
idx
for idx, _ in sorted(
enumerate(metric_dict[best_metric_name]),
key=lambda x: x[1],
reverse=False,
)
][0]
best_metric_dict = {
metric_name + "_best": metric_dict[metric_name][best_epoch]
for metric_name in metric_dict
if len(metric_dict[metric_name]) > best_epoch
}
best_metric_dict["run_id"] = run_id
best_metric_dict["epoch"] = best_epoch
metric_records.append(best_metric_dict)
return pd.merge(
pd.DataFrame.from_records(metric_records), self.run_df, left_on="run_id", right_on="info_run_id", how="inner"
)
from mlflow.tracking import MlflowClient
if __name__ == "__main__":
def print_metric_info(history):
for m in history:
print("name: {}".format(m.key))
print("value: {}".format(m.value))
print("step: {}".format(m.step))
print("timestamp: {}".format(m.timestamp))
print("--")
# Create a run under the default experiment (whose id is "0"). Since this is low-level
# CRUD operation, the method will create a run. To end the run, you'll have
# to explicitly end it.
client = MlflowClient()
experiment_id = "0"
run = client.create_run(experiment_id)
print("run_id: {}".format(run.info.run_id))
print("--")
# Log couple of metrics, update their initial value, and fetch each
# logged metrics' history.
for k, v in [("m1", 1.5), ("m2", 2.5)]:
client.log_metric(run.info.run_id, k, v, step=0)
client.log_metric(run.info.run_id, k, v + 1, step=1)
client.set_terminated(run.info.run_id)
# run terminated, but still you can fetch the metrics
print_metric_info(client.get_metric_history(run.info.run_id, k))
