def __init__(
self,
metric_names: List[str] = None,
log_on_batch_end: bool = True,
log_on_epoch_end: bool = True,
**logging_params,
):
"""
Args:
metric_names (List[str]): list of metric names to log,
if none - logs everything
log_on_batch_end (bool): logs per-batch metrics if set True
log_on_epoch_end (bool): logs per-epoch metrics if set True
"""
super().__init__(
order=CallbackOrder.Logging,
node=CallbackNode.Master,
type=CallbackType.Experiment,
)
self.metrics_to_log = metric_names
self.log_on_batch_end = log_on_batch_end
self.log_on_epoch_end = log_on_epoch_end
if not (self.log_on_batch_end or self.log_on_epoch_end):
raise ValueError("You have to log something!")
if (self.log_on_batch_end and not self.log_on_epoch_end) \
or (not self.log_on_batch_end and self.log_on_epoch_end):
self.batch_log_suffix = ""
self.epoch_log_suffix = ""
else:
self.batch_log_suffix = "_batch"
self.epoch_log_suffix = "_epoch"
self.logger = Logger(**logging_params)
def _pre_experiment_hook(self, experiment: Experiment):
monitoring_params = experiment.monitoring_params
log_on_batch_end: bool = monitoring_params.pop("log_on_batch_end",
False)
log_on_epoch_end: bool = monitoring_params.pop("log_on_epoch_end",
True)
self._init(
log_on_batch_end=log_on_batch_end,
log_on_epoch_end=log_on_epoch_end,
)
self.logger = Logger(**monitoring_params)
import random
from alchemy import Logger
# insert your personal token here
token = "..."
project = "default"
for gid in range(1):
group = f"group_{gid}"
for eid in range(2):
experiment = f"experiment_{eid}"
logger = Logger(
token=token,
experiment=experiment,
group=group,
project=project,
)
for mid in range(4):
metric = f"metric_{mid}"
# let's sample some random data
n = 300
x = random.randint(-10, 10)
for _ in range(n):
logger.log_scalar(metric, x)
x += random.randint(-1, 1)
logger.close()