def log_metrics(self, step=None, timestamp=None, **metrics):
"""Logs multiple metrics.
```python
>>> log_metrics(step=123, loss=0.023, accuracy=0.91)
```
> It's very important to log `step` as one of your metrics
if you want to compare experiments on the dashboard
and use the steps in x-axis instead of timestamps.
Args:
step: int, optional
timestamp: datetime, optional
**metrics: **kwargs, key: value
"""
self._log_has_events()
events = []
for metric in metrics:
event_value = events_processors.metric(metrics[metric])
if event_value == UNKNOWN:
continue
events.append(
LoggedEventSpec(
name=metric,
kind=V1ArtifactKind.METRIC,
event=V1Event.make(timestamp=timestamp,
step=step,
metric=event_value),
))
if events:
self._event_logger.add_events(events)
def log_metric(self, name: str, value: float, step: int = None, timestamp=None):
"""Logs a metric datapoint.
```python
>>> log_metric(name="loss", value=0.01, step=10)
```
> It's very important to log `step` as one of your metrics
if you want to compare experiments on the dashboard
and use the steps in x-axis instead of timestamps.
Args:
name: str, metric name
value: float, metric value
step: int, optional
timestamp: datetime, optional
"""
self._log_has_events()
events = []
event_value = events_processors.metric(value)
if event_value == UNKNOWN:
return
events.append(
LoggedEventSpec(
name=name,
kind=V1ArtifactKind.METRIC,
event=V1Event.make(timestamp=timestamp, step=step, metric=event_value),
)
)
if events:
self._add_events(events)
self._results[name] = event_value
