def log_metrics(self,
metrics: Dict[str, float],
step: Optional[int] = None) -> None:
# TODO: HACK figure out where this is being set to true
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
self.experiment.debug = self.debug
self.experiment.log(metrics, global_step=step)
def log_metrics(self,
metrics: Dict[str, float],
step: Optional[int] = None) -> None:
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
self.experiment.log_metrics(metrics, step)
if step is not None and (step +
1) % self._flush_logs_every_n_steps == 0:
self.save()
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
if step is not None:
self.experiment.log({**metrics, "trainer/global_step": step})
else:
self.experiment.log(metrics)
def test_add_prefix():
"""Verify add_prefix modifies the dict keys correctly."""
metrics = {"metric1": 1, "metric2": 2}
metrics = _add_prefix(metrics, "prefix", "-")
assert "prefix-metric1" in metrics
assert "prefix-metric2" in metrics
assert "metric1" not in metrics
assert "metric2" not in metrics
metrics = _add_prefix(metrics, "prefix2", "_")
assert "prefix2_prefix-metric1" in metrics
assert "prefix2_prefix-metric2" in metrics
assert "prefix-metric1" not in metrics
assert "prefix-metric2" not in metrics
assert metrics["prefix2_prefix-metric1"] == 1
assert metrics["prefix2_prefix-metric2"] == 2
def log_metrics(self, metrics: Dict[str, Union[torch.Tensor, float]], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
# Comet.ml expects metrics to be a dictionary of detached tensors on CPU
metrics_without_epoch = metrics.copy()
for key, val in metrics_without_epoch.items():
if is_tensor(val):
metrics_without_epoch[key] = val.cpu().detach()
epoch = metrics_without_epoch.pop("epoch", None)
metrics_without_epoch = _add_prefix(metrics_without_epoch, self._prefix, self.LOGGER_JOIN_CHAR)
self.experiment.log_metrics(metrics_without_epoch, step=step, epoch=epoch)
def log_metrics(self,
metrics: Dict[str, float],
step: Optional[int] = None):
assert (rank_zero_only.rank == 0
), "experiment tried to log from global_rank != 0"
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
for metric_name, metric_val in metrics.items():
if is_tensor(metric_val):
metric_val = metric_val.cpu().detach().item()
self.experiment.log(name=metric_name, val=metric_val)
self.experiment.next_step()
def log_metrics(self, metrics: Dict[str, Union[Tensor, float]], step: Optional[int] = None) -> None:
"""Log metrics (numeric values) in Neptune runs.
Args:
metrics: Dictionary with metric names as keys and measured quantities as values.
step: Step number at which the metrics should be recorded, currently ignored.
"""
if rank_zero_only.rank != 0:
raise ValueError("run tried to log from global_rank != 0")
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
for key, val in metrics.items():
# `step` is ignored because Neptune expects strictly increasing step values which
# Lightning does not always guarantee.
self.run[key].log(val)
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
for k, v in metrics.items():
if isinstance(v, Tensor):
v = v.item()
if isinstance(v, dict):
self.experiment.add_scalars(k, v, step)
else:
try:
self.experiment.add_scalar(k, v, step)
# todo: specify the possible exception
except Exception as ex:
m = f"\n you tried to log {v} which is currently not supported. Try a dict or a scalar/tensor."
raise ValueError(m) from ex
def log_metrics(self,
metrics: Dict[str, float],
step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
timestamp_ms = int(time() * 1000)
for k, v in metrics.items():
if isinstance(v, str):
log.warning(f"Discarding metric with string value {k}={v}.")
continue
new_k = re.sub("[^a-zA-Z0-9_/. -]+", "", k)
if k != new_k:
rank_zero_warn(
"MLFlow only allows '_', '/', '.' and ' ' special characters in metric name."
f" Replacing {k} with {new_k}.",
category=RuntimeWarning,
)
k = new_k
self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step)
