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App version: 3.4.14

log_metrics()

Python package: neptune-scale

Logs the specified metrics to a Neptune run.

You can log metrics representing a series of numeric values. Pass the metadata as a dictionary {key: value} with:

  • key: path to where the metadata should be stored in the run.
  • value: the piece of metadata to log.

For example, {"metrics/accuracy": 0.89}. In the attribute path, each forward slash / nests the attribute under a namespace. Use namespaces to structure the metadata into meaningful categories.

Related

Parameters​

data
Dict[str, float | int]
required
default: None

Dictionary of metrics to log. Each metric value is associated with a step. To log multiple metrics at once, pass multiple key-value pairs.

step
float | int
required
default: None

Index of the log entry. Must be increasing if preview=False.

Tip: Using float rather than int values can be useful, for example, when logging substeps in a batch.

timestamp
datetime
optional
default: None

Time of logging the metadata. If not provided, the current time is used. If provided, and timestamp.tzinfo is not set, the time is assumed to be in the local timezone.

preview
bool
optional
default: False

Whether the logged metrics are preview values.

preview_completion
float
optional
default: 0

A value between 0 and 1 that indicates the completion level of the metric computation. Higher value reflects a higher level of completion.

Ignored if preview is set to False.

Step requirements​

  • Within a particular metric, steps must be increasing.

    • Steps can be logged out of order only if the value is a preview value logged after the last regular step.

  • In fork runs, the step can't be smaller than the fork step. For details, see Fork an experiment.

  • For examples, see Log metrics: Setting step values.

Exceptions​

Neptune may warn or raise exceptions if there are malformed or inconsistent values.

Non-increasing steps​

If steps logged to the client are not increasing, Neptune raises the NeptuneSeriesStepNonIncreasing warning. This doesn't terminate the training process.

This step order requirement doesn't apply to preview values. For example, you can log a preview for step 10 and then again for step 9 without errors, if steps 9, 10, and all the following steps have preview set to True.

You can override the default behavior by implementing your own callbacks to handle error scenarios. For details, see Neptune API error handling.

Non-finite values​

By default, Neptune skips non-finite values such as Inf and NaN when logging a series. To raise an exception when such values are encountered, set the NEPTUNE_SKIP_NON_FINITE_METRICS environment variable to False:

Append a line with the export command to your .profile or other shell initialization file:

export NEPTUNE_SKIP_NON_FINITE_METRICS=False

Example​

Create a run and log metrics:

from neptune_scale import Run

with Run(...) as run:
    run.log_metrics(
        data={"loss": 0.14, "acc": 0.78},
        step=1.2,
    )