Live.log_metric()

def log_metric(
    name: str,
    val: Union[int, float, str],
    timestamp: bool = False,
    plot: Optional[bool] = True
):

Usage

from dvclive import Live

with Live() as live:
    live.log_metric("train/loss", 0.4)
    live.log_metric("val/loss", 0.9)

Description

On each Live.log_metric(name, val) call DVCLive will create a metrics history file in {Live.plots_dir}/metrics/{name}.tsv:

dvclive
├── metrics.json
└── plots
    └── metrics
        ├── train
        │   └── loss.tsv
        └── val
            └── loss.tsv

$ cat dvclive/plots/metrics/train/loss.tsv
timestamp      step  loss
1623671484747  0     0.4

$ dvc plots diff dvclive/plots

Each subsequent call to Live.log_metric(name, val) will add a new row to {Live.plots_dir}/metrics/{name}.tsv:

live.next_step()
live.log_metric("train/loss", 0.2, timestamp=True)
live.log_metric("val/loss", 0.4, timestamp=True)

timestamp      step  loss
1623671484747  0     0.4
1623671484892  1     0.2

In addition, DVCLive will store the latest value logged in Live.summary, so it can be serialized with calls to Live.make_summary(), Live.next_step() or when exiting the with block:

{
  "step": 1,
  "train": {
    "loss": 0.2
  },
  "val": {
    "loss": 0.4
  }
}

$ dvc metrics diff dvclive/metrics.json

Parameters

• name - name of the metric being logged.

• val - the value to be logged.

• timestamp - whether to automatically log timestamp in the metrics history file.

• plot - whether to add the metric value to the metrics history file for plotting. If False, the metric will only be saved to the metrics summary.

Exceptions

• dvclive.error.InvalidDataTypeError - thrown if the provided val does not have a supported type.