How it Works

Directory structure

DVCLive will store the logged data under the directory (dir) passed to Live(). If not provided, dvclive will be used by default.

The contents of the directory will depend on the methods used:

Example

To illustrate with an example, given the following code:

import random
from pathlib import Path

from dvclive import Live
from PIL import Image

EPOCHS = 2

with Live(report="notebook") as live:
    live.log_param("epochs", EPOCHS)

    for i in range(EPOCHS):
        live.log_metric("metric", i + random.random())
        live.log_metric("nested/metric", i + random.random())
        live.log_image(f"img/{live.step}.png", Image.new("RGB", (50, 50), (i, i, i)))
        Path("model.pt").write_text(str(random.random()))
        live.next_step()

    live.log_artifact("model.pt", type="model", name="mymodel")
    live.log_sklearn_plot("confusion_matrix", [0, 0, 1, 1], [0, 1, 0, 1])
    live.log_metric("summary_metric", 1.0, plot=False)
# live.end() has been called at this point

The resulting structure will be:

dvc.yaml
dvclive
├── metrics.json
├── params.yaml
├── plots
│   ├── images
│   │   └── img
│   │       ├── 0.png
│   │       └── 1.png
│   ├── metrics
│   │   ├── metric.tsv
│   │   └── nested
│   │       └── metric.tsv
│   └── sklearn
│       └── confusion_matrix.json
└── report.md
model.pt
model.pt.dvc

Git and DVC integration

DVCLive differs from some other experiment trackers by relying on Git and DVC for tracking instead of a central database. This provides a closer connection to your code, but you may need to relearn a few things if coming from another experiment tracker.

Git integration

DVCLive relies on Git to track the directory it generates, so it will save each run to the same path and overwrite the results each time. DVCLive uses Git to manage results, code changes, and data changes (with DVC).

By default, DVCLive will save a DVC experiment so you don't need to worry about manually making Git commits or branches for each experiment. You can recover them using dvc exp commands or using Git.

Track large artifacts with DVC

Models and data are often large and aren't easily tracked in Git. Live.log_artifact("model.pt") will cache the model.pt file with DVC and make Git ignore it. It will generate a model.pt.dvc metadata file, which can be tracked in Git and becomes part of the experiment. With this metadata file, you can retrieve the versioned artifact from the Git commit. You can also use Live.log_artifact("model.pt", type="model") to add it to the [Studio Model Registry].

Using Live.log_image() to log multiple images may also grow too large to track with Git, in which case you can use Live(cache_images=True) to cache them.

Setup to Run with DVC

You can create or modify the dvc.yaml file at the base of your repository (or elsewhere) to define a pipeline to run experiments with DVC or customize plots. A pipeline stage for model training might look like:

stages:
  train:
    cmd: python train.py
    deps:
      - train.py
    outs:
      - model.pt

Optionally add any subpaths of the DVCLive directory to the outputs. DVC will cache them by default, and you can use those paths as dependencies downstream in your pipeline. For example, to cache all DVCLive plots:

  stages:
    train:
      cmd: python train.py
      deps:
        - train.py
      outs:
        - model.pt
+       - dvclive/plots