#DVCLive

DVCLive is a Python library for logging machine learning metrics and other metadata in simple file formats, which is fully compatible with DVC.

#Documentation

• Get Started
• How it Works
• API Reference
• Integrations

#Quickstart

Python API Overview	PyTorch Lightning	Scikit-learn	Ultralytics YOLO v8

#Install dvclive

$ pip install dvclive

#Initialize DVC Repository

$ git init
$ dvc init
$ git commit -m "DVC init"

#Example code

Copy the snippet below into train.py for a basic API usage example:

import time
import random

from dvclive import Live

params = {"learning_rate": 0.002, "optimizer": "Adam", "epochs": 20}

with Live() as live:

    # log a parameters
    for param in params:
        live.log_param(param, params[param])

    # simulate training
    offset = random.uniform(0.2, 0.1)
    for epoch in range(1, params["epochs"]):
        fuzz = random.uniform(0.01, 0.1)
        accuracy = 1 - (2 ** - epoch) - fuzz - offset
        loss = (2 ** - epoch) + fuzz + offset

        # log metrics to studio
        live.log_metric("accuracy", accuracy)
        live.log_metric("loss", loss)
        live.next_step()
        time.sleep(0.2)

See Integrations for examples using DVCLive alongside different ML Frameworks.

#Running

Run this a couple of times to simulate multiple experiments:

$ python train.py
$ python train.py
$ python train.py
...

#Comparing

DVCLive outputs can be rendered in different ways:

#DVC CLI

You can use dvc exp show and dvc plots to compare and visualize metrics, parameters and plots across experiments:

$ dvc exp show

─────────────────────────────────────────────────────────────────────────────────────────────────────────────
Experiment                 Created    train.accuracy   train.loss   val.accuracy   val.loss   step   epochs
─────────────────────────────────────────────────────────────────────────────────────────────────────────────
workspace                  -                  6.0109      0.23311          6.062    0.24321      6   7
master                     08:50 PM                -            -              -          -      -   -
├── 4475845 [aulic-chiv]   08:56 PM           6.0109      0.23311          6.062    0.24321      6   7
├── 7d4cef7 [yarer-tods]   08:56 PM           4.8551      0.82012         4.5555   0.033533      4   5
└── d503f8e [curst-chad]   08:56 PM           4.9768     0.070585         4.0773    0.46639      4   5
─────────────────────────────────────────────────────────────────────────────────────────────────────────────

$ dvc plots diff $(dvc exp list --names-only) --open

#DVC Extension for VS Code

Inside the DVC Extension for VS Code, you can compare and visualize results using the Experiments and Plots views:

While experiments are running, live updates will be displayed in both views.

#DVC Studio

If you push the results to DVC Studio, you can compare experiments against the entire repo history:

You can enable Studio Live Experiments to see live updates while experiments are running.

#Comparison to related technologies

DVCLive is an ML Logger, similar to:

• MLFlow
• Weights & Biases
• Neptune

The main differences with those ML Loggers are:

• DVCLive does not require any additional services or servers to run.
• DVCLive metrics, parameters, and plots are stored as plain text files that can be versioned by tools like Git or tracked as pointers to files in DVC storage.
• DVCLive can save experiments or runs as hidden Git commits.

You can then use different options to visualize the metrics, parameters, and plots across experiments.

#Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

#License

Distributed under the terms of the Apache 2.0 license, dvclive is free and open source software.