DVCLive is a Python library for logging machine learning metrics and other metadata in simple file formats, which is fully compatible with DVC.
| Python API Overview | PyTorch Lightning | Scikit-learn | Ultralytics YOLO v8 |
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$ pip install dvclive$ git init
$ dvc init
$ git commit -m "DVC init"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.
Run this a couple of times to simulate multiple experiments:
$ python train.py
$ python train.py
$ python train.py
...DVCLive outputs can be rendered in different ways:
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
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.
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.
DVCLive is an ML Logger, similar to:
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.
Contributions are very welcome. To learn more, see the Contributor Guide.
Distributed under the terms of the Apache 2.0 license, dvclive is free and open source software.