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Get Started

DVCLive is a simple Python library whose interface consists of three main steps.


To get it up and running you just need to follow these steps:

1. Initialize DVCLive

from dvclive import Live

live = Live()

See Live() for details.

2. Log metrics

live.log(metric_name, value)

See Live.log() for details.

3. Increase the step number


See Live.next_step() for details.

Putting all together

Using the above steps, you can easily include DVCLive in your training code:

# train.py

from dvclive import Live

live = Live()

for epoch in range(NUM_EPOCHS):
    metrics = evaluate_model(...)

    for metric_name, value in metrics.items():
        live.log(metric_name, value)



After you run your training code, you should see the following content in the project:

$ ls
dvclive        train.py

Metrics Logs

For each {metric_name}, DVCLive produces metrics logs under dvclive/{metric_name}.tsv:

$ cat dvclive/{metric_name}.tsv
timestamp	step	{metric_name}
1614129197192	0	0.7612833380699158
1614129198031	1	0.8736833333969116
1614129198848	2	0.8907166719436646

Metrics Summary

In addition, when summary is enabled (True by default), DVCLive generates a metrics summary with the latest metrics:

$ cat dvclive.json
  "step": 2,
  "{metric_name}": 0.8907166719436646

What next?

There are other ways to use DVCLive:


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