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Install dvc from dvclive branch: https://github.com/iterative/dvc/tree/dvclive

Install dvclive.

Artificial example:

from dvclive import dvclive
import time


dvclive.init("logs", summarize=True)


def metric(i):
    return 1 - 1/(1+i)

def loss(i):
    return 1-(metric(i))


for i in range(100):
    dvclive.log("metric", metric(i))
    dvclive.log("loss", loss(i))

    dvclive.next_step()
    time.sleep(3)

A bit more real use case:

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

from dvclive import dvclive
from dvclive.keras import DvcLiveCallback

num_classes = 10
input_shape = (28, 28, 1)


def get_data():
    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

    x_train = x_train.astype("float32") / 255
    x_test = x_test.astype("float32") / 255
    x_train = np.expand_dims(x_train, -1)
    x_test = np.expand_dims(x_test, -1)

    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    return (x_train, y_train), (x_test, y_test)



def get_model():
    model= keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ])
    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
    return model

if __name__ == "__main__":
    model =get_model()
    (x_train, y_train), (_, _) = get_data()

    dvclive.init("logs", summarize=True)
    model.fit(x_train, y_train, batch_size=128, epochs=10, validation_split=0.1, callbacks=DvcLiveCallback())

Upon running, look for logs.html in your directory, open it and keep refreshing.

Note, that only thing that we did in the latter example was calling dvclive.init and providing callback for Keras.