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TensorFlow
DVCLive allows you to add experiment tracking capabilities to your TensorFlow projects.
Usage
If you prefer the Keras API, check the DVCLive - Keras page.
You need to add Live.log_metric() calls to each place where you would like to
log metrics and one single Live.next_step() call to indicate that the epoch
has ended.
let's consider the following example, extracted from the official TensorFlow guide:
from dvclive import Live
with Live() as live:
for epoch in range(epochs):
start_time = time.time()
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
with tf.GradientTape() as tape:
logits = model(x_batch_train, training=True)
loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
train_acc_metric.update_state(y_batch_train, logits)
live.log_metric("train/accuracy", float(train_acc_metric.result())
train_acc_metric.reset_states()
for x_batch_val, y_batch_val in val_dataset:
val_logits = model(x_batch_val, training=False)
val_acc_metric.update_state(y_batch_val, val_logits)
live.log_metric("val/accuracy", float(val_acc_metric.result())
val_acc_metric.reset_states()
live.next_step()