DVCLive allows you to easily add experiment tracking capabilities to your TensorFlow projects.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
To start using DVCLive you just need to add a few lines to your training code in any TensorFlow project.
💡 If you prefer the Keras API, check the DVCLive - Keras page.
To ilustrate with some code, extracted from the official TensorFlow guide:
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) + dvclive.log("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) + dvclive.log("val/accuracy", float(val_acc_metric.result()) val_acc_metric.reset_states() + dvclive.next_step()
This will generate the metrics logs and summaries as described in the Get Started.
💡Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.