If you're already using tensorboard it's really easy to integrate with wandb.
import tensorflow as tf import wandb wandb.init(config=tf.FLAGS, tensorboard=True)
If you want more control over what get's logged, WandB also provides a hook for TensorFlow estimators. It will log all
tf.summary values in the graph.
import tensorflow as tf import wandb wandb.init(config=tf.FLAGS) estimator.train(hooks=[wandb.tensorflow.WandbHook(steps_per_log=1000)])
The simplest way to log metrics in Tensorflow is by logging
tf.summary with the tensorflow logger:
import wandb with tf.Session() as sess: # ... wandb.tensorflow.log(tf.summary.merge_all())