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description
How to integrate a TensorFlow script to log metrics to W&B

TensorFlow

If you're already using TensorBoard, it's easy to integrate with wandb.

import tensorflow as tf
import wandb
wandb.init(config=tf.flags.FLAGS, sync_tensorboard=True)

See our example projects for a complete script example.

Custom Metrics

If you need to log additional custom metrics that aren't being logged to TensorBoard, you can call wandb.log in your code with the same step argument that TensorBoard is using: ie. wandb.log({"custom": 0.8}, step=global_step)

TensorFlow Hook

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)])

Manual Logging

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())
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