Advanced Tensorboard Features: Graph Dashboard

import wandb
# Just start a W&B run, passing `sync_tensorboard=True)`, to plot your Tensorboard files
wandb.init(project='my-project', sync_tensorboard=True)

# Your Keras, Tensorflow or PyTorch training code using TensorBoard
...

# If running in a notebook, finish the wandb run to upload the tensorboard logs to W&B
wandb.finish()

# Source: https://www.tensorflow.org/tensorboard/graphs
# The function to be traced.
@tf.function
def sample_function(x, y):
    # A simple hand-rolled layer.
    return tf.nn.relu(tf.matmul(x, y))

# Prepare our logger
stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
logdir = 'logs/func/%s' % stamp
writer = tf.summary.create_file_writer(logdir)

# Sample data for the function.
x = tf.random.uniform((3, 3))
y = tf.random.uniform((3, 3))

# Bracket the function call with tf.summary.trace_on() and tf.summary.trace_export().
tf.summary.trace_on(graph=True, profiler=True)
# Call only one tf.function when tracing.
z = sample_function(x, y)
with writer.as_default():
    tf.summary.trace_export(
        name="sample_function_trace", step=0, profiler_outdir=logdir
    )