Hi Alek,
If I understand correctly, you're basically reporting images to be shown in tensorboard (with tf.summary), am I correct?
When I use this code, I get images logged:
` import tensorflow as tf
from clearml import Task
Task.init('test','test tb image')
w = tf.summary.create_file_writer('test/logs')
with w.as_default():
image1 = tf.random.uniform(shape=[8, 8, 1])
image2 = tf.random.uniform(shape=[8, 8, 1])
tf.summary.image("grayscale_noise", [image1, image2], step=0)
Convert the original dtype=int32 Tensor into dtype=float64.
rgb_image_float = tf.constant([
[[1000, 0, 0], [0, 500, 1000]],
]) / 1000
tf.summary.image("picture", [rgb_image_float], step=0)
Convert original dtype=uint8 Tensor into proper range.
rgb_image_uint8 = tf.constant([
[[1, 1, 0], [0, 0, 1]],
], dtype=tf.uint8) * 255
tf.summary.image("picture", [rgb_image_uint8], step=1) `
Does it make a difference for the autologging if this is done in graphmode ?
Am I doing something differently from you?
I have to look deeper into our codebase to see what exactly happens.
We wrap everything in a tf.function() like
` def f():
print('Tracing!')
tf.print('Executing')
tf.function(f)() `
It is the graph mode from TF1 and we use the mode still (in a session).
I will come up with a min working example (probably next week)
Hi CourageousKoala93 , not 100% sure I understand what graphmode is, I see it's a legacy option maybe from TF1? If you can put a small snippet so I can try it on my side that'll be helpful!
Besides that, the procedure is the same, yes…
Hi Alek
It should be auto logged. Could you please give me some details about your environment ?
