<gh_stars>1000+
# ClearML - Example of tensorboard with tensorflow (without any actual training)
#
import os
import tensorflow as tf
import numpy as np
from tempfile import gettempdir
from PIL import Image

from clearml import Task


def generate_summary(k, step):
    # Make a normal distribution, with a shifting mean
    mean_moving_normal = tf.random.normal(shape=[1000], mean=(5 * k), stddev=1)
    # Record that distribution into a histogram summary
    tf.summary.histogram("normal/moving_mean", mean_moving_normal, step=step)
    tf.summary.scalar("normal/value", mean_moving_normal[-1], step=step)

    # Make a normal distribution with shrinking variance
    variance_shrinking_normal = tf.random.normal(shape=[1000], mean=0, stddev=1-k)
    # Record that distribution too
    tf.summary.histogram("normal/shrinking_variance", variance_shrinking_normal, step=step)
    tf.summary.scalar("normal/variance_shrinking_normal", variance_shrinking_normal[-1], step=step)

    # Let's combine both of those distributions into one dataset
    normal_combined = tf.concat([mean_moving_normal, variance_shrinking_normal], 0)
    # We add another histogram summary to record the combined distribution
    tf.summary.histogram("normal/bimodal", normal_combined, step=step)
    tf.summary.scalar("normal/normal_combined", normal_combined[0], step=step)

    # Add a gamma distribution
    gamma = tf.random.gamma(shape=[1000], alpha=k)
    tf.summary.histogram("gamma", gamma, step=step)

    # And a poisson distribution
    poisson = tf.random.poisson(shape=[1000], lam=k)
    tf.summary.histogram("poisson", poisson, step=step)

    # And a uniform distribution
    uniform = tf.random.uniform(shape=[1000], maxval=k*10)
    tf.summary.histogram("uniform", uniform, step=step)

    # Finally, combine everything together!
    all_distributions = [mean_moving_normal, variance_shrinking_normal, gamma, poisson, uniform]
    all_combined = tf.concat(all_distributions, 0)
    tf.summary.histogram("all_combined", all_combined, step=step)

    # Log text value
    tf.summary.text("this is a test", "This is the content", step=step)

    # convert to 4d [batch, col, row, RGB-channels]
    image_open = Image.open(os.path.join('..', '..', 'reporting', 'data_samples', 'picasso.jpg'))
    image = np.asarray(image_open)
    image_gray = image[:, :, 0][np.newaxis, :, :, np.newaxis]
    image_rgba = np.concatenate((image, 255*np.atleast_3d(np.ones(shape=image.shape[:2], dtype=np.uint8))), axis=2)
    image_rgba = image_rgba[np.newaxis, :, :, :]
    image = image[np.newaxis, :, :, :]

    tf.summary.image("test", image, max_outputs=10, step=step)
    tf.summary.image("test_gray", image_gray, max_outputs=10, step=step)
    tf.summary.image("test_rgba", image_rgba, max_outputs=10, step=step)


task = Task.init(project_name='examples', task_name='TensorbBoard toy example')

# create the tensorboard file writer in a temp folder
writer = tf.summary.create_file_writer(os.path.join(gettempdir(), "toy_tb_example"))

# Setup a loop and write the summaries to disk
N = 40
for step in range(N):
    k_val = step/float(N)
    with writer.as_default():
        generate_summary(k_val, tf.cast(step, tf.int64))

print('Tensorboard toy example done')
