tensorboard_prep

# From a Kaggle notebook, run the following blocks as separate cells 


#--------------------------------------------------------------------------
#  [1] Load libraries
#--------------------------------------------------------------------------
import os, multiprocessing, datetime
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorboard import *
from functools import partial


#--------------------------------------------------------------------------
#  [2] Install the plug-in
#--------------------------------------------------------------------------
pip install -U tensorboard-plugin-profile


#--------------------------------------------------------------------------
#  [3] Download ngrok to tunnel the tensorboard port to an external port
#--------------------------------------------------------------------------
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip


#--------------------------------------------------------------------------
#  [4] Clear logs from previous runs
#--------------------------------------------------------------------------
!rm -rf ./logs/ 
!mkdir ./logs/


#--------------------------------------------------------------------------
#  [5] More set up
#--------------------------------------------------------------------------
pool = multiprocessing.Pool(processes = 10)
results_of_processes = [pool.apply_async(os.system, args=(cmd, ), callback = None )
                        for cmd in [
                        f"tensorboard --logdir ./logs/ --host 0.0.0.0 --port 6006 &",
                        "./ngrok http 6006 &"
                        ]]


#--------------------------------------------------------------------------
#  [6] Get the URL for access to our Tensorboard
#--------------------------------------------------------------------------
! curl -s http://localhost:4040/api/tunnels | python3 -c \
    "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"


#--------------------------------------------------------------------------
#  [7A] EITHER USE CALLBACK IN FIT
#--------------------------------------------------------------------------
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, 
                                                      histogram_freq=1, 
                                                      profile_batch=(101,110))
# history = cnn.fit(train_dataset,
#                   steps_per_epoch=STEPS_PER_EPOCH, 
#                   epochs=EPOCHS,
#                   validation_data=valid_dataset,
#                   validation_steps=VALID_STEPS)
#                   callbacks=[tensorboard_callback])


#--------------------------------------------------------------------------
#  [7B] OR USE TRACER ON INDIVIDUAL FUNCTION CALL
#--------------------------------------------------------------------------
logdir = "logs/func/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(logdir)

# #CV_COLOR_MASK on CPU
# tf.summary.trace_on(graph=True, profiler=True) #this works for profiler, but not graph
# rac_masked = cv_color_mask(racoon_tensor)
# with writer.as_default():
      tf.summary.trace_export(name="cv_color_mask CPU",step=0,profiler_outdir=logdir)

# #wrapped CV_COLOR_MASK on CPU
# tf.summary.trace_on(graph=True, profiler=True) #this works for profiler, but not graph
# rac_masked = tf.py_function(func=cv_color_mask, inp=[racoon_tensor], Tout=tf.float32) 
# with writer.as_default():
#     tf.summary.trace_export(name="cv_color_mask CPU",step=0,profiler_outdir=logdir)
    
# #TF_COLOR_MASK on CPU
# tf.summary.trace_on(graph=True, profiler=True)
# rac_masked = tf_color_mask(racoon_tensor)                                                
# with writer.as_default():
#     tf.summary.trace_export(name="tf_color_mask CPU",step=0,profiler_outdir=logdir)

# #decorated TF_COLOR_MASK on CPU
# tf_color_mask = tf.function(tf_color_mask)
# tf.summary.trace_on(graph=True, profiler=True)
# rac_masked = tf_color_mask(racoon_tensor)                                                
# with writer.as_default():
#     tf.summary.trace_export(name="Decorated tf_color_mask",step=0,profiler_outdir=logdir)
    
# #wrapped CV_COLOR_MASK on GPU
# tf.summary.trace_on(graph=True, profiler=True) #this works for profiler, but not graph
# with tf.device("/device:GPU:0"):
#     rac_masked = tf.py_function(func=cv_color_mask, inp=[racoon_tensor], Tout=tf.float32) 
# with writer.as_default():
#     tf.summary.trace_export(name="cv_color_mask GPU",step=0,profiler_outdir=logdir)
    
# #TF_COLOR_MASK on GPU
# tf.summary.trace_on(graph=True, profiler=True)
# with tf.device("/device:GPU:0"):
#     rac_masked = tf_color_mask(racoon_tensor)                                                
# with writer.as_default():
#     tf.summary.trace_export(name="tf_color_mask GPU",step=0,profiler_outdir=logdir)
    
# #decorated TF_COLOR_MASK on GPU
# tf_color_mask = tf.function(tf_color_mask)
# tf.summary.trace_on(graph=True, profiler=True)
# with tf.device("/device:GPU:0"):
#     rac_masked = tf_color_mask(racoon_tensor)                                                
# with writer.as_default():
#     tf.summary.trace_export(name="Decorated tf_color_mask GPU",step=0,profiler_outdir=logdir)

#--------------------------------------------------------------------------
# Much thanks to https://www.kaggle.com/shivam1600/tensorboard-on-kaggle
#--------------------------------------------------------------------------