satnogs-wut/src/wut-tf.py

#!/usr/bin/env python3
#
# wut-tf.py
#
# https://spacecruft.org/spacecruft/satnogs-wut
#
# Distributed Learning

from __future__ import absolute_import, division, print_function, unicode_literals
from __future__ import print_function
import os
import json
import numpy as np
import datetime
import tensorflow as tf
import tensorflow.python.keras
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras import optimizers
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from tensorflow.keras.layers import Input, concatenate
from tensorflow.keras.models import load_model
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing.image import load_img
os.environ["TF_CONFIG"] = json.dumps({
    "cluster": {
        "worker": [ "ml1-int:2222", "ml2-int:2222", "ml3-int:2222", "ml4-int:2222", "ml5-int:2222" ]
    }#,
   #"task": {"type": "worker", "index": 0 },
})
print("Tensorflow Version: ", tf.__version__)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
print("Num CPUs Available: ", len(tf.config.experimental.list_physical_devices('CPU')))
print(tf.config.experimental.list_physical_devices())
#with tf.device("GPU:0"):
#  tf.ones(())  # Make sure we can run on GPU
print("XLA_FLAGS='{}'".format(os.getenv("XLA_FLAGS")))
print(os.getenv("XLA_FLAGS"))
tf.keras.backend.clear_session()
IMG_HEIGHT = 416
IMG_WIDTH= 804
batch_size = 32
epochs = 4
BUFFER_SIZE = 10000
NUM_WORKERS = 6
GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
#strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
#strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
#    tf.distribute.experimental.CollectiveCommunication.RING)
AUTOTUNE = tf.data.experimental.AUTOTUNE
NUM_TOTAL_IMAGES=100
tf.config.optimizer.set_jit(True)
#tf.summary.trace_on(profiler=True)
#tf.summary.trace_export(name=trace-export,profiler_outdir=logs)
options = tf.data.Options()