unified scripts for single GPU usage

tensorflow/cifar10/train_model.py

 from __future__ import print_function
 import numpy as np
 import os, sys
+import random
 import argparse
 import datetime
 import tensorflow as tf
@@ -79,12 +80,14 @@ def setup(args):
     if args.num_interop_threads:
         tf.config.threading.set_inter_op_parallelism_threads(args.num_interop_threads)
 
+    gpu_devices = [] if args.device == "cpu" else tf.config.list_physical_devices("GPU")
+
     print(f"Tensorflow get_intra_op_parallelism_threads: {tf.config.threading.get_intra_op_parallelism_threads()}")
     print(f"Tensorflow get_inter_op_parallelism_threads: {tf.config.threading.get_inter_op_parallelism_threads()}")
 
-    l_gpu_devices = [] if args.device == "cpu" else tf.config.list_physical_devices("GPU")
+    
     print("List of GPU devices found:")
-    for dev in l_gpu_devices:
+    for dev in gpu_devices:
         print(str(dev.device_type) + ": " + dev.name)
     print("")
     sys.stdout.flush()
@@ -93,6 +96,11 @@ def setup(args):
     tf.config.optimizer.set_jit(True)
 
 def main():
+    # always use the same seed
+    random.seed(42)
+    tf.random.set_seed(42)
+    np.random.seed(42)
+
     # parse command line arguments
     args = parse_command_line()
 
@@ -107,6 +115,7 @@ def main():
 
     model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=args.num_classes)
     # model.summary() # display the model architecture
+
     cur_optimizer = Adam(0.001)
     model.compile(loss="categorical_crossentropy", optimizer=cur_optimizer, metrics=["accuracy"])