diff --git a/workflows/rnd_or_grid/README.md b/workflows/rnd_or_grid/README.md
new file mode 100644
index 00000000..792551f5
--- /dev/null
+++ b/workflows/rnd_or_grid/README.md
@@ -0,0 +1,13 @@
+# Simple grid or random parameter sweep with Swift for all the benchmarks, using command line. D type , which runs a parameter sweep. It calls command-line programs as follows:
+- determineParameters.{sh,py}: Reads data/**settings.json** for sweep parameters, and return as a string for use by Swift program (sweep-parameters.txt)
+- evaluateOne.{sh,py}: Runs a single experiment. (Calls the specified benchmark).
+- computeStats.{sh,py}: Ingests data from all of the experiments and computes simple stats.
+
+Usage: ./run <run directory> <benchmark name> <search type> ./run ex3_p1b1_grid p1b1 grid
+
+Notes:
+**settings.json**: sweep parameters variation
+1. json file must be present in the data folder and named as: <benchmark name>_settings.json, samples files are available and must be modified as per needs.
+2. Run directory will be created in the experiments folder
+3. New variables can be introduced in the determineParameters.py and evaluateOne.py. 
+4. Variations of parameters must be specified in data/*.json files
diff --git a/workflows/rnd_or_grid/data/nt3_settings.json b/workflows/rnd_or_grid/data/nt3_settings.json
new file mode 100644
index 00000000..d505cd22
--- /dev/null
+++ b/workflows/rnd_or_grid/data/nt3_settings.json
@@ -0,0 +1,12 @@
+{
+    "parameters":
+          { 
+          	"epochs": [1, 2 ],
+            "batch_size": [10, 20],
+            "classes": [2, 3]
+          },
+    "samples":
+    	  {
+    	  	"num": [2]
+    	  }
+}
diff --git a/workflows/rnd_or_grid/data/p1b1_settings.json b/workflows/rnd_or_grid/data/p1b1_settings.json
new file mode 100644
index 00000000..f8f7a7a8
--- /dev/null
+++ b/workflows/rnd_or_grid/data/p1b1_settings.json
@@ -0,0 +1,13 @@
+{
+    "parameters":
+          { 
+            "epochs": [1, 2, 8],
+            "batch_size": [20, 40],
+            "N1": [1000, 2000], 
+            "NE": [500, 600]
+          },
+    "samples":
+          {
+            "num": [2]
+          }
+}
diff --git a/workflows/rnd_or_grid/data/p1b3_settings.json b/workflows/rnd_or_grid/data/p1b3_settings.json
new file mode 100644
index 00000000..12a32b60
--- /dev/null
+++ b/workflows/rnd_or_grid/data/p1b3_settings.json
@@ -0,0 +1,14 @@
+{
+    "parameters":
+          { 
+          	"epochs": [1, 2],
+            "batch_size": [50, 60],
+            "test_cell_split": [0.15, 0.25], 
+            "drop": [0.1, 0.15]
+          },
+    "samples":
+          {
+            "num": [2]
+          }
+
+}
diff --git a/workflows/rnd_or_grid/data/p2b1_settings.json b/workflows/rnd_or_grid/data/p2b1_settings.json
new file mode 100644
index 00000000..ce5a0af3
--- /dev/null
+++ b/workflows/rnd_or_grid/data/p2b1_settings.json
@@ -0,0 +1,13 @@
+{
+    "parameters":
+          { 
+            "epochs": [1, 2],
+            "batch_size": [32, 40],
+            "molecular_epochs": [1, 3], 
+            "weight_decay": [0.0005, 0.0006]
+          },
+    "samples":
+          {
+            "num": [2]
+          }
+}
diff --git a/workflows/rnd_or_grid/data/p3b1_settings.json b/workflows/rnd_or_grid/data/p3b1_settings.json
new file mode 100644
index 00000000..c84ca192
--- /dev/null
+++ b/workflows/rnd_or_grid/data/p3b1_settings.json
@@ -0,0 +1,12 @@
+{
+    "parameters":
+          { 
+          	"epochs": [1 , 2 ],
+            "batch_size": [20, 40]
+          },
+    "samples":
+          {
+            "num": [2]
+          }
+}
+	
\ No newline at end of file
diff --git a/workflows/rnd_or_grid/python/computeStats.py b/workflows/rnd_or_grid/python/computeStats.py
new file mode 100644
index 00000000..f414c378
--- /dev/null
+++ b/workflows/rnd_or_grid/python/computeStats.py
@@ -0,0 +1,40 @@
+import sys
+from collections import defaultdict
+import json, os
+
+def extractVals(A):
+    B = defaultdict(dict)
+    A1 = A.split()
+    for n, val in zip(A1[0::2], A1[1::2]):
+        B[n] = float(val)
+    return(B)
+
+def computeStats(swiftArrayAsString):
+    A = extractVals(swiftArrayAsString)
+    vals = []
+    for a in A:
+        vals += [A[a]]
+    print('%d values, with min=%f, max=%f, avg=%f\n'%(len(vals),min(vals),max(vals),sum(vals)/float(len(vals))))
+    
+    filename = os.environ['TURBINE_OUTPUT']+ "/final_stats.txt"
+    # writing the val loss to the output file
+    with open(filename, 'w') as the_file:
+        the_file.write('%d values, with min=%f, max=%f, avg=%f\n'%(len(vals),min(vals),max(vals),sum(vals)/float(len(vals))))
+
+
+
+if (len(sys.argv) < 2):
+	print('requires arg=dataFilename')
+	sys.exit(1)
+
+dataFilename = sys.argv[1]
+
+try:
+    with open(dataFilename, 'r') as the_file:
+        data = the_file.read()
+except IOError as e:
+    print("Could not open: %s" % dataFilename)
+    print("PWD is: '%s'" % os.getcwd())
+
+computeStats(data)
+
diff --git a/workflows/rnd_or_grid/python/determineParameters.py b/workflows/rnd_or_grid/python/determineParameters.py
new file mode 100644
index 00000000..06ef2e1b
--- /dev/null
+++ b/workflows/rnd_or_grid/python/determineParameters.py
@@ -0,0 +1,154 @@
+import sys, json, os
+import random 
+import itertools
+
+# ===== Definitions =========================================================
+def expand(Vs, fr, to, soFar):
+    soFarNew = []
+    for s in soFar:
+        if (Vs[fr] == None):
+             print ("ERROR: The order of json inputs and values must be preserved")
+             sys.exit(1)
+        for v in Vs[fr]:
+             if s == '':
+                 soFarNew += [str(v)]
+             else:
+                 soFarNew += [s+','+str(v)]
+    if fr==to:
+        return(soFarNew)
+    else:
+        return expand(Vs, fr+1, to, soFarNew)
+
+def generate_random(values, n_samples, benchmarkName):
+    # select '#samples' random numbers between the range provided in settings.json file
+    result = ""
+    param_listed = []
+    for s in range(n_samples):
+        if(benchmarkName=="p1b1"):
+            # values = {1:epochs, 2: batch_size, 3: N1, 4: NE}
+            t_epoch= random.randint(values[1][0], values[1][1])
+            t_batch_size= random.randint(values[2][0], values[2][1])
+            t_N1= random.randint(values[3][0], values[3][1])
+            t_NE= random.randint(values[4][0], values[4][1])
+            result+=str(t_epoch) + ',' + str(t_batch_size) + ',' + str(t_N1) + ',' + str(t_NE) 
+        elif(benchmarkName=="p1b3"):
+            # values = {1:epochs, 2: batch_size, 3: test_cell_split, 4: drop}
+            t_epoch= random.randint(values[1][0], values[1][1])
+            t_batch_size= random.randint(values[2][0], values[2][1])
+            t_tcs= random.uniform(values[3][0], values[3][1])
+            t_drop= random.uniform(values[4][0], values[4][1])
+            result+=str(t_epoch) + ',' + str(t_batch_size) + ',' + str(t_tcs) + ',' + str(t_drop) 
+        elif(benchmarkName=="nt3"):
+            # values = {1:epochs, 2: batch_size, 3: classes}
+            t_epoch= random.randint(values[1][0], values[1][1])
+            t_batch_size= random.randint(values[2][0], values[2][1])
+            t_classes= random.randint(values[3][0], values[3][1])
+            result+=str(t_epoch) + ',' + str(t_batch_size) + ',' + str(t_classes)  
+        elif(benchmarkName=="p2b1"):
+            # values = {1:epochs, 2: batch_size, 3: molecular_epochs, 4: weight_decay}
+            t_epoch= random.randint(values[1][0], values[1][1])
+            t_batch_size= random.randint(values[2][0], values[2][1])
+            t_me= random.randint(values[3][0], values[3][1])
+            t_wd= random.uniform(values[4][0], values[4][1])
+            result+=str(t_epoch) + ',' + str(t_batch_size) + ',' + str(t_me) + ',' + str(t_wd) 
+        elif(benchmarkName=="p3b1"):
+            # values = {1:epochs, 2: batch_size}//, 3: learning_rate, 4: n_fold}
+            t_epoch= random.randint(values[1][0], values[1][1])
+            t_batch_size= random.randint(values[2][0], values[2][1])
+            result+=str(t_epoch) + ',' + str(t_batch_size)
+        else:
+            print('ERROR: Tried all possible benchmarks, Invalid benchmark name or json file')
+            sys.exit(1)
+        # Populate the result string for writing sweep-parameters file
+        param_listed += [str(result)]
+        result=""
+    return (param_listed)
+
+# ===== Main program ========================================================
+if (len(sys.argv) < 3):
+    print('requires arg1=settingsFilename and arg2=paramsFilename')
+    sys.exit(1)
+
+settingsFilename = sys.argv[1]
+paramsFilename   = sys.argv[2]
+benchmarkName    = sys.argv[3]
+searchType       = sys.argv[4]
+
+## Read in the variables from json file
+#Trying to open the settings file
+print("Reading settings: %s" % settingsFilename)
+try:
+    with open(settingsFilename) as fp:
+        settings = json.load(fp)
+except IOError as e:
+    print("Could not open: %s" % settingsFilename)
+    print("PWD is: '%s'" % os.getcwd())
+    sys.exit(1)
+
+# Register new variables for any benchmark here
+#Common variables
+epochs = settings.get('parameters').get('epochs')
+batch_size = settings.get('parameters').get('batch_size')
+# P1B1
+N1 = settings.get('parameters').get('N1')
+NE = settings.get('parameters').get('NE') 
+#NT3
+classes = settings.get('parameters').get('classes')   
+#P2B1
+molecular_epochs = settings.get('parameters').get('molecular_epochs') 
+weight_decay = settings.get('parameters').get('weight_decay') 
+#P3B1
+# learning_rate = settings.get('parameters').get('learning_rate') 
+# n_fold = settings.get('parameters').get('n_fold') 
+#P1B3
+test_cell_split = settings.get('parameters').get('test_cell_split') 
+drop = settings.get('parameters').get('drop') 
+
+# For random scheme determine number of samples
+samples = settings.get('samples', {}).get('num', None)
+
+## Done reading from file 
+
+# Make values for computing grid sweep parameters
+values = {}
+if(benchmarkName=="p1b1"):
+    values = {1:epochs, 2: batch_size, 3: N1, 4: NE}
+elif(benchmarkName=="p1b3"):
+    values = {1:epochs, 2: batch_size, 3: test_cell_split, 4: drop}
+elif(benchmarkName=="nt3"):
+    values = {1:epochs, 2: batch_size, 3: classes}
+elif(benchmarkName=="p2b1"):
+    values = {1:epochs, 2: batch_size, 3: molecular_epochs, 4: weight_decay}
+elif(benchmarkName=="p3b1"):
+    values = {1:epochs, 2: batch_size}
+else:
+    print('ERROR: Tried all possible benchmarks, Invalid benchmark name or json file')
+    sys.exit(1)
+
+# this (result) is : seperated string with all params
+result = {}
+# Determine parameter space based of search type
+if(searchType == "grid"):
+    results = expand(values, 1, len(values), [''])
+elif(searchType =="random"):
+    if(samples == None):
+        print ("ERROR: Provide number of samples in json file")
+        sys.exit(1)
+    # result, results = generate_random(values, samples, benchmarkName) 
+    results = generate_random(values, samples[0], benchmarkName) 
+else:
+    print ("ERROR: Invalid search type, specify either - grid or random")
+    sys.exit(1)
+
+counter=0
+for a, b in itertools.combinations(results, 2):
+    if(a == b):
+        print ("Warning: skipping -identical parameters found", counter)
+        results.remove(a)
+
+#These are final : seperated parameters for evaluation
+result = ':'.join(results)
+
+with open(paramsFilename, 'w') as the_file:
+    the_file.write(result)
+
diff --git a/workflows/rnd_or_grid/python/evaluateOne.py b/workflows/rnd_or_grid/python/evaluateOne.py
new file mode 100644
index 00000000..73d5db74
--- /dev/null
+++ b/workflows/rnd_or_grid/python/evaluateOne.py
@@ -0,0 +1,90 @@
+import sys
+import json, os
+import socket
+
+
+if (len(sys.argv) < 3):
+	print('requires arg1=param and arg2=filename')
+	sys.exit(1)
+
+parameterString = sys.argv[1]
+filename        = sys.argv[2]
+benchmarkName   = sys.argv[3]
+
+integs = [float(x) for x in parameterString.split(',')]
+
+if (benchmarkName == "p1b1"):
+	import p1b1_runner
+	hyper_parameter_map = {'epochs' : int(integs[0])}
+	hyper_parameter_map['framework'] = 'keras'
+	hyper_parameter_map['batch_size'] = int(integs[1])
+	hyper_parameter_map['dense'] = [int(integs[2]), int(integs[3])] 
+	hyper_parameter_map['run_id'] = parameterString
+	# hyper_parameter_map['instance_directory'] = os.environ['TURBINE_OUTPUT'] 
+	hyper_parameter_map['save'] = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid())
+	sys.argv = ['p1b1_runner']
+	val_loss = p1b1_runner.run(hyper_parameter_map)
+elif (benchmarkName == "p1b3"):
+	import p1b3_runner
+	hyper_parameter_map = {'epochs' : int(integs[0])}
+	hyper_parameter_map['framework'] = 'keras'
+	hyper_parameter_map['batch_size'] = int(integs[1])
+	hyper_parameter_map['test_cell_split'] = int(integs[2])
+	hyper_parameter_map['drop'] = int(integs[3])
+	hyper_parameter_map['run_id'] = parameterString
+	# hyper_parameter_map['instance_directory'] = os.environ['TURBINE_OUTPUT'] 
+	hyper_parameter_map['save'] = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid())
+	sys.argv = ['p1b3_runner']
+	val_loss = p1b3_runner.run(hyper_parameter_map)
+elif (benchmarkName == "p2b1"):
+	import p2b1_runner
+	hyper_parameter_map = {'epochs' : int(integs[0])}
+	hyper_parameter_map['framework'] = 'keras'
+	hyper_parameter_map['batch_size'] = int(integs[1])
+	hyper_parameter_map['molecular_epochs'] = int(integs[2])
+	hyper_parameter_map['weight_decay'] = integs[3]
+	hyper_parameter_map['run_id'] = parameterString
+	hyper_parameter_map['save'] = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid())
+	sys.argv = ['p2b1_runner']
+	val_loss = p2b1_runner.run(hyper_parameter_map)
+elif (benchmarkName == "nt3"):
+	import nt3_tc1_runner
+	hyper_parameter_map = {'epochs' : int(integs[0])}
+	hyper_parameter_map['framework'] = 'keras'
+	hyper_parameter_map['batch_size'] = int(integs[1])
+	hyper_parameter_map['classes'] = int(integs[2])
+	hyper_parameter_map['model_name'] = 'nt3'	
+	hyper_parameter_map['save'] = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid())
+	sys.argv = ['nt3_runner']
+	val_loss = nt3_tc1_runner.run(hyper_parameter_map)
+elif (benchmarkName == "p3b1"):
+	import p3b1_runner
+	hyper_parameter_map = {'epochs' : int(integs[0])}
+	hyper_parameter_map['framework'] = 'keras'
+	hyper_parameter_map['batch_size'] = int(integs[1])
+	hyper_parameter_map['run_id'] = parameterString
+	# hyper_parameter_map['instance_directory'] = os.environ['TURBINE_OUTPUT'] 
+	hyper_parameter_map['save'] = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid())
+	sys.argv = ['p3b1_runner']
+	val_loss = p3b1_runner.run(hyper_parameter_map)
+
+# print (parameterString)
+# print ("filename is " + filename)
+# print (str(os.getpid()))
+print (val_loss)
+
+# sfn = os.environ['TURBINE_OUTPUT']+ "/output-"+str(os.getpid()) + "/procname-" + parameterString
+# with open(sfn, 'w') as sfile:
+#     sfile.write(socket.getfqdn())
+#     proc_id = "-"+ str(os.getpid())
+#     sfile.write(proc_id)
+
+# works around this error:
+# https://github.com/tensorflow/tensorflow/issues/3388
+from keras import backend as K
+K.clear_session()
+
+# writing the val loss to the output file (result-*)
+with open(filename, 'w') as the_file:
+    the_file.write(repr(val_loss))
+
diff --git a/workflows/rnd_or_grid/python/nt3_tc1_runner.py b/workflows/rnd_or_grid/python/nt3_tc1_runner.py
new file mode 100644
index 00000000..678f8a00
--- /dev/null
+++ b/workflows/rnd_or_grid/python/nt3_tc1_runner.py
@@ -0,0 +1,73 @@
+# tensoflow.__init__ calls _os.path.basename(_sys.argv[0])
+# so we need to create a synthetic argv.
+import sys
+if not hasattr(sys, 'argv'):
+    sys.argv  = ['nt3_tc1']
+
+import json
+import os
+import numpy as np
+import importlib
+import runner_utils
+
+def import_pkg(framework, model_name):
+    if framework == 'keras':
+        module_name = "{}_baseline_keras2".format(model_name)
+        pkg = importlib.import_module(module_name)
+    # elif framework is 'mxnet':
+    #     import nt3_baseline_mxnet
+    #     pkg = nt3_baseline_keras_baseline_mxnet
+    # elif framework is 'neon':
+    #     import nt3_baseline_neon
+    #     pkg = nt3_baseline_neon
+    else:
+        raise ValueError("Invalid framework: {}".format(framework))
+    return pkg
+
+def run(hyper_parameter_map):
+    framework = hyper_parameter_map['framework']
+    model_name = hyper_parameter_map['model_name']
+    pkg = import_pkg(framework, model_name)
+
+    runner_utils.format_params(hyper_parameter_map)
+
+    # params is python dictionary
+    params = pkg.initialize_parameters()
+    for k,v in hyper_parameter_map.items():
+        #if not k in params:
+        #    raise Exception("Parameter '{}' not found in set of valid arguments".format(k))
+        params[k] = v
+
+    runner_utils.write_params(params, hyper_parameter_map)
+    history = pkg.run(params)
+
+    if framework == 'keras':
+        # works around this error:
+        # https://github.com/tensorflow/tensorflow/issues/3388
+        try:
+            from keras import backend as K
+            K.clear_session()
+        except AttributeError:      # theano does not have this function
+            pass
+
+    # use the last validation_loss as the value to minimize
+    val_loss = history.history['val_loss']
+    return val_loss[-1]
+
+if __name__ == '__main__':
+    param_string = sys.argv[1]
+    instance_directory = sys.argv[2]
+    model_name = sys.argv[3]
+    framework = sys.argv[4]
+    exp_id = sys.argv[5]
+    run_id = sys.argv[6]
+    benchmark_timeout = int(sys.argv[7])
+    hyper_parameter_map = runner_utils.init(param_string, instance_directory, framework, 'save')
+    hyper_parameter_map['model_name'] = model_name
+    hyper_parameter_map['experiment_id'] = exp_id
+    hyper_parameter_map['run_id'] = run_id
+    hyper_parameter_map['timeout'] = benchmark_timeout
+    # clear sys.argv so that argparse doesn't object
+    sys.argv = ['nt3_tc1_runner']
+    result = run(hyper_parameter_map)
+    runner_utils.write_output(result, instance_directory)
diff --git a/workflows/rnd_or_grid/python/p1b1_runner.py b/workflows/rnd_or_grid/python/p1b1_runner.py
new file mode 100644
index 00000000..20ce7e7d
--- /dev/null
+++ b/workflows/rnd_or_grid/python/p1b1_runner.py
@@ -0,0 +1,46 @@
+# tensoflow.__init__ calls _os.path.basename(_sys.argv[0])
+# so we need to create a synthetic argv.
+import sys
+if not hasattr(sys, 'argv'):
+    sys.argv  = ['p1b1']
+
+import json
+import os
+import p1b1
+
+def run(hyper_parameter_map):
+    framework = hyper_parameter_map['framework']
+    if framework is 'keras':
+        import p1b1_baseline_keras2
+        pkg = p1b1_baseline_keras2
+    elif framework is 'mxnet':
+        import p1b1_baseline_mxnet
+        pkg = p1b1_baseline_mxnet
+    elif framework is 'neon':
+        import p1b1_baseline_neon
+        pkg = p1b1_baseline_neon
+    else:
+        raise ValueError("Invalid framework: {}".format(framework))
+
+    # params is python dictionary
+    params = pkg.initialize_parameters()
+    for k,v in hyper_parameter_map.items():
+        #if not k in params:
+        #    raise Exception("Parameter '{}' not found in set of valid arguments".format(k))
+        params[k] = v
+
+    print(params)
+    history = pkg.run(params)
+
+    if framework is 'keras':
+        # works around this error:
+        # https://github.com/tensorflow/tensorflow/issues/3388
+        try:
+            from keras import backend as K
+            K.clear_session()
+        except AttributeError:      # theano does not have this function
+            pass
+
+    # use the last validation_loss as the value to minimize
+    val_loss = history.history['val_loss']
+    return val_loss[-1]
diff --git a/workflows/rnd_or_grid/python/p1b3_runner.py b/workflows/rnd_or_grid/python/p1b3_runner.py
new file mode 100644
index 00000000..f330ed18
--- /dev/null
+++ b/workflows/rnd_or_grid/python/p1b3_runner.py
@@ -0,0 +1,60 @@
+# tensoflow.__init__ calls _os.path.basename(_sys.argv[0])
+# so we need to create a synthetic argv.
+import sys
+if not hasattr(sys, 'argv'):
+    sys.argv  = ['p1b3']
+
+import json
+import os
+import p1b3
+import runner_utils
+
+def run(hyper_parameter_map):
+    framework = hyper_parameter_map['framework']
+    if framework == 'keras':
+        import p1b3_baseline_keras2
+        pkg = p1b3_baseline_keras2
+    elif framework == 'mxnet':
+        import p1b3_baseline_mxnet
+        pkg = p1b3_baseline_mxnet
+    elif framework == 'neon':
+        import p1b3_baseline_neon
+        pkg = p1b3_baseline_neon
+    else:
+        raise ValueError("Invalid framework: {}".format(framework))
+
+    # params is python dictionary
+    params = pkg.initialize_parameters()
+    runner_utils.format_params(hyper_parameter_map)
+
+    for k,v in hyper_parameter_map.items():
+        #if not k in params:
+        #    raise Exception("Parameter '{}' not found in set of valid arguments".format(k))
+        params[k] = v
+
+    runner_utils.write_params(params, hyper_parameter_map)
+    history = pkg.run(params)
+
+    if framework == 'keras':
+        # works around this error:
+        # https://github.com/tensorflow/tensorflow/issues/3388
+        try:
+            from keras import backend as K
+            K.clear_session()
+        except AttributeError:      # theano does not have this function
+            pass
+
+    # use the last validation_loss as the value to minimize
+    val_loss = history.history['val_loss']
+    return val_loss[-1]
+
+if __name__ == '__main__':
+    param_file = sys.argv[1]
+    instance_directory = sys.argv[2]
+    framework = sys.argv[3]
+    hyper_parameter_map = runner_utils.init(param_file, instance_directory, framework,
+                               'save')
+    # clear sys.argv so that argparse doesn't object
+    sys.argv = ['p1b3_runner']
+    result = run(hyper_parameter_map)
+    runner_utils.write_output(result, instance_directory)
diff --git a/workflows/rnd_or_grid/python/p2b1_runner.py b/workflows/rnd_or_grid/python/p2b1_runner.py
new file mode 100644
index 00000000..b3583c7d
--- /dev/null
+++ b/workflows/rnd_or_grid/python/p2b1_runner.py
@@ -0,0 +1,58 @@
+# tensoflow.__init__ calls _os.path.basename(_sys.argv[0])
+# so we need to create a synthetic argv.
+import sys
+if not hasattr(sys, 'argv'):
+    sys.argv  = ['p2b1']
+
+import json
+import os
+import p2b1
+import runner_utils
+
+def run(hyper_parameter_map):
+    framework = hyper_parameter_map['framework']
+    if framework == 'keras':
+        import p2b1_baseline_keras2
+        pkg = p2b1_baseline_keras2
+    else:
+        raise ValueError("Invalid framework: {}".format(framework))
+
+    # params is python dictionary
+    params = pkg.initialize_parameters()
+    runner_utils.format_params(hyper_parameter_map)
+
+    for k,v in hyper_parameter_map.items():
+        #if not k in params:
+        #    raise Exception("Parameter '{}' not found in set of valid arguments".format(k))
+        params[k] = v
+
+    runner_utils.write_params(params, hyper_parameter_map)
+    loss_history = pkg.run(params)
+
+    if framework == 'keras':
+        # works around this error:
+        # https://github.com/tensorflow/tensorflow/issues/3388
+        try:
+            from keras import backend as K
+            K.clear_session()
+        except AttributeError:      # theano does not have this function
+            pass
+
+    return loss_history[-1]
+
+if __name__ == '__main__':
+    param_string = sys.argv[1]
+    instance_directory = sys.argv[2]
+    framework = sys.argv[3]
+    exp_id = sys.argv[4]
+    run_id = sys.argv[5]
+    benchmark_timeout = int(sys.argv[6])
+    hyper_parameter_map = runner_utils.init(param_string, instance_directory,
+                                            framework, 'save_path')
+    hyper_parameter_map['experiment_id'] = exp_id
+    hyper_parameter_map['run_id'] = run_id
+    hyper_parameter_map['timeout'] = benchmark_timeout
+    # clear sys.argv so that argparse doesn't object
+    sys.argv = ['p2b1_runner']
+    result = run(hyper_parameter_map)
+    runner_utils.write_output(result, instance_directory)
diff --git a/workflows/rnd_or_grid/python/p3b1_runner.py b/workflows/rnd_or_grid/python/p3b1_runner.py
new file mode 100644
index 00000000..385d3e26
--- /dev/null
+++ b/workflows/rnd_or_grid/python/p3b1_runner.py
@@ -0,0 +1,97 @@
+# tensoflow.__init__ calls _os.path.basename(_sys.argv[0])
+# so we need to create a synthetic argv.
+import sys
+if not hasattr(sys, 'argv'):
+    sys.argv  = ['p3b1']
+
+import json
+import os
+import p3b1
+import runner_utils
+import socket
+
+node_pid = "%s,%i" % (socket.gethostname(), os.getpid())
+print("node,pid: " + node_pid)
+
+logger = None
+
+def get_logger():
+    """ Set up logging """
+    global logger
+    if logger is not None:
+        return logger
+    import logging, sys
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+    h = logging.StreamHandler(stream=sys.stdout)
+    fmtr = logging.Formatter('%(asctime)s %(name)s %(levelname)-9s %(message)s',
+                             datefmt='%Y/%m/%d %H:%M:%S')
+    h.setFormatter(fmtr)
+    logger.addHandler(h)
+    return logger
+
+def run(hyper_parameter_map):
+
+    logger = get_logger()
+    framework = hyper_parameter_map['framework']
+    logger.debug("IMPORT START")
+    if framework == 'keras':
+        import p3b1_baseline_keras2
+        pkg = p3b1_baseline_keras2
+    else:
+        raise ValueError("Unsupported framework: {}".format(framework))
+    logger.debug("IMPORT STOP")
+
+    # params is python dictionary
+    params = pkg.initialize_parameters()
+    runner_utils.format_params(hyper_parameter_map)
+
+    for k,v in hyper_parameter_map.items():
+        #if not k in params:
+        #    raise Exception("Parameter '{}' not found in set of valid arguments".format(k))
+        params[k] = v
+
+    logger.debug("WRITE_PARAMS START")
+    runner_utils.write_params(params, hyper_parameter_map)
+    logger.debug("WRITE_PARAMS STOP")
+    logger.debug("DO_N_FOLD START")
+    avg_loss = pkg.do_n_fold(params)
+    logger.debug("DO_N_FOLD STOP")
+
+    if framework == 'keras':
+        # works around this error:
+        # https://github.com/tensorflow/tensorflow/issues/3388
+        try:
+            from keras import backend as K
+            K.clear_session()
+        except AttributeError:      # theano does not have this function
+            pass
+
+    return avg_loss
+
+if __name__ == '__main__':
+    logger = get_logger()
+    logger.debug("RUN START")
+
+    param_string = sys.argv[1]
+    instance_directory = sys.argv[2]
+    framework = sys.argv[3]
+    exp_id = sys.argv[4]
+    run_id = sys.argv[5]
+    benchmark_timeout = int(sys.argv[6])
+
+    logger.debug("RUN INIT START")
+    
+    hyper_parameter_map = runner_utils.init(param_string, instance_directory,
+                                            framework, 'save_path')
+    logger.debug("RUN INIT STOP")
+    hyper_parameter_map['experiment_id'] = exp_id
+    hyper_parameter_map['run_id'] = run_id
+    hyper_parameter_map['timeout'] = benchmark_timeout
+    # clear sys.argv so that argparse doesn't object
+    sys.argv = ['p3b1_runner']
+    result = run(hyper_parameter_map)
+    logger.debug("WRITE OUTPUT START")
+    runner_utils.write_output(result, instance_directory)
+    logger.debug("WRITE OUTPUT STOP")
+    logger.debug("RUN STOP")
diff --git a/workflows/rnd_or_grid/python/test/run_test_runners.sh b/workflows/rnd_or_grid/python/test/run_test_runners.sh
new file mode 100755
index 00000000..019b4824
--- /dev/null
+++ b/workflows/rnd_or_grid/python/test/run_test_runners.sh
@@ -0,0 +1,7 @@
+#! /usr/bin/env bash
+
+RUNNER_DIR=../../../../../Benchmarks/Pilot1/P1B1:../../../../../Benchmarks/Pilot2/P2B1:../../../../../Benchmarks/Pilot3/P3B1:../../../../../Benchmarks/Pilot1/NT3:../../../../../Benchmarks/Pilot1/P1B3
+export PYTHONPATH="$PWD/..:$RUNNER_DIR:../../../common/python"
+echo $PYTHONPATH
+
+python test_runners.py
\ No newline at end of file
diff --git a/workflows/rnd_or_grid/python/test/run_theta_runners.sh b/workflows/rnd_or_grid/python/test/run_theta_runners.sh
new file mode 100644
index 00000000..20f00a74
--- /dev/null
+++ b/workflows/rnd_or_grid/python/test/run_theta_runners.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+set -eu
+
+# Theta / Tensorflow env vars
+export KMP_BLOCKTIME=30
+export KMP_SETTINGS=1
+export KMP_AFFINITY=granularity=fine,verbose,compact,1,0
+export OMP_NUM_THREADS=128
+
+export PYTHONHOME="/lus/theta-fs0/projects/Candle_ECP/ncollier/py2_tf_gcc6.3_eigen3_native"
+#export PYTHONHOME="/home/rjain/anaconda2"
+PYTHON="$PYTHONHOME/bin/python"
+export LD_LIBRARY_PATH="$PYTHONHOME/lib"
+export PATH="$PYTHONHOME/bin:$PATH"
+
+RUNNER_DIR=../../../../../Benchmarks/Pilot1/P1B1:../../../../../Benchmarks/Pilot2/P2B1:../../../../../Benchmarks/Pilot3/P3B1:../../../../../Benchmarks/Pilot1/NT3:../../../../../Benchmarks/Pilot1/P1B3
+COMMON_DIR=../../../common/python
+PYTHONPATH="$PYTHONHOME/lib/python2.7:"
+PYTHONPATH+="../:$RUNNER_DIR:$COMMON_DIR:"
+PYTHONPATH+="$PYTHONHOME/lib/python2.7/site-packages"
+export PYTHONPATH
+export PROJECT=Candle_ECP
+
+echo $PYTHONPATH
+$PYTHON test_runners.py
diff --git a/workflows/rnd_or_grid/python/test/test_runners.py b/workflows/rnd_or_grid/python/test/test_runners.py
new file mode 100644
index 00000000..0f2c2b14
--- /dev/null
+++ b/workflows/rnd_or_grid/python/test/test_runners.py
@@ -0,0 +1,62 @@
+import p1b1_runner
+import p2b1_runner
+import p1b3_runner
+import p3b1_runner
+import nt3_tc1_runner
+
+def main():
+
+    hyper_parameter_map = {'epochs' : 1}
+    hyper_parameter_map['framework'] = 'keras'
+    # hyper_parameter_map['save_path'] = save_path
+#     hyper_parameter_map = {'epochs' : 1}
+#     hyper_parameter_map['batch_size'] = 40
+#     hyper_parameter_map['dense'] = [1219, 536] 
+#     hyper_parameter_map['framework'] = 'keras'
+
+#1 # p1b1 
+    # hyper_parameter_map['save'] = './p1bl1_testing_failure'
+    print("STARTING#####P1B1##########")
+    ts_p1b1 = datetime.now()
+    p1b1_validation_loss = p1b1_runner.run(hyper_parameter_map)
+    te_p1b1 = datetime.now()
+    print("Validation loss=",p1b1_validation_loss)
+    print("DONE##########P1B1#####, TIME=", te_p1b1 - ts_p1b1)
+
+
+#2 # p1b3 
+    print("STARTING#####P1B3##########")
+    ts_p1b3 = datetime.now()
+    p1b3_validation_loss = p1b3_runner.run(hyper_parameter_map)
+    te_p1b3 = datetime.now()
+    print("Validation loss=",p1b3_validation_loss)
+    print("DONE##########P1B3#####, TIME=", te_p1b3 - ts_p1b3)
+
+#3 # p2b1 
+    print("STARTING#####P2B1##########")
+    ts_p2b1 = datetime.now()
+    p2b1_validation_loss = p2b1_runner.run(hyper_parameter_map)
+    te_p2b1 = datetime.now()
+    print("Validation loss=",p2b1_validation_loss)
+    print("DONE##########P2B1#####, TIME=", te_p2b1 - ts_p2b1)
+
+#4 # p3b1 
+    print("STARTING#####P3B1##########")
+    ts_p3b1 = datetime.now()
+    p3b1_validation_loss = p3b1_runner.run(hyper_parameter_map)
+    te_p3b1 = datetime.now()
+    print("Validation loss=",p3b1_validation_loss)
+    print("DONE##########P3B1#####, TIME=", te_p3b1 - ts_p3b1)
+
+#5 # NT3 
+    print("STARTING#####NT3##########")
+    hyper_parameter_map['model_name'] = 'nt3'
+    ts_nt3 = datetime.now()
+    nt3tc1_validation_loss = nt3_tc1_runner.run(hyper_parameter_map)
+    te_nt3 = datetime.now()
+    print("Validation loss=",nt3tc1_validation_loss)
+    print("DONE##########NT3#####, TIME=", te_nt3 - ts_nt3)
+
+if __name__ == '__main__':
+    main()
+
diff --git a/workflows/rnd_or_grid/swift/computeStats.sh b/workflows/rnd_or_grid/swift/computeStats.sh
new file mode 100755
index 00000000..9d2b0e25
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/computeStats.sh
@@ -0,0 +1,2 @@
+#!/bin/bash
+python $APP_HOME/../python/computeStats.py $1
diff --git a/workflows/rnd_or_grid/swift/determineParameters.sh b/workflows/rnd_or_grid/swift/determineParameters.sh
new file mode 100755
index 00000000..098d26ec
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/determineParameters.sh
@@ -0,0 +1,2 @@
+#!/bin/bash
+python $APP_HOME/../python/determineParameters.py $1 $2 $3 $4
diff --git a/workflows/rnd_or_grid/swift/evaluateOne.sh b/workflows/rnd_or_grid/swift/evaluateOne.sh
new file mode 100755
index 00000000..08a72341
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/evaluateOne.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+filename=$TURBINE_OUTPUT/result-$1.txt
+python -u $APP_HOME/../python/evaluateOne.py $1 $filename $3
diff --git a/workflows/rnd_or_grid/swift/rnd_or_grid.swift b/workflows/rnd_or_grid/swift/rnd_or_grid.swift
new file mode 100644
index 00000000..ea050373
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/rnd_or_grid.swift
@@ -0,0 +1,64 @@
+import string;
+import files;
+import io;
+import sys;
+
+// ===== Interface definitions for the programs that we call ======
+// Random values are created from bounds specified in data/settings.json file
+app (file f)
+determineParameters(string settingsFilename, string benchmark, string searchType)
+{
+  (getenv("APP_HOME")+"/determineParameters.sh") settingsFilename f benchmark searchType;
+}
+
+// This is where the p1b1 runner is called
+app (file f)
+evaluateOne(string params, string benchmark)
+{
+  (getenv("APP_HOME")+"/evaluateOne.sh") params f benchmark;
+}
+
+// call this to read all the resultsFiles and compute stats
+app ()
+computeStats(string resultsFile)
+{
+  (getenv("APP_HOME")+"/computeStats.sh") resultsFile;
+}
+
+// call this to create any required directories
+app (void o) make_dir(string dirname) {
+  "mkdir" "-p" dirname;
+}
+
+
+// ===== The program proper ==============================================
+string turbine_output = getenv("TURBINE_OUTPUT");
+string app_home = getenv("APP_HOME");
+float results[string];
+
+//make the experiments dir
+make_dir(turbine_output);
+
+// Get parameters
+benchmark = argv("benchmark_name");
+searchType = argv("search_type");
+inputFile = argv("input_file");
+settingsFilename = app_home+"/../data/"+inputFile;
+string sweepParamFile = turbine_output+"/sweep-parameters.txt";
+file parametersFile<sweepParamFile> = determineParameters(settingsFilename, benchmark, searchType);
+parametersString = read(parametersFile);
+parameters = split(parametersString, ":");
+
+// Run experiments in parallel, passing each a different parameter set
+foreach param in parameters
+{
+	string rName = turbine_output+"/result-"+param+".txt";
+    file resultFile<rName> = evaluateOne(param, benchmark);
+    results[param] = string2float(read(resultFile));
+}
+
+// Compute stats of this array of results
+// Write directly to a file with write
+file tmp = write(repr(results));
+computeStats(filename(tmp));
+
diff --git a/workflows/rnd_or_grid/swift/run b/workflows/rnd_or_grid/swift/run
new file mode 100755
index 00000000..2b9d8d63
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/run
@@ -0,0 +1,58 @@
+#!/bin/bash
+#
+# Usage: ./run 
+#
+
+if [ "$#" -ne 4 ]; then
+  script_name=$(basename $0)
+  echo "Usage: ${script_name} EXPERIMENT_ID (run1_p1b1) BENCHMARKS_NAME (eg. p1b1) SEARCH_TYPE (eg. grid or random) INPUT_JSON"
+  echo "Example: ./run p1b1_experiment1 p1b1 random p1b1_settings.json"
+  echo "-This creates a p1b1_experiment1 directory in ../experiments"
+  echo " uses random scheme for variables specified in ../data/p1b1_settings.json file"
+  exit 1
+fi
+
+#### set this variable to add new benchmarks directory
+RUNNERS_DIR=../../../../Benchmarks/Pilot1/P1B1:../../../../Benchmarks/Pilot2/P2B1:../../../../Benchmarks/Pilot3/P3B1:../../../../Benchmarks/Pilot1/NT3:../../../../Benchmarks/Pilot1/P1B3
+###
+
+THIS=$( cd $( dirname $0 ); /bin/pwd )
+export APP_HOME=$THIS
+
+PROJECT_ROOT=$APP_HOME/..
+
+export PYTHONPATH=$PYTHONPATH:$PROJECT_ROOT/python:$RUNNERS_DIR:$PROJECT_ROOT/../common/python:$PYTHONPATH
+
+export EXPID=$1
+B_NAME=$2
+S_NAME=$3
+JSON_F=$4
+
+export TURBINE_OUTPUT=$APP_HOME/../experiments/$EXPID
+
+	# prefix=$PWD/../data/
+	# suffix="_settings.json"
+	# export SETTINGS_FILE=$prefix$BENCHMARK_NAME$suffix
+
+# TODO edit QUEUE, WALLTIME, PPN, AND TURNBINE_JOBNAME
+# as required. Note that QUEUE, WALLTIME, PPN, AND TURNBINE_JOBNAME will
+# be ignored if MACHINE flag (see below) is not set
+export QUEUE=batch
+export WALLTIME=00:10:00
+export PPN=1
+export TURBINE_JOBNAME="${EXPID}_job"
+
+### set the desired number of processors
+PROCS=3
+###
+
+# Resident task workers and ranks
+export TURBINE_RESIDENT_WORK_WORKERS=1
+export RESIDENT_WORK_RANKS=$(( PROCS - 2 ))
+
+echo $PYTHONPATH
+
+# remove -l option for removing printing processors ranks
+# settings.json file has all the parameter combinations to be tested
+echo swift-t  -n $PROCS $APP_HOME/grid-sweep.swift $*
+swift-t  -l -n $PROCS $APP_HOME/rnd_or_grid.swift $* --benchmark_name=$B_NAME --search_type=$S_NAME --input_file=$JSON_F
diff --git a/workflows/rnd_or_grid/swift/theta_run.sh b/workflows/rnd_or_grid/swift/theta_run.sh
new file mode 100644
index 00000000..9002ffb0
--- /dev/null
+++ b/workflows/rnd_or_grid/swift/theta_run.sh
@@ -0,0 +1,81 @@
+#! /usr/bin/env bash
+set -eu
+
+# Autodetect this workflow directory
+export APP_HOME=$( cd $( dirname $0 ) ; /bin/pwd )
+
+#### set this variable to add new benchmarks directory
+RUNNERS_DIR=$APP_HOME/../../../../Benchmarks/Pilot1/P1B1:$APP_HOME/../../../../Benchmarks/Pilot2/P2B1:$APP_HOME/../../../../Benchmarks/Pilot3/P3B1:$APP_HOME/../../../../Benchmarks/Pilot1/NT3:$APP_HOME/../../../../Benchmarks/Pilot1/P1B3
+###
+# The number of MPI processes
+# Note that 2 processes are reserved for Swift/EMEMS
+# The default of 4 gives you 2 workers, i.e., 2 concurrent Keras runs
+export PROCS=${PROCS:-36}
+# MPI processes per node
+# Cori has 32 cores per node, 128GB per node
+export PPN=${PPN:-1}
+export QUEUE=${QUEUE:-default}
+export WALLTIME=${WALLTIME:-01:20:00}
+
+
+if [ "$#" -ne 4 ]; then
+  script_name=$(basename $0)
+  echo "Usage: ${script_name} EXPERIMENT_ID (run1_p1b1) BENCHMARKS_NAME (eg. p1b1) SEARCH_TYPE (eg. grid or random) INPUT_JSON"
+  echo "Example: ./run p1b1_experiment1 p1b1 random p1b1_settings.json"
+  echo "-This creates a p1b1_experiment1 directory in ../experiments"
+  echo " uses random scheme for variables specified in ../data/p1b1_settings.json file"
+  exit 1
+fi
+
+# uncomment to turn on swift/t logging. Can also set TURBINE_LOG,
+# TURBINE_DEBUG, and ADLB_DEBUG to 0 to turn off logging
+export TURBINE_LOG=1 TURBINE_DEBUG=1 ADLB_DEBUG=1
+
+export EXPID=$1
+export B_NAME=$2
+export S_NAME=$3
+export JSON_F=$4
+
+export TURBINE_OUTPUT=$APP_HOME/../experiments/$EXPID
+export PROJECT=Candle_ECP
+export TURBINE_JOBNAME="${EXPID}_job"
+
+TCL=/home/wozniak/Public/sfw/theta/tcl-8.6.1
+export R=/home/wozniak/Public/sfw/theta/R-3.4.0/lib64/R
+export PY=/home/rjain/anaconda2
+export LD_LIBRARY_PATH=$PY/lib:$R/lib:$LD_LIBRARY_PATH
+COMMON_DIR=$APP_HOME/../../common/python
+PYTHONPATH=$APP_HOME/../python:$RUNNERS_DIR:$COMMON_DIR
+PYTHONHOME=/home/rjain/anaconda2
+
+export PATH=/home/rjain/install/stc/bin:$TCL/bin:$PATH
+#$PYTHONHOME/bin:$TCL/bin:$PATH
+
+# Resident task workers and ranks
+export TURBINE_RESIDENT_WORK_WORKERS=1
+export RESIDENT_WORK_RANKS=$(( PROCS - 2 ))
+
+
+TURBINE_DIR=/home/rjain/install/turbine/lib
+
+# set machine to your scheduler type (e.g. pbs, slurm, cobalt etc.),
+# or empty for an immediate non-queued unscheduled run
+MACHINE="theta"
+
+if [ -n "$MACHINE" ]; then
+  MACHINE="-m $MACHINE"
+fi
+
+set -x
+WORKFLOW_SWIFT=rnd_or_grid.swift
+swift-t -n $PROCS $MACHINE -r $TURBINE_DIR \
+        -e LD_LIBRARY_PATH=$LD_LIBRARY_PATH \
+        -e TURBINE_RESIDENT_WORK_WORKERS=$TURBINE_RESIDENT_WORK_WORKERS \
+    -e RESIDENT_WORK_RANKS=$RESIDENT_WORK_RANKS \
+    -e APP_HOME=$APP_HOME \
+    -e PYTHONPATH=$PYTHONPATH \
+    -e PYTHONHOME=$PYTHONHOME \
+    -e TURBINE_DEBUG=$TURBINE_DEBUG\
+    -e ADLB_DEBUG=$ADLB_DEBUG \
+    -e TURBINE_OUTPUT=$TURBINE_OUTPUT \
+        $APP_HOME/$WORKFLOW_SWIFT  --benchmark_name=$B_NAME --search_type=$S_NAME --input_file=$JSON_F &