def test_edit_workload():
# Workloads to compare against
workload1 = Workload("test_workload(I_v3)", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 4000, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
workload2 = Workload("Small_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 200, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
# Call get_workloads
WL_data = aixprt.get_workloads()
assert len(WL_data) == 2
# Check that both workloads exist in the system
assert compare_workloads(WL_data["test_workload(I_v3)"], workload1)
assert compare_workloads(WL_data["Small_Workload"], workload2)
# Now edit the first workload
aixprt.edit_workload("test_workload(I_v3)", "workload_edited", "edited", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", "5000", "0.01", "10", "10", "20", "100", "-2", "100", "True", "0", "0", "0", "")
workload1_edited = Workload("workload_edited", "edited", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 5000, 0.01, 10, 10, 20, 100, -2, 100, True, 0, 0, 0, "")
# Enure it was edited properly
WL_data = aixprt.get_workloads()
assert len(WL_data) == 2
assert not "test_workload(I_v3)" in WL_data
assert "workload_edited" in WL_data
assert compare_workloads(WL_data["workload_edited"], workload1_edited)
# Now try to edit a workload to have all invalid parameters (with the exception of name, comment, and command)
actual_invalid = aixprt.edit_workload("Small_Workload", "invalidWL", "", '', "cat", "0.0.0.0", "1.0", "False", "1.0", "asdga100", "--2", "1%00", "FTrue", "z", "e", "ro", "")
expected_invalid = ["tfhub_model", "training_steps", "learning_rate", "testing_percentage", "validation_percentage", "eval_step_interval", "train_batch_size", "test_batch_size", "validation_batch_size", "flip_left_right", "random_crop", "random_scale", "random_brightness"]
WL_data = aixprt.get_workloads()
assert len(WL_data) == 2
assert len(expected_invalid) == 13
assert expected_invalid == actual_invalid
# Ensure that the workload was not edited
assert compare_workloads(WL_data["Small_Workload"], workload2)
def test_remove_workload():
expected_workload1 = Workload("test_workload(I_v3)", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 4000, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
expected_workload2 = Workload("Small_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 200, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
expected_workload3 = Workload("Smaller_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 100, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
# Ensure file is setup properly
WL_data = aixprt.get_workloads()
assert len(WL_data) == 2
assert "test_workload(I_v3)" in WL_data
assert "Small_Workload" in WL_data
# Try to remove a non-existent workload
assert aixprt.remove_workload("Not_in_file") == 0
assert len(WL_data) == 2
assert "test_workload(I_v3)" in WL_data
assert "Small_Workload" in WL_data
# Remove an existing workload
actual_workload1 = aixprt.remove_workload("test_workload(I_v3)")
WL_data = aixprt.get_workloads()
assert len(WL_data) == 1
assert not "test_workload(I_v3)" in WL_data
assert compare_workloads(expected_workload1, actual_workload1)
# Remove an existing workload that is used in a suite
suite1 = aixprt.get_suite("Small_Suite")
assert len(suite1) == 2
actual_workload2 = aixprt.remove_workload("Small_Workload")
WL_data = aixprt.get_workloads()
assert len(WL_data) == 0
assert not "Small_Workload" in WL_data
assert compare_workloads(expected_workload2, actual_workload2)
suite1 = aixprt.get_suite("Small_Suite")
assert len(suite1) == 1
assert not "Small_Workload" in suite1
# Remove a workload that occurs multiple times in multiple suites
# Setup for this particular test:
aixprt.add_workload("test_workload(I_v3)", "", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "4100", "0.01", "10", "10", "10", "100", "-1", "100", "False", "0", "0", "0", "")
aixprt.add_workload("Small_Workload", "", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "200", "0.01", "10", "10", "10", "100", "-1", "100", "False", "0", "0", "0", "")
aixprt.add_workload("Smaller_Workload", "", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "100", "0.01", "10", "10", "10", "100", "-1", "100", "False", "0", "0", "0", "")
WL_data = aixprt.get_workloads()
assert len(WL_data) == 3
suite2_A = aixprt.get_suite("Small_Suite")
assert len(suite2_A) == 1
suite2_B = aixprt.get_suite("Smaller_Suite")
assert len(suite2_B) == 2
# Now remove
actual_workload3 = aixprt.remove_workload("Smaller_Workload")
WL_data = aixprt.get_workloads()
assert len(WL_data) == 2
assert not "Smaller_Workload" in WL_data
assert compare_workloads(expected_workload3, actual_workload3)
suite2_A = aixprt.get_suite("Small_Suite")
assert len(suite2_A) == 0
assert not "Smaller_Workload" in suite2_A
suite2_B = aixprt.get_suite("Smaller_Suite")
assert len(suite2_B) == 0
assert not "Smaller_Workload" in suite2_B
def test_get_workloads():
workload1 = Workload("test_workload(I_v3)", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 4000, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
workload2 = Workload("Small_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 200, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
# Call get_workloads
WL_data = aixprt.get_workloads()
# Check that there are only 2 workloads in the list and ensure both are correct
assert len(WL_data) == 2
assert "test_workload(I_v3)" in WL_data
assert "Small_Workload" in WL_data
assert compare_workloads(WL_data["test_workload(I_v3)"], workload1)
assert compare_workloads(WL_data["Small_Workload"], workload2)
def generateWorkload(self):
W = []
logging.basicConfig(filename='example.log', level=logging.DEBUG)
creation_Time = 0
i = 0
TotalCPUTime = np.random.normal(self.mean_job_time, self.sd_job_time)
TotalCPUTime = round(TotalCPUTime * self.time_unit)
logging.info('Task' + str(i) + ":Creation Time:" + str(creation_Time) +
":Total CPU time:" + str(TotalCPUTime))
IO = IOGenerator(self.rate_io, TotalCPUTime, self.mean_io_time,
self.sd_io_time, self.time_unit)
iolist = IOList(IO.generateIOEvent())
T = Task("Task 0", 0, np.abs(creation_Time), np.abs(TotalCPUTime),
iolist)
W.append(T)
for i in xrange(self.num_jobs):
creation_Time = round(
random.expovariate(self.rate_jobs) *
self.time_unit) + W[-1].creationTime
TotalCPUTime = np.random.normal(self.mean_job_time,
self.sd_job_time)
TotalCPUTime = round(TotalCPUTime * self.time_unit)
logging.info('Task' + str(i) + ":Creation Time:" +
str(creation_Time) + ":Total CPU time:" +
str(TotalCPUTime))
IO = IOGenerator(self.rate_io, TotalCPUTime, self.mean_io_time,
self.sd_io_time, self.time_unit)
iolist = IOList(IO.generateIOEvent())
T = Task("Task " + str(i), 0, np.abs(creation_Time),
np.abs(TotalCPUTime), iolist)
W.append(T)
self.work_load_list = W
w = Workload(W)
return w
def create_workload(self, name, image="busybox", network=None):
"""
Create a workload container inside this host container.
"""
workload = Workload(self, name, image=image, network=network)
self.workloads.add(workload)
return workload
def create_workload(self, name, image="busybox", network="bridge", ip=None, labels=[]):
"""
Create a workload container inside this host container.
"""
workload = Workload(self, name, image=image, network=network, ip=ip, labels=labels)
self.workloads.add(workload)
return workload
def get_workloads():
"""
Retrieves the current workloads in the workloads.json file and returns them as a dictionary of paired
workload names and Workload objects
:return: A dictionary with each workload name being used as keys to access the related workload objects
"""
data = {}
json_path = Path("workloads.json")
if (not json_path.is_file()):
return data
data = load_workloads()
workloads_dict = {}
for x in data['workloads']:
wl = Workload(x['name'], x['comment'], x['tfhub model'],
x['training steps'], x['learning rate'],
x['testing percentage'], x['validation percentage'],
x['eval step interval'], x['train batch size'],
x['test batch size'], x['validation batch size'],
x['flip left/right'], x['random crop'],
x['random scale'], x['random brightness'], x['command'])
workloads_dict[x['name']] = wl
return workloads_dict
def workload():
"""
A fixture for a Workload.
"""
_workload = Workload()
yield _workload
_workload.terminate()
def generate_query(self):
if 'workload' in self.scheme:
rel_list = [(x.source, x.target, x.label)
for x in self.relation_ins]
wl = Workload(list(self.node_labels), rel_list, self.workload)
queries = wl.generate_workload()
filename = 'query.cypher'
tl = Translate(filename)
tl.translate(queries)
def remove_workload(workload_name):
"""
Removes a workload from the workloads.json
:param workload_name: name of the workload being removed\n
:return: the removed workload, if it was sucessfuly removed from the list; else, it returns 0\n
"""
# Opening workloads.json into data
data = {}
json_path = Path("workloads.json")
if (not json_path.is_file()):
return data
data = load_workloads()
removed_WL = {}
index = 0
# Traverses through the list of workloads until it finds a matching name,
# then it removes the workload from the list and saves it to removed_WL
for x in data['workloads']:
if x['name'] == workload_name:
removed_WL = data['workloads'].pop(index)
index += 1
# If a workload was removed, then update the json and return the removed workload
if removed_WL:
with open('workloads.json', 'w') as json_file:
json.dump(data, json_file, indent=4)
json_file.close()
# Check if that workload was in any suites
suite_appearances = is_workload_in_suites(workload_name)
# If so, then remove them from the suites
if not suite_appearances == 1:
count = len(suite_appearances) - 1
while count >= 0:
remove_workload_from_suite(suite_appearances[count][1],
suite_appearances[count][0])
count -= 1
# Create an actual workload object with removed_WL
removed_WL = Workload(
removed_WL['name'], removed_WL['comment'],
removed_WL['tfhub model'], removed_WL['training steps'],
removed_WL['learning rate'], removed_WL['testing percentage'],
removed_WL['validation percentage'],
removed_WL['eval step interval'], removed_WL['train batch size'],
removed_WL['test batch size'], removed_WL['validation batch size'],
removed_WL['flip left/right'], removed_WL['random crop'],
removed_WL['random scale'], removed_WL['random brightness'],
removed_WL['command'])
return removed_WL
# Else, return 0
return 0
def dummyGen():
ioList1 = IOList([IO(10, 5), IO(13, 7)])
ioList2 = IOList([])
# high priority IO instensive task
t1 = Task("Task 1", 0, 3, 20, ioList1)
# low priority CPU intensive task
t2 = Task("Task 2", 10, 0, 15, ioList2)
w = Workload([t1, t2])
return w
def create_workload(self,
base_name,
image="busybox",
network="bridge",
ip=None,
labels=[],
namespace=None):
"""
Create a workload container inside this host container.
"""
name = base_name + "_" + \
''.join([random.choice(string.ascii_letters) for ii in range(6)]).lower()
workload = Workload(self,
name,
image=image,
network=network,
ip=ip,
labels=labels,
namespace=namespace)
self.workloads.add(workload)
return workload
def test_add_workload():
WL_data = aixprt.get_workloads()
workload1 = Workload("test_workload1", "test comment", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', 4100, 0.02, 10, 20, 10, 100, -2, 100, True, 0, 0, 0, "")
# Test to ensure that the workload to be added does not exist in the file
assert not "test_workload1" in WL_data
# Then add and assert that it does exist in it
aixprt.add_workload("test_workload1", "test comment", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "4100", "0.02", "10", "20", "10", "100", "-2", "100", "True", "0", "0", "0", "")
WL_data = aixprt.get_workloads()
assert "test_workload1" in WL_data
assert compare_workloads(WL_data["test_workload1"], workload1)
# Try to add the same workload again and ensure it does not
assert len(WL_data) == 3
assert aixprt.add_workload("test_workload1", "test comment", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "4100", "0.02", "10", "20", "10", "100", "-2", "100", "True", "0", "0", "0", "") == 1
WL_data = aixprt.get_workloads()
assert len(WL_data) == 3
# Try to add invalid workloads and ensure it does not
# Workload with 1 thing wrong
invalid_1 = aixprt.add_workload("wrong_workload", "test comment", 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1', "4100", "0.02", "1.0", "20", "10", "100", "-2", "100", "True", "0", "0", "0", "")
expected_invalid_1 = ["testing_percentage"]
WL_data = aixprt.get_workloads()
assert len(WL_data) == 3
assert len(invalid_1) == 1
assert invalid_1 == expected_invalid_1
# Workload with everything wrong (except for the comment and command parameters, which can both be empty strings)
invalid_2 = aixprt.add_workload("", "", '', "cat", "0..02", "1.0", "False", "1.0", "asdga100", "--2", "1%00", "FTrue", "z", "e", "ro", "")
expected_invalid_2 = ["name", "tfhub_model", "training_steps", "learning_rate", "testing_percentage", "validation_percentage", "eval_step_interval", "train_batch_size", "test_batch_size", "validation_batch_size", "flip_left_right", "random_crop", "random_scale", "random_brightness"]
WL_data = aixprt.get_workloads()
assert len(WL_data) == 3
assert len(invalid_2) == 14 #TODO fix after regex is corrected
assert invalid_2 == expected_invalid_2
# Add a workload with a command
WL_data = aixprt.get_workloads()
assert len(WL_data) == 3
assert aixprt.add_workload("test_workload2", "", '', "", "", "", "", "", "", "", "", "", "", "", "", "help dir") == 0
WL_data = aixprt.get_workloads()
assert len(WL_data) == 4
def parse_config(fpath):
conf = yaml.load(open(fpath, 'r'), Loader=yaml.FullLoader)['workload']
if conf['interarrival'].lower().endswith('s'):
conf['interarrival'] = int(conf['interarrival'].lower().rsplit('s',
1)[0])
if conf['duration'].lower().endswith('s'):
conf['duration'] = int(conf['duration'].lower().rsplit('s', 1)[0])
elif conf['duration'].lower().endswith('m'):
conf['duration'] = int(conf['duration'].lower().rsplit('m', 1)[0]) * 60
elif conf['duration'].lower().endswith('h'):
conf['duration'] = int(conf['duration'].lower().rsplit('h',
1)[0]) * 3600
if conf['cache']['size'].lower().endswith('g'):
conf['cache']['size'] = int(conf['cache']['size'].lower().rsplit(
'g', 1)[0]) * (1 << 30)
return conf
if __name__ == '__main__':
conf_file = sys.argv[1] if len(sys.argv) > 1 else 'config.yaml'
conf = parse_config(conf_file)
env = simpy.Environment()
workload = Workload(conf, type='simulation', env=env)
env.run(until=conf['duration'])
workload.dump_stats()
def add_workload(name,
comment,
tfhub_model,
training_steps,
learning_rate,
testing_percentage,
validation_percentage,
eval_step_interval,
train_batch_size,
test_batch_size,
validation_batch_size,
flip_left_right,
random_crop,
random_scale,
random_brightness,
command=None):
"""
Adds a new workload entry to the workloads.json file
:param name: Name of the new workload\n
:param comment: Optional workload comment/description of the workload\n
:param tfhub_model: URL pertaining to the machine learning model that is to be used from Tensorflow's github\n
:param training_steps: The number of training steps that are run before ending\n
:param learning_rate: Numerical rate pertaining to the rate of learning when training\n
:param testing_percentage: Percentage of images to use as a test set\n
:param validation_percentage: Percentage of images to use as a validation set\n
:param eval_step_interval: Number of steps to evaluate the training results after\n
:param train_batch_size: Number of images to train on at a time\n
:param test_batch_size: Number of images to test on. "-1" causes the entire test set to be used\n
:param validation_batch_size: Number of images to use in the evaluation batch. "-1" causes the entire validation set to be used\n
:param flip_left_right: True or False, whether to randomly flip half of the training images horizontally\n
:param random_crop: Percentage determining how much of a margin to randomly crop off the training images\n
:param random_scale: Percentage determining how much to randomly scale the size of the training images by\n
:param random_brightness: Percentage determining how much to randomly multiply the training image input pixels up or down by\n
:param command: Optional console command to be run upon running this workload\n
:return: A list of invalid parameters if an error has occured. A 0 is returned on success\n
"""
# Creating a new workload to validate parameters
new_WL = Workload(name, comment, tfhub_model, training_steps,
learning_rate, testing_percentage, validation_percentage,
eval_step_interval, train_batch_size, test_batch_size,
validation_batch_size, flip_left_right, random_crop,
random_scale, random_brightness, command)
invalid_params = ""
invalid_params = new_WL.validate_parameters()
# If parameters are valid, then try to open workloads.json
if len(invalid_params) == 0:
json_path = Path("workloads.json")
if json_path.is_file():
data = load_workloads()
for x in data['workloads']:
# Check if a workload with the same name already exists; if so, don't add this new one
if (x['name'] == name):
print(f"Workload {name} already exists.")
return 1
# If workloads.json exists and the workload is acceptable, then add it to the workloads dictionary
else:
data = {}
data['workloads'] = []
data['workloads'].append({
'name': new_WL.name,
'comment': new_WL.comment,
'tfhub model': new_WL.tfhub_model,
'training steps': new_WL.training_steps,
'learning rate': new_WL.learning_rate,
'testing percentage': new_WL.testing_percentage,
'validation percentage': new_WL.validation_percentage,
'eval step interval': new_WL.eval_step_interval,
'train batch size': new_WL.train_batch_size,
'test batch size': new_WL.test_batch_size,
'validation batch size': new_WL.validation_batch_size,
'flip left/right': new_WL.flip_left_right,
'random crop': new_WL.random_crop,
'random scale': new_WL.random_scale,
'random brightness': new_WL.random_brightness,
'command': new_WL.command
})
# Put updated dictionary into workloads.json
with open('workloads.json', 'w') as json_file:
json.dump(data, json_file, indent=4)
json_file.close()
# Success
return 0
else:
return invalid_params
def edit_workload(original_name,
new_name,
comment,
tfhub_model,
training_steps,
learning_rate,
testing_percentage,
validation_percentage,
eval_step_interval,
train_batch_size,
test_batch_size,
validation_batch_size,
flip_left_right,
random_crop,
random_scale,
random_brightness,
command=None):
"""
Edits an existing workload in the workloads.json file
:param original_name: Name of the workload to be edited\n
:param new_name: Name of the new workload\n
:param comment: Optional workload comment/description of the workload\n
:param tfhub_model: URL pertaining to the machine learning model that is to be used from Tensorflow's github\n
:param training_steps: The number of training steps that are run before ending\n
:param learning_rate: Numerical rate pertaining to the rate of learning when training\n
:param testing_percentage: Percentage of images to use as a test set\n
:param validation_percentage: Percentage of images to use as a validation set\n
:param eval_step_interval: Number of steps to evaluate the training results after\n
:param train_batch_size: Number of images to train on at a time\n
:param test_batch_size: Number of images to test on. "-1" causes the entire test set to be used\n
:param validation_batch_size: Number of images to use in the evaluation batch. "-1" causes the entire validation set to be used\n
:param flip_left_right: True or False, whether to randomly flip half of the training images horizontally\n
:param random_crop: Percentage determining how much of a margin to randomly crop off the training images\n
:param random_scale: Percentage determining how much to randomly scale the size of the training images by\n
:param random_brightness: Percentage determining how much to randomly multiply the training image input pixels up or down by\n
:param command: Optional console command to be run upon running this workload\n
:return: A list of invalid parameters if an error has occured\n
"""
# Creating a updated workload to validate parameters
new_WL = Workload(new_name, comment, tfhub_model, training_steps,
learning_rate, testing_percentage, validation_percentage,
eval_step_interval, train_batch_size, test_batch_size,
validation_batch_size, flip_left_right, random_crop,
random_scale, random_brightness, command)
invalid_params = ""
invalid_params = new_WL.validate_parameters()
# If parameters are valid, then try to open workloads.json
if len(invalid_params) == 0:
data = {}
json_path = Path("workloads.json")
# Check if the workloads file exists in order to edit it
if (not json_path.is_file()):
print("There are currently no workloads to edit.")
return
# If it exists, then load the workloads from it
data = load_workloads()
# Edit the parameters of the workload corresponding to the original name
for x in data['workloads']:
if x['name'] == original_name:
x['name'] = new_WL.name
x['comment'] = new_WL.comment
x['tfhub model'] = new_WL.tfhub_model
x['training steps'] = new_WL.training_steps
x['learning rate'] = new_WL.learning_rate
x['testing percentage'] = new_WL.testing_percentage
x['validation percentage'] = new_WL.validation_percentage
x['eval step interval'] = new_WL.eval_step_interval
x['train batch size'] = new_WL.train_batch_size
x['test batch size'] = new_WL.test_batch_size
x['validation batch size'] = new_WL.validation_batch_size
x['flip left/right'] = new_WL.flip_left_right
x['random crop'] = new_WL.random_crop
x['random scale'] = new_WL.random_scale
x['random brightness'] = new_WL.random_brightness
x['command'] = new_WL.command
# Put updated dictionary into workloads.json
with open('workloads.json', 'w') as json_file:
json.dump(data, json_file, indent=4)
json_file.close()
return 0
else:
return invalid_params
from mongo import Mongo
from pymongo import *
from query import Query
import json
from bson import json_util
from datetime import datetime
from workload import Workload
if __name__ == "__main__":
# Connect to Mongo
mongo = Mongo()
db = mongo.getDb()
pipeline = Workload().getWorkload()
f = open("demofile.md", "w")
lst = db.list_collection_names()
lst.sort()
f.write('# Execution stats for each query')
f.write('\n')
f.write('\n')
for i in lst:
f.write('- [' + i + '](#' + i + ')')
f.write('\n')
f.write('\n')
for i in lst:
f.write('## ')
def main(file_path):
workload = Workload(file_path)
for t, workload in workload.next():
time.sleep(1) # sleep for 1 sec
periodic_curl(workload)
def main():
# NOTE: you can set invoke count of each function below the chain definitions (the concurrencies variable)
# aws
lambda1 = LambdaFunction(url="https://lambda1.com", id=1)
lambda2 = LambdaFunction(url="https://lambda2.com", id=2)
lambda3 = LambdaFunction(url="https://lambda3.com", id=3)
lambda4 = LambdaFunction(url="https://lambda4.com", id=4)
## Chain 0 ##
node1_c0 = ChainNode(function=lambda1, nodeID=1, children=[], lastNodeIDs=[1], chainFunctionIDs=[1], args={})
chain0 = node1_c0
## Chain 1 ##
# L2
# /
# L1
# \
# L3
node3_c1 = ChainNode(function=lambda3, nodeID=3, children=[], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
node2_c1 = ChainNode(function=lambda2, nodeID=2, children=[], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
node1_c1 = ChainNode(function=lambda1, nodeID=1, children=[node2_c1, node3_c1], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
chain1 = node1_c1
## Chain 2 ##
# L3-L2-L1
node3_c2 = ChainNode(function=lambda1, nodeID=3, children=[], lastNodeIDs=[3], chainFunctionIDs=[1,2,3], args={})
node2_c2 = ChainNode(function=lambda2, nodeID=2, children=[node3_c2], lastNodeIDs=[3], chainFunctionIDs=[1,2,3], args={})
node1_c2 = ChainNode(function=lambda3, nodeID=1, children=[node2_c2], lastNodeIDs=[3], chainFunctionIDs=[1,2,3], args={})
chain2 = node1_c2
## Chain 3 ##
# L2
# /
# L1
# \
# L3-L4-L4-L4
node6_c3 = ChainNode(function=lambda4, nodeID=6, children=[], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
node5_c3 = ChainNode(function=lambda4, nodeID=5, children=[node6_c3], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
node4_c3 = ChainNode(function=lambda4, nodeID=4, children=[node5_c3], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
node3_c3 = ChainNode(function=lambda3, nodeID=3, children=[node4_c3], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
node2_c3 = ChainNode(function=lambda2, nodeID=2, children=[], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
node1_c3 = ChainNode(function=lambda1, nodeID=1, children=[node2_c1, node3_c1], lastNodeIDs=[6,2], chainFunctionIDs=[1,2,3,4,4,4], args={})
chain3 = node1_c3
## Chain 4 ##
# L2
# /
# L1-L3
# \
# L4
node4_c4 = ChainNode(function=lambda4, nodeID=4, children=[], lastNodeIDs=[2,3,4], chainFunctionIDs=[1,2,3,4], args={})
node3_c4 = ChainNode(function=lambda3, nodeID=3, children=[], lastNodeIDs=[2,3,4], chainFunctionIDs=[1,2,3,4], args={})
node2_c4 = ChainNode(function=lambda2, nodeID=2, children=[], lastNodeIDs=[2,3,4], chainFunctionIDs=[1,2,3,4], args={})
node1_c4 = ChainNode(function=lambda1, nodeID=1, children=[node2_c4, node3_c4, node4_c4], lastNodeIDs=[2,3,4], chainFunctionIDs=[1,2,3,4], args={})
chain4 = node1_c4
## Chain 5 ##
# L2
# /
# L1
# \
# L3-L3
node4_c5 = ChainNode(function=lambda3, nodeID=4, children=[], lastNodeIDs=[4,2], chainFunctionIDs=[1,2,3,3], args={})
node3_c5 = ChainNode(function=lambda3, nodeID=3, children=[node4_c5], lastNodeIDs=[4,2], chainFunctionIDs=[1,2,3,3], args={})
node2_c5 = ChainNode(function=lambda2, nodeID=2, children=[], lastNodeIDs=[4,2], chainFunctionIDs=[1,2,3,3], args={})
node1_c5 = ChainNode(function=lambda1, nodeID=1, children=[node2_c5, node3_c5], lastNodeIDs=[4,2], chainFunctionIDs=[1,2,3,3], args={})
chain5 = node1_c5
######################################################################################################################
# NOTE: real applications
## ObjectDetection using openCV
# L2
# /
# L1
# \
# L3
objectDetection1 = LambdaFunction(url="image-preprocessing.com", id=1)
objectDetection2 = LambdaFunction(url="opencv_maskrcnn.com", id=2)
objectDetection3 = LambdaFunction(url="opencv_yolo.com", id=3)
node3_c6 = ChainNode(function=objectDetection3, nodeID=3, children=[], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
node2_c6 = ChainNode(function=objectDetection2, nodeID=2, children=[], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
node1_c6 = ChainNode(function=objectDetection1, nodeID=1, children=[node2_c6, node3_c6], lastNodeIDs=[2,3], chainFunctionIDs=[1,2,3], args={})
chain6 = node1_c6
concurrencies = [
{"chain": chain0, "count": 0},
{"chain": chain1, "count": 1},
{"chain": chain2, "count": 1},
{"chain": chain3, "count": 0},
{"chain": chain4, "count": 0},
{"chain": chain5, "count": 1},
{"chain": chain6, "count": 0},
]
chain_fan2 = Workload("Fan2", 5, 20, chain1, 1, 1000)
chain_linear = Workload("Linear", 5, 20, chain2, 2, 1000)
chain_fan_linear = Workload("FanAndLinear", 5, 20, chain5, 5, 1000)
# Set up Kafka
# kafka_url = "localhost:9092"
# kafka_pq_topic = "pending_queue"
# producer_PQ = connect_kafka_producer(kafka_url)
# # Producer Side Logging
# kafka_psq_topic = "producer_side_logging_queue"
# producer_PSQ = connect_kafka_producer(kafka_url)
# Functions are invoked here
chain_fan2.startWorkload(producer_PQ, producer_PSQ)
chain_linear.startWorkload(producer_PQ, producer_PSQ)
chain_fan_linear.startWorkload(producer_PQ, producer_PSQ)
producer_PQ.close()
producer_PSQ.close()
from mongo import Mongo
from workload import Workload
from query import Query
import pandas as pd
from table import Table
if __name__ == "__main__":
mongo = Mongo()
db = mongo.getDb()
list_of_collections = db.list_collection_names()
workload = Workload()
pipeline = workload.getWorkload()
data = []
for i in pipeline:
dct = {}
for j in list_of_collections:
q = Query(db, j, i)
dct[j] = q.getQueryExecTime()
data.append(dct)
df = pd.DataFrame(data)
df_total = pd.DataFrame([dict(df.sum())])
list_of_queries= []
for i in range(len(pipeline)):
list_of_queries.append('Query '+str(i+1))
df['Query'] = list_of_queries
df.set_index('Query', inplace=True)
def getAllStages(execStats):
s = set()
if type(execStats) == type({}):
for i in execStats:
if i == 'stage':
s.add(execStats[i])
else:
s.update(getAllStages(execStats[i]))
elif type(execStats) == type([]):
for i in execStats:
s.update(getAllStages(i))
return s
if __name__ == "__main__":
p = Workload()
mongo = Mongo()
db = mongo.getDb()
list_of_collections = db.list_collection_names()
list_of_collections.sort()
pipeline = p.getWorkload()
f = open("list_operations.md", "w")
for i in list_of_collections:
f.write('## ')
f.write(i)
f.write('\n')
f.write('\n')
f.write('| Query | Operations |')
f.write('\n')
f.write('|---|---|')
f.write('\n')
def test_compare_workloads():
workloadA_1 = Workload("Small_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 200, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
workloadA_2 = Workload("Small_Workload", "", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 200, 0.01, 10, 10, 10, 100, -1, 100, False, 0, 0, 0, "")
workloadB = Workload("test_workload(I_v3)", "comment", "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1", 4000, 0.02, 30, 10, 110, 100, -3, 100, True, 0, 0, 0, "")
assert compare_workloads(workloadA_1, workloadA_2)
assert not compare_workloads(workloadA_1, workloadB)
