def run(cloudburst: CloudburstConnection,
num_requests: int,
data_size: str,
do_optimize: bool):
def stage1(self, row: Row) -> bytes:
import numpy as np
return np.random.rand(row['size'])
def stage2(self, row: Row) -> int:
return 3
print(f'Creating flow with {data_size} ({DATA_SIZES[data_size]}) inputs.')
flow = Flow('colocate-benchmark', FlowType.PUSH, cloudburst)
f1 = flow.map(stage1)
p1 = f1.map(stage2, names=['val1'])
p2 = f1.map(stage2, names=['val2'])
p3 = f1.map(stage2, names=['val3'])
p4 = f1.map(stage2, names=['val4'])
p5 = f1.map(stage2, names=['val5'])
# p6 = f1.map(stage2, names=['val6'])
# p7 = f1.map(stage2, names=['val7'])
# p8 = f1.map(stage2, names=['val8'])
p1.join(p2).join(p3).join(p4).join(p5) # .join(p6).join(p7).join(p8)
if do_optimize:
flow = optimize(flow, rules=optimize_rules)
print('Flow has been optimized...')
flow.deploy()
print('Flow successfully deployed!')
latencies = []
inp = Table([('size', IntType)])
inp.insert([DATA_SIZES[data_size]])
print('Starting benchmark...')
for i in range(num_requests):
if i % 100 == 0 and i > 0:
print(f'On request {i}...')
start = time.time()
res = flow.run(inp).get()
end = time.time()
latencies.append(end - start)
print_latency_stats(latencies, 'E2E')
def run(cloudburst: CloudburstConnection, num_requests: int, gamma: int,
num_replicas: int):
def stage1(self, val: int) -> int:
return val + 1
def stage2(self, row: Row) -> float:
import time
from scipy.stats import gamma
delay = gamma.rvs(3.0, scale=row['scale']) * 10 / 1000 # convert to ms
time.sleep(delay)
return delay
def stage3(self, row: Row) -> float:
return row['val']
print(f'Creating flow with {num_replicas} replicas and' +
f' gamma={GAMMA_VALS[gamma]}')
flow = Flow('fusion-benchmark', FlowType.PUSH, cloudburst)
flow.map(stage1, col='val') \
.map(stage2, names=['val'], high_variance=True) \
.map(stage3, names=['val'])
optimize_rules['compete_replicas'] = num_replicas
flow = optimize(flow, rules=optimize_rules)
print('Flow has been optimized...')
flow.deploy()
print('Flow successfully deployed!')
latencies = []
inp = Table([('val', IntType), ('scale', FloatType)])
inp.insert([1, GAMMA_VALS[gamma]])
print('Starting benchmark...')
for i in range(num_requests):
if i % 100 == 0 and i > 0:
print(f'On request {i}...')
time.sleep(.300) # Sleep to let the queue drain.
start = time.time()
res = flow.run(inp).get()
end = time.time()
latencies.append(end - start)
print_latency_stats(latencies, 'E2E')
def run(cloudburst: CloudburstConnection, num_requests: int, num_fns: int,
data_size: str, do_optimize: bool):
def fusion_op(self, row: Row) -> bytes:
return row['data']
print(f'Creating flow with {num_fns} operators and {data_size}' +
f' ({DATA_SIZES[data_size]}) inputs.')
flow = Flow('fusion-benchmark', FlowType.PUSH, cloudburst)
marker = flow
for _ in range(num_fns):
marker = marker.map(fusion_op, names=['data'])
if do_optimize:
flow = optimize(flow, rules=optimize_rules)
print('Flow has been optimized...')
flow.deploy()
print('Flow successfully deployed!')
latencies = []
inp = Table([('data', BtsType)])
inp.insert([os.urandom(DATA_SIZES[data_size])])
print('Starting benchmark...')
for i in range(num_requests):
if i % 100 == 0 and i > 0:
print(f'On request {i}...')
start = time.time()
res = flow.run(inp).get()
end = time.time()
latencies.append(end - start)
print_latency_stats(latencies, 'E2E')
names=['img', 'resnet_index', 'resnet_max_prob'],
gpu=gpu,
batching=gpu)
incept = rnet.filter(low_prob) \
.map(incept,
init=incept_cons,
names=['incept_index', 'incept_max_prob'],
gpu=gpu,
batching=gpu)
rnet.join(incept, how='left') \
.map(cascade_predict, init=cascade_init, names=['class'],
batching=False)
print('Optimizing flow...')
flow = optimize(flow, rules=optimize_rules)
print('Deploying flow...')
flow.deploy()
local = args.local[0].lower() == 'true'
if local:
run(flow, cloudburst, args.requests[0], local)
else:
flow.cloudburst = None # Hack to serialize and send flow.
queue = [flow]
while len(queue) > 0:
op = queue.pop(0)
op.cb_fn = None
queue.extend(op.downstreams)
def run(cloudburst: CloudburstConnection,
num_requests: int,
data_size: str,
breakpoint: bool,
do_optimize: bool):
print('Creating data...')
size = DATA_SIZES[data_size]
for i in range(1, NUM_DATA_POINTS+1):
arr = np.random.rand(size)
cloudburst.put_object('data-' + str(i), arr)
def stage1(self, row: Row) -> (int, str):
idx = int(row['req_num'] / 10) + 1
key = 'data-%d' % (idx)
return idx, key
def stage2(self, row: Row) -> str:
import numpy as np
arr = row[row['key']]
return float(np.sum(arr))
print(f'Creating flow with {data_size} ({DATA_SIZES[data_size]}) inputs.')
flow = Flow('locality-benchmark', FlowType.PUSH, cloudburst)
flow.map(stage1, names=['index', 'key']) \
.lookup('key', dynamic=True) \
.map(stage2, names=['sum'])
optimize_rules['breakpoint'] = breakpoint
if do_optimize:
flow = optimize(flow, rules=optimize_rules)
print('Flow has been optimized...')
flow.deploy()
print('Flow successfully deployed!')
latencies = []
inp = Table([('req_num', IntType)])
if breakpoint:
print('Starting warmup...')
for i in range(NUM_DATA_POINTS):
inp = Table([('req_num', IntType)])
inp.insert([i * 10])
res = flow.run(inp).get()
print('Pausing to let cache metadata propagate...')
time.sleep(15)
print('Starting benchmark...')
for i in range(num_requests):
if i % 100 == 0 and i > 0:
print(f'On request {i}...')
inp = Table([('req_num', IntType)])
inp.insert([i])
start = time.time()
res = flow.run(inp).get()
end = time.time()
latencies.append(end - start)
with open('data.bts', 'wb') as f:
from cloudburst.shared.serializer import Serializer
ser = Serializer()
bts = ser.dump(latencies)
f.write(bts)
print_latency_stats(latencies, 'E2E')