-
-
Notifications
You must be signed in to change notification settings - Fork 773
/
data_loader.py
583 lines (531 loc) · 21.4 KB
/
data_loader.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
import json
import random
import numpy as np
import paho.mqtt.client as mqtt_client
import requests
import torch
from .stackoverflow_lr.data_loader import load_partition_data_federated_stackoverflow_lr
from .FederatedEMNIST.data_loader import load_partition_data_federated_emnist
from .ImageNet.data_loader import load_partition_data_ImageNet
from .Landmarks.data_loader import load_partition_data_landmarks
from .MNIST.data_loader import load_partition_data_mnist, download_mnist
from .cifar10.data_loader import load_partition_data_cifar10
from .cifar10.efficient_loader import efficient_load_partition_data_cifar10
from .cifar100.data_loader import load_partition_data_cifar100
from .cinic10.data_loader import load_partition_data_cinic10
from .edge_case_examples.data_loader import load_poisoned_dataset
from .fed_cifar100.data_loader import load_partition_data_federated_cifar100
from .fed_shakespeare.data_loader import load_partition_data_federated_shakespeare
from .file_operation import *
from .shakespeare.data_loader import load_partition_data_shakespeare
from .stackoverflow_nwp.data_loader import load_partition_data_federated_stackoverflow_nwp
from ..core.mlops import MLOpsConfigs
import boto3
from botocore.config import Config
def connect_mqtt(mqtt_config) -> mqtt_client:
def on_connect(client, userdata, flags, rc):
if rc == 0:
print("Connected to MQTT Host!")
else:
print("Failed to connect, return code %d\n", rc)
# generate client ID with pub prefix randomly
client_id = f"python-mqtt-{random.randint(0, 1000)}"
client = mqtt_client.Client(client_id, clean_session=False)
client.username_pw_set(mqtt_config["MQTT_USER"], mqtt_config["MQTT_PWD"])
client.connect(mqtt_config["BROKER_HOST"], mqtt_config["BROKER_PORT"])
return client
def subscribe(s3_obj, BUCKET_NAME, client: mqtt_client, args):
def on_message(client, userdata, msg):
logging.info(f"Received `{msg.payload.decode()}` from `{msg.topic}` topic")
if msg.payload.decode():
disconnect(client)
make_dir(
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % args.client_id,
)
)
# start download the file
download_s3_file(
s3_obj,
BUCKET_NAME,
json.loads(msg.payload.decode())["edge_id"],
json.loads(msg.payload.decode())["dataset"],
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % args.client_id,
),
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % args.client_id,
"cifar-10-python.tar.gz",
),
)
topic = "data_svr/dataset/%s" % args.client_id
client.subscribe(topic)
client.on_message = on_message
def disconnect(client: mqtt_client):
client.disconnect()
logging.info(f"Received message, Mqtt stop listen.")
def setup_s3_service(s3_config):
_config = Config(
retries={
'max_attempts': 4,
'mode': 'standard'
}
)
# s3 client
s3 = boto3.client('s3', region_name=s3_config["CN_REGION_NAME"], aws_access_key_id=s3_config["CN_S3_AKI"],
aws_secret_access_key=s3_config["CN_S3_SAK"], config=_config)
BUCKET_NAME = s3_config["BUCKET_NAME"]
return s3, BUCKET_NAME
def data_server_preprocess(args):
mqtt_config, s3_config, _, _ = MLOpsConfigs.fetch_all_configs()
s3_obj, BUCKET_NAME = setup_s3_service(s3_config)
args.private_local_data = ""
if args.process_id == 0:
pass
else:
client = connect_mqtt(mqtt_config)
subscribe(s3_obj, BUCKET_NAME, client, args)
if args.dataset == "cifar10":
# Mlops Run
# check mlops run_status
private_local_dir, split_status, edgeids, dataset_s3_key = check_rundata(args)
args.private_local_data = private_local_dir
# MLOPS Run. User supply the local data dir
if len(args.private_local_data) != 0:
logging.info("User has set the private local data dir")
disconnect(client)
# MLOPS Run need to Split Data
elif len(args.synthetic_data_url) != 0:
if split_status == 0 or split_status == 3:
logging.info("Data Server Start Splitting Dataset")
split_edge_data(args, edgeids)
elif split_status == 1:
logging.info("Data Server Is Splitting Dataset, Waiting For Mqtt Message")
elif split_status == 2:
logging.info("Data Server Splitted Dataset Complete")
query_data_server(args, args.client_id, s3_obj, BUCKET_NAME)
disconnect(client)
elif len(args.data_cache_dir) != 0:
logging.info("No synthetic data url and private local data dir")
return
client.loop_forever()
def split_edge_data(args, edge_list=None):
try:
url = "http://127.0.0.1:5000/split_dataset"
edge_list = json.loads(edge_list)
json_params = {"runId": args.run_id, "edgeIds": edge_list, "dataset": args.dataset}
response = requests.post(
url, json=json_params, verify=True, headers={"content-type": "application/json", "Connection": "keep-alive"}
)
result = response.json()["errno"]
return result
except requests.exceptions.SSLError as err:
print(err)
def check_rundata(args):
# local simulation run
logging.info("Checking Run Data")
# mlops run
try:
url = "http://127.0.0.1:5000/check_rundata"
json_params = {
"runId": args.run_id,
}
response = requests.post(
url,
json=json_params,
verify=True,
headers={"content-type": "application/json", "Connection": "keep-alive"},
)
return response.json()["private_local_dir"], response.json()["split_status"], response.json()["edgeids"], response.json()["dataset_s3_key"]
except requests.exceptions.SSLError as err:
print(err)
def query_data_server(args, edgeId, s3_obj, BUCKET_NAME):
try:
url = "http://127.0.0.1:5000/get_edge_dataset"
json_params = {"runId": args.run_id, "edgeId": edgeId}
response = requests.post(
url, json=json_params, verify=True, headers={"content-type": "application/json", "Connection": "keep-alive"}
)
if response.json()["errno"] == 0:
if not check_is_download(
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % edgeId,
"cifar-10-batches-py",
)
):
make_dir(
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % edgeId,
)
)
# start download the file
download_s3_file(
s3_obj,
BUCKET_NAME,
edgeId,
response.json()["dataset_key"],
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % edgeId,
),
os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % edgeId,
"cifar-10-python.tar.gz",
),
)
else:
logging.info("Edge Data Already Exists. Start Training Now.")
return response.json()
except requests.exceptions.SSLError as err:
print(err)
return err
def load(args):
return load_synthetic_data(args)
def combine_batches(batches):
full_x = torch.from_numpy(np.asarray([])).float()
full_y = torch.from_numpy(np.asarray([])).long()
for (batched_x, batched_y) in batches:
full_x = torch.cat((full_x, batched_x), 0)
full_y = torch.cat((full_y, batched_y), 0)
return [(full_x, full_y)]
def load_synthetic_data(args):
if args.training_type == "cross_silo" and args.dataset == "cifar10" and hasattr(args, 'synthetic_data_url') and args.synthetic_data_url.find("https") != -1:
data_server_preprocess(args)
dataset_name = args.dataset
# check if the centralized training is enabled
centralized = True if (args.client_num_in_total == 1 and args.training_type != "cross_silo") else False
# check if the full-batch training is enabled
args_batch_size = args.batch_size
if args.batch_size <= 0:
full_batch = True
args.batch_size = 128 # temporary batch size
else:
full_batch = False
if dataset_name == "mnist":
download_mnist(args.data_cache_dir)
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_mnist(
args,
args.batch_size,
train_path=os.path.join(args.data_cache_dir, "MNIST", "train"),
test_path=os.path.join(args.data_cache_dir, "MNIST", "test"),
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_federated_emnist(args.dataset, args.data_cache_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_federated_shakespeare(args.dataset, args.data_cache_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_federated_cifar100(args.dataset, args.data_cache_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_cache_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
client_num,
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_cache_dir)
args.client_num_in_total = client_num
elif dataset_name == "ILSVRC2012":
logging.info("load_data. dataset_name = %s" % dataset_name)
(
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_ImageNet(
dataset=dataset_name,
data_dir=args.data_cache_dir,
partition_method=None,
partition_alpha=None,
client_number=args.client_num_in_total,
batch_size=args.batch_size,
)
elif dataset_name == "gld23k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 233
fed_train_map_file = os.path.join(args.data_cache_dir, "mini_gld_train_split.csv")
fed_test_map_file = os.path.join(args.data_cache_dir, "mini_gld_test.csv")
(
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_landmarks(
dataset=dataset_name,
data_dir=args.data_cache_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None,
partition_alpha=None,
client_number=args.client_num_in_total,
batch_size=args.batch_size,
)
elif dataset_name == "gld160k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 1262
fed_train_map_file = os.path.join(args.data_cache_dir, "federated_train.csv")
fed_test_map_file = os.path.join(args.data_cache_dir, "test.csv")
(
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = load_partition_data_landmarks(
dataset=dataset_name,
data_dir=args.data_cache_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None,
partition_alpha=None,
client_number=args.client_num_in_total,
batch_size=args.batch_size,
)
else:
if dataset_name == "cifar10":
if hasattr(args, "synthetic_data_url") or hasattr(args, "private_local_data"):
if hasattr(args, "synthetic_data_url"):
args.private_local_data = ""
else:
args.synthetic_data_url = ""
if args.process_id != 0:
args.data_cache_dir = os.path.join(
args.data_cache_dir,
"run_Id_%s" % args.run_id,
"edgeNums_%s" % (args.client_num_in_total),
args.dataset,
"edgeId_%s" % args.client_id,
)
(
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = efficient_load_partition_data_cifar10(
args.dataset,
args.data_cache_dir,
args.partition_method,
args.partition_alpha,
args.client_num_in_total,
args.batch_size,
args.process_id,
args.synthetic_data_url,
args.private_local_data
)
if centralized:
train_data_local_num_dict = {
0: sum(user_train_data_num for user_train_data_num in train_data_local_num_dict.values())
}
train_data_local_dict = {
0: [batch for cid in sorted(train_data_local_dict.keys()) for batch in
train_data_local_dict[cid]]
}
test_data_local_dict = {
0: [batch for cid in sorted(test_data_local_dict.keys()) for batch in test_data_local_dict[cid]]
}
args.client_num_in_total = 1
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {
cid: combine_batches(train_data_local_dict[cid]) for cid in train_data_local_dict.keys()
}
test_data_local_dict = {
cid: combine_batches(test_data_local_dict[cid]) for cid in test_data_local_dict.keys()
}
args.batch_size = args_batch_size
dataset = [
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
]
return dataset, class_num
else:
# data_loader = load_partition_data_cifar10
data_loader = efficient_load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
(
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
) = data_loader(
args.dataset,
args.data_cache_dir,
args.partition_method,
args.partition_alpha,
args.client_num_in_total,
args.batch_size,
)
if centralized:
train_data_local_num_dict = {
0: sum(user_train_data_num for user_train_data_num in train_data_local_num_dict.values())
}
train_data_local_dict = {
0: [batch for cid in sorted(train_data_local_dict.keys()) for batch in train_data_local_dict[cid]]
}
test_data_local_dict = {
0: [batch for cid in sorted(test_data_local_dict.keys()) for batch in test_data_local_dict[cid]]
}
args.client_num_in_total = 1
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {
cid: combine_batches(train_data_local_dict[cid]) for cid in train_data_local_dict.keys()
}
test_data_local_dict = {cid: combine_batches(test_data_local_dict[cid]) for cid in test_data_local_dict.keys()}
args.batch_size = args_batch_size
dataset = [
train_data_num,
test_data_num,
train_data_global,
test_data_global,
train_data_local_num_dict,
train_data_local_dict,
test_data_local_dict,
class_num,
]
return dataset, class_num
def load_poisoned_dataset_from_edge_case_examples(args):
return load_poisoned_dataset(args=args)