-
Notifications
You must be signed in to change notification settings - Fork 0
/
config.cfg
executable file
·111 lines (96 loc) · 2.17 KB
/
config.cfg
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
# montiored by utils.py config_parse()
# integer are automatically converted to int type
# strinsg dont need quotes and are automatically converted to str type
# key names end with "*path" will be converted to absolute path
[AUTHOR]
username = 'NavinKumarMNK'
emailid = 'mnk.navin@outlook.com'
project = 'MegVerse AI'
[GENERAL]
root_path=/mnt/nfs_share/nfs_share/malproj
[DISTRIBUTED_ENV]
ip_address = all
data_parallel = 0
model_parallel = 1
horovod = 0
num_workers = 5
no_of_cpu_per_worker = all
use_gpu = True
[MALWARE_CLASSIFIER_BB]
in_channels = 2
hidden_rep = 1536
num_classes = 2
weights_path = /weights/MalCoAtNetBB
[MALWARE_CLASSIFIER_RGB]
in_channels = 3
hidden_rep = 1536
num_classes = 2
weights_path = /weights/MalCoAtNetRGB
[MALWARE_CLASSIFIER_TRAIN]
max_epochs = 50
min_epochs = 20
#accelerator = gpu
benchmark = True
weights_summary = full
precision = 16
auto_lr_find = True
auto_scale_batch_size = True
auto_select_gpus = True
check_val_every_n_epoch = 1
fast_dev_run = False
enable_progress_bar = True
accumulate_grad_batches=16
sync_batchnorm=True
limit_train_batches=0.1
#limit_val_batches=0.05
num_sanity_val_steps=0
[MALBINIMG_RGB_DATASET]
batch_size = 16
root_dir = /mnt/nfs_share/nfs_share/malbinimg
num_workers = 8
[MALBINIMG_BB_DATASET]
batch_size = 16
root_dir = /home/windows/winbinimg/
num_workers = 1
[SVM_CLASSIFIER]
weights_path = /weights/svm_weights
kernel = linear
C = 1.0
gamma = scale
class_weight = balanced
[APICALLS_DATASET]
annotation = label.csv
data_dir_path = /data/system_call/
batch_size = 64
num_workers = 8
[SYSTEMCALL_LSTM]
num_classes = 2
input_size = 102
hidden_size = 512
num_layers = 3
max_length = 1000
[APICALLS_TRAIN]
max_epochs = 100
min_epochs = 50
accelerator = gpu
weights_summary = full
precision = 16
auto_lr_find = True
auto_scale_batch_size = True
auto_select_gpus = True
check_val_every_n_epoch = 1
fast_dev_run = False
enable_progress_bar = True
gradient_clip_val = 5
gradient_clip_algorithm = norm
track_grad_norm=2
num_sanity_val_steps=0
[PRODUCTION]
temp_data_path = temp/
production = 1
connection_string = 'mongodb://localhost:27017/'
eventhub_name = 'eventhub'
coatnet = 1
lgbm_classifer = 1
api_analysis = 1
server_analysis = 0