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unnecessary logging is blocked
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@warisgill
warisgill committed Jan 28, 2023
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273 changes: 11 additions & 262 deletions fault-localization/artifact.ipynb
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Expand Up @@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -34,30 +34,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gulzar/anaconda3/envs/torch/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Global seed set to 786\n",
"Global seed set to 786\n"
]
},
{
"data": {
"text/plain": [
"786"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import logging\n",
"import time\n",
Expand Down Expand Up @@ -92,7 +71,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -130,7 +109,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -151,212 +130,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
" ***Simulating FL setup iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001 ***\n",
"Files already downloaded and verified\n",
"Files already downloaded and verified\n",
"Spliting Datasets 50000 into parts:[10000, 10000, 10000, 10000, 10000]\n",
"input shape, torch.Size([1, 3, 32, 32])\n",
"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/faulty_client_0_noise_rate_1_classes.ckpt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using 16bit native Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train mod batch = 272, and drop_last = False\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:05<00:00, 3.47it/s, loss=2.35, train_acc=0.130, train_loss=2.360, val_acc=0.0961, val_loss=2.460] "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:05<00:00, 3.47it/s, loss=2.35, train_acc=0.130, train_loss=2.360, val_acc=0.0961, val_loss=2.460]\n",
"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_1.ckpt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using 16bit native Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train mod batch = 272, and drop_last = False\n",
"Epoch 4: 100%|██████████| 20/20 [00:06<00:00, 3.17it/s, loss=0.585, train_acc=0.812, train_loss=0.597, val_acc=0.729, val_loss=0.810]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:06<00:00, 3.17it/s, loss=0.585, train_acc=0.812, train_loss=0.597, val_acc=0.729, val_loss=0.810]\n",
"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_2.ckpt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using 16bit native Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train mod batch = 272, and drop_last = False\n",
"Epoch 4: 100%|██████████| 20/20 [00:06<00:00, 3.21it/s, loss=0.544, train_acc=0.817, train_loss=0.516, val_acc=0.745, val_loss=0.840]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:06<00:00, 3.21it/s, loss=0.544, train_acc=0.817, train_loss=0.516, val_acc=0.745, val_loss=0.840]\n",
"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_3.ckpt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using 16bit native Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train mod batch = 272, and drop_last = False\n",
"Epoch 4: 100%|██████████| 20/20 [00:07<00:00, 2.85it/s, loss=0.54, train_acc=0.817, train_loss=0.539, val_acc=0.743, val_loss=0.842] "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:07<00:00, 2.84it/s, loss=0.54, train_acc=0.817, train_loss=0.539, val_acc=0.743, val_loss=0.842]\n",
"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_4.ckpt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using 16bit native Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train mod batch = 272, and drop_last = False\n",
"Epoch 4: 100%|██████████| 20/20 [00:05<00:00, 3.51it/s, loss=0.558, train_acc=0.803, train_loss=0.622, val_acc=0.744, val_loss=0.849]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4: 100%|██████████| 20/20 [00:05<00:00, 3.50it/s, loss=0.558, train_acc=0.803, train_loss=0.622, val_acc=0.744, val_loss=0.849]\n",
"Total clients: 5\n",
"++Training is done: iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001\n"
]
}
],
"outputs": [],
"source": [
"# FL training\n",
"c2ms, exp2info = trainFLMain(args)\n",
Expand All @@ -366,18 +142,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> Running FaultyClientLocalization ..\n",
"Same prediction threshold 5\n"
]
}
],
"outputs": [],
"source": [
"# Fault localazation to find potetial faulty clients\n",
"potential_faulty_clients, _, _ = runFaultyClientLocalization(\n",
Expand All @@ -386,27 +153,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"+++ Faulty Clients {0}\n",
"Fault Localization Accuracy: 100.0\n"
]
}
],
"outputs": [],
"source": [
"# Fault localization accuracy \n",
"acc = evaluateFaultLocalization(\n",
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8 changes: 5 additions & 3 deletions fault-localization/utils/FLSimulation.py
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Expand Up @@ -10,8 +10,10 @@
from .dl_models import ImageClassifer, initialize_model
from .fl_datasets import *

logging.basicConfig(filename='example.log', level=logging.ERROR)
logger = logging.getLogger("pytorch_lightning")
# logging.basicConfig(filename='example.log', level=logging.ERROR)
# logger = logging.getLogger("pytorch_lightning")
logging.getLogger("pytorch_lightning").setLevel(logging.WARNING)

seed_everything(786)


Expand Down Expand Up @@ -126,7 +128,7 @@ def getFLClientsDatasets():
stringID2intID[k] = normal_id

data_config["single_input_shape"] = valid[0][0].unsqueeze(0).shape
print(f'input shape, {data_config["single_input_shape"]}')
# print(f'input shape, {data_config["single_input_shape"]}')
# return

model_config["classes"] = num_classes
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2 changes: 1 addition & 1 deletion fault-localization/utils/faulty_client_localization/InferenceGuidedInputs.py
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Expand Up @@ -11,7 +11,7 @@ class InferenceGuidedInputs:
def __init__(self, clients2models, shape, randomGenerator, apply_transform, dname=None, k_gen_inputs=10, min_nclients_same_pred=5, time_delta=60):
self.clients2models = clients2models
self.min_nclients_same_pred = 3 #min_nclients_same_pred
print(f"Same prediction threshold {self.min_nclients_same_pred}")
# print(f"Same prediction threshold {self.min_nclients_same_pred}")
self.same_seqs_set = set()
self.k_gen_inputs = k_gen_inputs
self.size = 1024
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4 changes: 2 additions & 2 deletions fault-localization/utils/fl_datasets.py
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Expand Up @@ -273,8 +273,8 @@ def __init__(self, train_dataset, val_dataset, batch_size, num_workers=4) -> Non
print(
f"Dropping last batch because of uneven data size: {len(self.train_dataset)} % {self.batch_size} == 1")

print(
f"Train mod batch = {len(train_dataset) % batch_size}, and drop_last = {self.drop_last}")
# print(
# f"Train mod batch = {len(train_dataset) % batch_size}, and drop_last = {self.drop_last}")

def train_dataloader(self):
return DataLoader(self.train_dataset, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True, shuffle=True, drop_last=self.drop_last)
Expand Down

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