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unnecessary logging is blocked
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‎fault-localization/artifact.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"Global seed set to 786\n",
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"Global seed set to 786\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"786"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"import logging\n",
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"import time\n",
@@ -92,7 +71,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -151,212 +130,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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" ***Simulating FL setup iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001 ***\n",
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"Files already downloaded and verified\n",
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"Files already downloaded and verified\n",
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"Spliting Datasets 50000 into parts:[10000, 10000, 10000, 10000, 10000]\n",
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"input shape, torch.Size([1, 3, 32, 32])\n",
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"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"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using 16bit native Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train mod batch = 272, and drop_last = False\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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] "
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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",
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"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_1.ckpt\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using 16bit native Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train mod batch = 272, and drop_last = False\n",
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"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]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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",
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"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_2.ckpt\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using 16bit native Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train mod batch = 272, and drop_last = False\n",
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"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]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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",
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"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_3.ckpt\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using 16bit native Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train mod batch = 272, and drop_last = False\n",
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"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] "
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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"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",
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"Training : .storage/checkpoints/iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001/client_4.ckpt\n"
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]
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},
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"text": [
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"Using 16bit native Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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{
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"text": [
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"Train mod batch = 272, and drop_last = False\n",
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"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]"
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]
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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]
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},
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"text": [
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"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",
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"Total clients: 5\n",
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"++Training is done: iid_resnet50_cifar10_clients_5_faulty_[0]_bsize_512_epochs_5_lr_0.001\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# FL training\n",
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"c2ms, exp2info = trainFLMain(args)\n",
@@ -366,18 +142,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"> Running FaultyClientLocalization ..\n",
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"Same prediction threshold 5\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Fault localazation to find potetial faulty clients\n",
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"potential_faulty_clients, _, _ = runFaultyClientLocalization(\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"+++ Faulty Clients {0}\n",
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"Fault Localization Accuracy: 100.0\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Fault localization accuracy \n",
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"acc = evaluateFaultLocalization(\n",

‎fault-localization/utils/FLSimulation.py

Lines changed: 5 additions & 3 deletions
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from .dl_models import ImageClassifer, initialize_model
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from .fl_datasets import *
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logging.basicConfig(filename='example.log', level=logging.ERROR)
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logger = logging.getLogger("pytorch_lightning")
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# logging.basicConfig(filename='example.log', level=logging.ERROR)
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# logger = logging.getLogger("pytorch_lightning")
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logging.getLogger("pytorch_lightning").setLevel(logging.WARNING)
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seed_everything(786)
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@@ -126,7 +128,7 @@ def getFLClientsDatasets():
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stringID2intID[k] = normal_id
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data_config["single_input_shape"] = valid[0][0].unsqueeze(0).shape
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print(f'input shape, {data_config["single_input_shape"]}')
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# print(f'input shape, {data_config["single_input_shape"]}')
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# return
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model_config["classes"] = num_classes

‎fault-localization/utils/faulty_client_localization/InferenceGuidedInputs.py

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Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ class InferenceGuidedInputs:
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def __init__(self, clients2models, shape, randomGenerator, apply_transform, dname=None, k_gen_inputs=10, min_nclients_same_pred=5, time_delta=60):
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self.clients2models = clients2models
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self.min_nclients_same_pred = 3 #min_nclients_same_pred
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print(f"Same prediction threshold {self.min_nclients_same_pred}")
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# print(f"Same prediction threshold {self.min_nclients_same_pred}")
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self.same_seqs_set = set()
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self.k_gen_inputs = k_gen_inputs
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self.size = 1024

‎fault-localization/utils/fl_datasets.py

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@@ -273,8 +273,8 @@ def __init__(self, train_dataset, val_dataset, batch_size, num_workers=4) -> Non
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print(
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f"Dropping last batch because of uneven data size: {len(self.train_dataset)} % {self.batch_size} == 1")
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print(
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f"Train mod batch = {len(train_dataset) % batch_size}, and drop_last = {self.drop_last}")
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# print(
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# f"Train mod batch = {len(train_dataset) % batch_size}, and drop_last = {self.drop_last}")
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def train_dataloader(self):
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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)

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