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log_clean_train.log
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log_clean_train.log
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(base) PS Q:\projects\Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning> C:/Users/xuan/anaconda3/Scripts/activate
(base) PS Q:\projects\Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning> conda activate ml
(ml) PS Q:\projects\Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning> conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
Channels:
- pytorch
- nvidia
- defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: C:\Users\xuan\anaconda3\envs\ml
added / updated specs:
- pytorch
- pytorch-cuda=12.1
- torchaudio
- torchvision
The following packages will be downloaded:
package | build
---------------------------|-----------------
brotli-python-1.0.9 | py38hd77b12b_8 347 KB
certifi-2024.2.2 | py38haa95532_0 160 KB
charset-normalizer-2.0.4 | pyhd3eb1b0_0 35 KB
filelock-3.13.1 | py38haa95532_0 21 KB
gmpy2-2.1.2 | py38h7f96b67_0 160 KB
idna-3.7 | py38haa95532_0 115 KB
intel-openmp-2021.4.0 | haa95532_3556 2.2 MB
jinja2-3.1.3 | py38haa95532_0 271 KB
lz4-c-1.9.4 | h2bbff1b_1 152 KB
markupsafe-2.1.3 | py38h2bbff1b_0 25 KB
mkl-2021.4.0 | haa95532_640 114.9 MB
mkl-service-2.4.0 | py38h2bbff1b_0 51 KB
mkl_fft-1.3.1 | py38h277e83a_0 139 KB
mkl_random-1.2.2 | py38hf11a4ad_0 225 KB
mpc-1.1.0 | h7edee0f_1 260 KB
mpfr-4.0.2 | h62dcd97_1 1.5 MB
mpir-3.0.0 | hec2e145_1 1.3 MB
mpmath-1.3.0 | py38haa95532_0 832 KB
networkx-3.1 | py38haa95532_0 2.7 MB
numpy-1.24.3 | py38hf95b240_0 11 KB
numpy-base-1.24.3 | py38h005ec55_0 6.1 MB
pillow-10.3.0 | py38h2bbff1b_0 837 KB
pysocks-1.7.1 | py38haa95532_0 31 KB
pytorch-2.3.0 |py3.8_cuda12.1_cudnn8_0 1.20 GB pytorch
pyyaml-6.0.1 | py38h2bbff1b_0 160 KB
requests-2.31.0 | py38haa95532_1 98 KB
six-1.16.0 | pyhd3eb1b0_1 18 KB
sympy-1.12 | py38haa95532_0 10.5 MB
torchaudio-2.3.0 | py38_cu121 7.0 MB pytorch
torchvision-0.18.0 | py38_cu121 7.6 MB pytorch
typing_extensions-4.11.0 | py38haa95532_0 61 KB
urllib3-2.1.0 | py38haa95532_1 154 KB
win_inet_pton-1.1.0 | py38haa95532_0 35 KB
xz-5.4.6 | h8cc25b3_1 609 KB
zlib-1.2.13 | h8cc25b3_1 131 KB
zstd-1.5.5 | hd43e919_2 720 KB
------------------------------------------------------------
Total: 1.36 GB
The following NEW packages will be INSTALLED:
blas pkgs/main/win-64::blas-1.0-mkl
brotli-python pkgs/main/win-64::brotli-python-1.0.9-py38hd77b12b_8
certifi pkgs/main/win-64::certifi-2024.2.2-py38haa95532_0
charset-normalizer pkgs/main/noarch::charset-normalizer-2.0.4-pyhd3eb1b0_0
cuda-cccl nvidia/win-64::cuda-cccl-12.4.127-0
cuda-cudart nvidia/win-64::cuda-cudart-12.1.105-0
cuda-cudart-dev nvidia/win-64::cuda-cudart-dev-12.1.105-0
cuda-cupti nvidia/win-64::cuda-cupti-12.1.105-0
cuda-libraries nvidia/win-64::cuda-libraries-12.1.0-0
cuda-libraries-dev nvidia/win-64::cuda-libraries-dev-12.1.0-0
cuda-nvrtc nvidia/win-64::cuda-nvrtc-12.1.105-0
cuda-nvrtc-dev nvidia/win-64::cuda-nvrtc-dev-12.1.105-0
cuda-nvtx nvidia/win-64::cuda-nvtx-12.1.105-0
cuda-opencl nvidia/win-64::cuda-opencl-12.4.127-0
cuda-opencl-dev nvidia/win-64::cuda-opencl-dev-12.4.127-0
cuda-profiler-api nvidia/win-64::cuda-profiler-api-12.4.127-0
cuda-runtime nvidia/win-64::cuda-runtime-12.1.0-0
filelock pkgs/main/win-64::filelock-3.13.1-py38haa95532_0
freetype pkgs/main/win-64::freetype-2.12.1-ha860e81_0
gmpy2 pkgs/main/win-64::gmpy2-2.1.2-py38h7f96b67_0
idna pkgs/main/win-64::idna-3.7-py38haa95532_0
intel-openmp pkgs/main/win-64::intel-openmp-2021.4.0-haa95532_3556
jinja2 pkgs/main/win-64::jinja2-3.1.3-py38haa95532_0
jpeg pkgs/main/win-64::jpeg-9e-h2bbff1b_1
lcms2 pkgs/main/win-64::lcms2-2.12-h83e58a3_0
lerc pkgs/main/win-64::lerc-3.0-hd77b12b_0
libcublas nvidia/win-64::libcublas-12.1.0.26-0
libcublas-dev nvidia/win-64::libcublas-dev-12.1.0.26-0
libcufft nvidia/win-64::libcufft-11.0.2.4-0
libcufft-dev nvidia/win-64::libcufft-dev-11.0.2.4-0
libcurand nvidia/win-64::libcurand-10.3.5.147-0
libcurand-dev nvidia/win-64::libcurand-dev-10.3.5.147-0
libcusolver nvidia/win-64::libcusolver-11.4.4.55-0
libcusolver-dev nvidia/win-64::libcusolver-dev-11.4.4.55-0
libcusparse nvidia/win-64::libcusparse-12.0.2.55-0
libcusparse-dev nvidia/win-64::libcusparse-dev-12.0.2.55-0
libdeflate pkgs/main/win-64::libdeflate-1.17-h2bbff1b_1
libjpeg-turbo pkgs/main/win-64::libjpeg-turbo-2.0.0-h196d8e1_0
libnpp nvidia/win-64::libnpp-12.0.2.50-0
libnpp-dev nvidia/win-64::libnpp-dev-12.0.2.50-0
libnvjitlink nvidia/win-64::libnvjitlink-12.1.105-0
libnvjitlink-dev nvidia/win-64::libnvjitlink-dev-12.1.105-0
libnvjpeg nvidia/win-64::libnvjpeg-12.1.1.14-0
libnvjpeg-dev nvidia/win-64::libnvjpeg-dev-12.1.1.14-0
libpng pkgs/main/win-64::libpng-1.6.39-h8cc25b3_0
libtiff pkgs/main/win-64::libtiff-4.5.1-hd77b12b_0
libuv pkgs/main/win-64::libuv-1.44.2-h2bbff1b_0
libwebp-base pkgs/main/win-64::libwebp-base-1.3.2-h2bbff1b_0
lz4-c pkgs/main/win-64::lz4-c-1.9.4-h2bbff1b_1
markupsafe pkgs/main/win-64::markupsafe-2.1.3-py38h2bbff1b_0
mkl pkgs/main/win-64::mkl-2021.4.0-haa95532_640
mkl-service pkgs/main/win-64::mkl-service-2.4.0-py38h2bbff1b_0
mkl_fft pkgs/main/win-64::mkl_fft-1.3.1-py38h277e83a_0
mkl_random pkgs/main/win-64::mkl_random-1.2.2-py38hf11a4ad_0
mpc pkgs/main/win-64::mpc-1.1.0-h7edee0f_1
mpfr pkgs/main/win-64::mpfr-4.0.2-h62dcd97_1
mpir pkgs/main/win-64::mpir-3.0.0-hec2e145_1
mpmath pkgs/main/win-64::mpmath-1.3.0-py38haa95532_0
networkx pkgs/main/win-64::networkx-3.1-py38haa95532_0
numpy pkgs/main/win-64::numpy-1.24.3-py38hf95b240_0
numpy-base pkgs/main/win-64::numpy-base-1.24.3-py38h005ec55_0
openjpeg pkgs/main/win-64::openjpeg-2.4.0-h4fc8c34_0
pillow pkgs/main/win-64::pillow-10.3.0-py38h2bbff1b_0
pysocks pkgs/main/win-64::pysocks-1.7.1-py38haa95532_0
pytorch pytorch/win-64::pytorch-2.3.0-py3.8_cuda12.1_cudnn8_0
pytorch-cuda pytorch/win-64::pytorch-cuda-12.1-hde6ce7c_5
pytorch-mutex pytorch/noarch::pytorch-mutex-1.0-cuda
pyyaml pkgs/main/win-64::pyyaml-6.0.1-py38h2bbff1b_0
requests pkgs/main/win-64::requests-2.31.0-py38haa95532_1
six pkgs/main/noarch::six-1.16.0-pyhd3eb1b0_1
sympy pkgs/main/win-64::sympy-1.12-py38haa95532_0
torchaudio pytorch/win-64::torchaudio-2.3.0-py38_cu121
torchvision pytorch/win-64::torchvision-0.18.0-py38_cu121
typing_extensions pkgs/main/win-64::typing_extensions-4.11.0-py38haa95532_0
urllib3 pkgs/main/win-64::urllib3-2.1.0-py38haa95532_1
win_inet_pton pkgs/main/win-64::win_inet_pton-1.1.0-py38haa95532_0
xz pkgs/main/win-64::xz-5.4.6-h8cc25b3_1
yaml pkgs/main/win-64::yaml-0.2.5-he774522_0
zlib pkgs/main/win-64::zlib-1.2.13-h8cc25b3_1
zstd pkgs/main/win-64::zstd-1.5.5-hd43e919_2
Proceed ([y]/n)? n
CondaSystemExit: Exiting.
(ml) PS Q:\projects\Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning> python clean_train.py --clean-epoch 80 --dup 0 --multies 4 --unit 0.25
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./raw_data/cifar-10-python.tar.gz
1.3%
Traceback (most recent call last):
File "clean_train.py", line 85, in <module>
trainset = torchvision.datasets.CIFAR10(root='./raw_data/', train=True, download=True,
File "C:\Users\xuan\anaconda3\envs\ml\lib\site-packages\torchvision\datasets\cifar.py", line 66, in __init__
self.download()
File "C:\Users\xuan\anaconda3\envs\ml\lib\site-packages\torchvision\datasets\cifar.py", line 140, in download
download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
File "C:\Users\xuan\anaconda3\envs\ml\lib\site-packages\torchvision\datasets\utils.py", line 395, in download_and_extract_archive
download_url(url, download_root, filename, md5)
File "C:\Users\xuan\anaconda3\envs\ml\lib\site-packages\torchvision\datasets\utils.py", line 132, in download_url
_urlretrieve(url, fpath)
File "C:\Users\xuan\anaconda3\envs\ml\lib\site-packages\torchvision\datasets\utils.py", line 30, in _urlretrieve
while chunk := response.read(chunk_size):
File "C:\Users\xuan\anaconda3\envs\ml\lib\http\client.py", line 459, in read
n = self.readinto(b)
File "C:\Users\xuan\anaconda3\envs\ml\lib\http\client.py", line 503, in readinto
n = self.fp.readinto(b)
File "C:\Users\xuan\anaconda3\envs\ml\lib\socket.py", line 669, in readinto
return self._sock.recv_into(b)
File "C:\Users\xuan\anaconda3\envs\ml\lib\ssl.py", line 1274, in recv_into
return self.read(nbytes, buffer)
File "C:\Users\xuan\anaconda3\envs\ml\lib\ssl.py", line 1132, in read
return self._sslobj.read(len, buffer)
KeyboardInterrupt
(ml) PS Q:\projects\Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning> python clean_train.py --clean-epoch 80 --dup 0 --multies 4 --unit 0.25
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./raw_data/cifar-10-python.tar.gz
100.0%
Extracting ./raw_data/cifar-10-python.tar.gz to ./raw_data/
Files already downloaded and verified
Epoch 0, loss = 1.9170, acc = 0.2850
Epoch 1, loss = 1.6124, acc = 0.4071
Epoch 2, loss = 1.4924, acc = 0.4554
Epoch 3, loss = 1.3992, acc = 0.4909
Epoch 4, loss = 1.3065, acc = 0.5283
Epoch 5, loss = 1.2529, acc = 0.5490
Epoch 6, loss = 1.1956, acc = 0.5681
Epoch 7, loss = 1.1486, acc = 0.5881
Epoch 8, loss = 1.1110, acc = 0.6042
Epoch 9, loss = 1.0708, acc = 0.6173
Epoch 10, loss = 1.0370, acc = 0.6305
Epoch 11, loss = 1.0157, acc = 0.6397
Epoch 12, loss = 0.9745, acc = 0.6552
Epoch 13, loss = 0.9424, acc = 0.6665
Epoch 14, loss = 0.9283, acc = 0.6716
Epoch 15, loss = 0.9018, acc = 0.6813
Epoch 16, loss = 0.8861, acc = 0.6879
Epoch 17, loss = 0.8715, acc = 0.6930
Epoch 18, loss = 0.8501, acc = 0.7005
Epoch 19, loss = 0.8268, acc = 0.7096
Epoch 20, loss = 0.8114, acc = 0.7151
Epoch 21, loss = 0.8049, acc = 0.7183
Epoch 22, loss = 0.7947, acc = 0.7197
Epoch 23, loss = 0.7814, acc = 0.7292
Epoch 24, loss = 0.7594, acc = 0.7354
Epoch 25, loss = 0.7618, acc = 0.7342
Epoch 26, loss = 0.7423, acc = 0.7387
Epoch 27, loss = 0.7344, acc = 0.7410
Epoch 28, loss = 0.7226, acc = 0.7471
Epoch 29, loss = 0.7139, acc = 0.7491
Epoch 30, loss = 0.7041, acc = 0.7529
Epoch 31, loss = 0.6964, acc = 0.7571
Epoch 32, loss = 0.6875, acc = 0.7598
Epoch 33, loss = 0.6869, acc = 0.7615
Epoch 34, loss = 0.6802, acc = 0.7631
Epoch 35, loss = 0.6708, acc = 0.7675
Epoch 36, loss = 0.6621, acc = 0.7712
Epoch 37, loss = 0.6547, acc = 0.7732
Epoch 38, loss = 0.6567, acc = 0.7707
Epoch 39, loss = 0.6396, acc = 0.7803
Epoch 40, loss = 0.6402, acc = 0.7776
Epoch 41, loss = 0.6372, acc = 0.7786
Epoch 42, loss = 0.6284, acc = 0.7830
Epoch 43, loss = 0.6253, acc = 0.7831
Epoch 44, loss = 0.6205, acc = 0.7839
Epoch 45, loss = 0.6071, acc = 0.7898
Epoch 46, loss = 0.6074, acc = 0.7907
Epoch 47, loss = 0.6046, acc = 0.7907
Epoch 48, loss = 0.6000, acc = 0.7924
Epoch 49, loss = 0.6029, acc = 0.7903
Epoch 50, loss = 0.5958, acc = 0.7922
Epoch 51, loss = 0.5864, acc = 0.7960
Epoch 52, loss = 0.5856, acc = 0.7958
Epoch 53, loss = 0.5783, acc = 0.7988
Epoch 54, loss = 0.5750, acc = 0.8000
Epoch 55, loss = 0.5729, acc = 0.8021
Epoch 56, loss = 0.5698, acc = 0.8019
Epoch 57, loss = 0.5701, acc = 0.8028
Epoch 58, loss = 0.5627, acc = 0.8024
Epoch 59, loss = 0.5604, acc = 0.8045
Epoch 60, loss = 0.5612, acc = 0.8046
Epoch 61, loss = 0.5538, acc = 0.8060
Epoch 62, loss = 0.5542, acc = 0.8060
Epoch 63, loss = 0.5468, acc = 0.8086
Epoch 64, loss = 0.5482, acc = 0.8105
Epoch 65, loss = 0.5386, acc = 0.8109
Epoch 66, loss = 0.5397, acc = 0.8117
Epoch 67, loss = 0.5359, acc = 0.8144
Epoch 68, loss = 0.5367, acc = 0.8124
Epoch 69, loss = 0.5337, acc = 0.8142
Epoch 70, loss = 0.5270, acc = 0.8168
Epoch 71, loss = 0.5267, acc = 0.8171
Epoch 72, loss = 0.5267, acc = 0.8177
Epoch 73, loss = 0.5250, acc = 0.8164
Epoch 74, loss = 0.5176, acc = 0.8213
Epoch 75, loss = 0.5117, acc = 0.8199
Epoch 76, loss = 0.5155, acc = 0.8199
Epoch 77, loss = 0.5162, acc = 0.8198
Epoch 78, loss = 0.5042, acc = 0.8226
Epoch 79, loss = 0.5122, acc = 0.8222
Epoch 0, loss = 0.5162, acc = 0.8202
Epoch 1, loss = 0.5128, acc = 0.8237
Epoch 2, loss = 0.5062, acc = 0.8219
Epoch 3, loss = 0.4975, acc = 0.8282
Epoch 4, loss = 0.4962, acc = 0.8275
Epoch 5, loss = 0.4979, acc = 0.8279
Epoch 6, loss = 0.4903, acc = 0.8278
Epoch 7, loss = 0.4996, acc = 0.8251
Epoch 8, loss = 0.4887, acc = 0.8296
Epoch 9, loss = 0.4902, acc = 0.8315
Epoch 10, loss = 0.4829, acc = 0.8310
Epoch 11, loss = 0.4873, acc = 0.8317
Epoch 12, loss = 0.4793, acc = 0.8316
Epoch 13, loss = 0.4823, acc = 0.8318
Epoch 14, loss = 0.4836, acc = 0.8317
Epoch 15, loss = 0.4837, acc = 0.8320
Epoch 16, loss = 0.4744, acc = 0.8341
Epoch 17, loss = 0.4731, acc = 0.8349
Epoch 18, loss = 0.4720, acc = 0.8327
Epoch 19, loss = 0.4769, acc = 0.8339
clean acc: 0.8353
Training a model costs 1328.1520s.