Python 张量流卡在第一纪元上
我的执行卡在下面的步骤上 我在Windows10上的Python3.7上获得了GTX3090。按照这里的指示，我使用conda安装了cuda、cudnn和tensorflow gpuPython 张量流卡在第一纪元上,python,tensorflow,anaconda,gpu,conda,Python,Tensorflow,Anaconda,Gpu,Conda,我的执行卡在下面的步骤上 我在Windows10上的Python3.7上获得了GTX3090。按照这里的指示，我使用conda安装了cuda、cudnn和tensorflow gpu conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py37h936c3e2_0 有人能告诉我为什么卡在这一步吗 2021-04-01 10:09:38.147949: I tensorflow/stream_executor/platform/default/
conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py37h936c3e2_0
2021-04-01 10:09:38.147949: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
Warning, `split` argument is replaced by `split_val`, please condider to change your source code.The `split` argument will be removed in future releases.
class 0, validation count: 68, train count: 159
class 5, validation count: 83, train count: 195
Total data: 2 classes for 354 files for train
Total data: 2 classes for 151 files for validation
2021-04-01 10:09:40.046207: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2021-04-01 10:09:40.763173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:44:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 82 deviceMemorySize: 24.00GiB deviceMemoryBandwidth: 871.81GiB/s
2021-04-01 10:09:40.763755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA Quadro P1000 computeCapability: 6.1
coreClock: 1.5185GHz coreCount: 4 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 89.53GiB/s
2021-04-01 10:09:40.764222: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2021-04-01 10:09:40.768538: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2021-04-01 10:09:40.772484: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2021-04-01 10:09:40.774033: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2021-04-01 10:09:40.778690: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2021-04-01 10:09:40.781972: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2021-04-01 10:09:40.793304: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2021-04-01 10:09:40.793720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1843] Ignoring visible gpu device (device: 1, name: NVIDIA Quadro P1000, pci bus id: 0000:01:00.0, compute capability: 6.1) with core count: 4. The minimum required count is 8. You can adjust this requirement with the env var TF_MIN_GPU_MULTIPROCESSOR_COUNT.
2021-04-01 10:09:40.794339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-04-01 10:09:40.795239: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-01 10:09:40.808243: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2af71215370 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-04-01 10:09:40.808648: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-04-01 10:09:40.809155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:44:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 82 deviceMemorySize: 24.00GiB deviceMemoryBandwidth: 871.81GiB/s
2021-04-01 10:09:40.809789: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2021-04-01 10:09:40.810112: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2021-04-01 10:09:40.810416: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2021-04-01 10:09:40.810728: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2021-04-01 10:09:40.811035: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2021-04-01 10:09:40.811338: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2021-04-01 10:09:40.811624: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2021-04-01 10:09:40.811974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-04-01 10:09:42.137546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-01 10:09:42.137800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2021-04-01 10:09:42.137935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2021-04-01 10:09:42.138228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 19112 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:44:00.0, compute capability: 8.6)
2021-04-01 10:09:42.141792: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2af27c8f640 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-04-01 10:09:42.142075: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
Epoch 1/1000
2021-04-01 10:09:46.249137: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2021-04-01 10:09:47.132659: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
# Name Version Build Channel
_tflow_select 2.3.0 gpu
absl-py 0.12.0 pyhd8ed1ab_0 conda-forge
aiohttp 3.7.4 py37hcc03f2d_0 conda-forge
astor 0.8.1 pyh9f0ad1d_0 conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-timeout 3.0.1 py_1000 conda-forge
attrs 20.3.0 pyhd3deb0d_0 conda-forge
blinker 1.4 py_1 conda-forge
brotlipy 0.7.0 py37hcc03f2d_1001 conda-forge
ca-certificates 2020.12.5 h5b45459_0 conda-forge
cachetools 4.2.1 pyhd8ed1ab_0 conda-forge
certifi 2020.12.5 py37h03978a9_1 conda-forge
cffi 1.14.5 py37hd8e9650_0 conda-forge
chardet 4.0.0 py37h03978a9_1 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
cryptography 3.4.7 py37h20c650d_0 conda-forge
cudatoolkit 10.1.243 h3826478_8 conda-forge
cudnn 7.6.5.32 h36d860d_1 conda-forge
cycler 0.10.0 pypi_0 pypi
gast 0.3.3 py_0 conda-forge
google-auth 1.28.0 pyh44b312d_0 conda-forge
google-auth-oauthlib 0.4.1 py_2 conda-forge
google-pasta 0.2.0 pyh8c360ce_0 conda-forge
grpcio 1.36.1 py37h04d2302_0 conda-forge
h5py 2.10.0 nompi_py37h23cfb99_105 conda-forge
hdf5 1.10.6 nompi_h5268f04_1114 conda-forge
idna 2.10 pyh9f0ad1d_0 conda-forge
importlib-metadata 3.10.0 py37h03978a9_0 conda-forge
intel-openmp 2020.3 h57928b3_311 conda-forge
keras 2.4.3 py_0 conda-forge
keras-applications 1.0.8 py_1 conda-forge
keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge
keras-video-generators 1.0.14 pypi_0 pypi
kiwisolver 1.3.1 pypi_0 pypi
krb5 1.17.2 hbae68bd_0 conda-forge
libblas 3.9.0 8_mkl conda-forge
libcblas 3.9.0 8_mkl conda-forge
libcurl 7.76.0 hf1763fc_0 conda-forge
liblapack 3.9.0 8_mkl conda-forge
libprotobuf 3.15.6 h7755175_0 conda-forge
libssh2 1.9.0 h680486a_6 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
markdown 3.3.4 pyhd8ed1ab_0 conda-forge
matplotlib 3.4.1 pypi_0 pypi
mkl 2020.4 hb70f87d_311 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
multidict 5.1.0 py37hcc03f2d_1 conda-forge
numpy 1.19.5 py37hd20adf4_1 conda-forge
oauthlib 3.0.1 py_0 conda-forge
opencv-python 4.5.1.48 pypi_0 pypi
openssl 1.1.1k h8ffe710_0 conda-forge
opt_einsum 3.3.0 py_0 conda-forge
pillow 8.1.2 pypi_0 pypi
pip 21.0.1 pyhd8ed1ab_0 conda-forge
protobuf 3.15.6 py37hf2a7229_0 conda-forge
pyasn1 0.4.8 py_0 conda-forge
pyasn1-modules 0.2.7 py_0 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pyjwt 2.0.1 pyhd8ed1ab_1 conda-forge
pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge
pyparsing 2.4.7 pypi_0 pypi
pyreadline 2.1 py37h03978a9_1003 conda-forge
pysocks 1.7.1 py37h03978a9_3 conda-forge
python 3.7.10 h7840368_100_cpython conda-forge
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.7 1_cp37m conda-forge
pyyaml 5.4.1 pypi_0 pypi
requests 2.25.1 pyhd3deb0d_0 conda-forge
requests-oauthlib 1.3.0 pyh9f0ad1d_0 conda-forge
rsa 4.7.2 pyh44b312d_0 conda-forge
scipy 1.6.2 py37h924764e_0 conda-forge
setuptools 49.6.0 py37h03978a9_3 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
sqlite 3.35.3 h8ffe710_0 conda-forge
tensorboard 2.4.1 pyhd8ed1ab_0 conda-forge
tensorboard-plugin-wit 1.8.0 pyh44b312d_0 conda-forge
tensorflow 2.3.0 mkl_py37h936c3e2_0
tensorflow-base 2.3.0 gpu_py37h18d21e4_0
tensorflow-estimator 2.4.0 pyh9656e83_0 conda-forge
tensorflow-gpu 2.3.0 he13fc11_0
termcolor 1.1.0 py_2 conda-forge
tk 8.6.10 h8ffe710_1 conda-forge
typing-extensions 3.7.4.3 0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
urllib3 1.26.4 pyhd8ed1ab_0 conda-forge
vc 14.2 hb210afc_4 conda-forge
vs2015_runtime 14.28.29325 h5e1d092_4 conda-forge
werkzeug 1.0.1 pyh9f0ad1d_0 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
win_inet_pton 1.1.0 py37h03978a9_2 conda-forge
wincertstore 0.2 py37h03978a9_1006 conda-forge
wrapt 1.12.1 py37hcc03f2d_3 conda-forge
yaml 0.2.5 he774522_0 conda-forge
yarl 1.6.3 py37hcc03f2d_1 conda-forge
zipp 3.4.1 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h62dcd97_1010 conda-forge
Total params: 5,502,338
Trainable params: 5,500,418
Non-trainable params: 1,920
我还向我的环境变量中添加了
CUDA\u CACHE\u MAXSIZE=2147483648RTX 2080 TiTuringCUDA版本从10.x开始，其中asRTX 3090
卡基于Ampere
架构，兼容的CUDA版本从11.x开始
因此，gpu卡的兼容Tensorflow版本是2.4.0
。有关更多详细信息，请参考。此步骤需要多长时间取决于您的型号。能否请您将model.summary（）
的输出添加到您的问题中。@Christian更新的问题我使用了keras
而不是tensorflow.keras
，结果成功了。因此，必须与TF和Keras版本冲突。是的，我也经历过同样的行为。我在tf环境中卸载了普通keras，并使用tensorflow导入keras中的