我从https://github.com/NVlabs/stylegan下载了stylegan代码,并希望通过我的数据集对其进行训练。我正在使用ubuntu机器(Ubuntu 18.04.3 LTS),
python train.py
给出错误,说:
2020-01-26 23:30:27.115726: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
2020-01-26 23:30:27.115811: E tensorflow/stream_executor/cuda/cuda_dnn.cc:337] Possibly insufficient driver version: 430.50.0
这是我的cuda,cudnn和pip列表的输出:
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130
$nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50 Driver Version: 430.50 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2060 Off | 00000000:01:00.0 On | N/A |
| 42% 37C P8 14W / 170W | 529MiB / 5931MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1100 G /usr/lib/xorg/Xorg 245MiB |
| 0 1578 G /usr/bin/gnome-shell 149MiB |
| 0 2179 G ...quest-channel-token=1359353350696709871 132MiB |
+-----------------------------------------------------------------------------+
$dpkg -l | grep -i cudnn
ii libcudnn7 7.6.5.32-1+cuda10.2 amd64 cuDNN runtime libraries
ii libcudnn7-dev 7.6.5.32-1+cuda10.2 amd64 cuDNN development libraries and headers
$pip list
absl-py (0.9.0)
astor (0.8.1)
bleach (1.5.0)
certifi (2019.11.28)
chardet (3.0.4)
gast (0.3.3)
google-pasta (0.1.8)
grpcio (1.26.0)
h5py (2.10.0)
html5lib (0.9999999)
idna (2.8)
Keras-Applications (1.0.8)
Keras-Preprocessing (1.1.0)
Markdown (3.1.1)
mock (3.0.5)
numpy (1.18.1)
opencv-python (4.1.0.25)
Pillow (6.1.0)
pip (9.0.1)
pkg-resources (0.0.0)
protobuf (3.11.2)
requests (2.22.0)
scipy (1.2.0)
setuptools (45.1.0)
six (1.14.0)
tensorboard (1.14.0)
tensorflow-estimator (1.14.0)
tensorflow-gpu (1.14.0)
termcolor (1.1.0)
tqdm (4.32.2)
urllib3 (1.25.7)
Werkzeug (0.16.0)
wheel (0.33.6)
wrapt (1.11.2)
absl-py (0.9.0)
astor (0.8.1)
bleach (1.5.0)
certifi (2019.11.28)
chardet (3.0.4)
gast (0.3.3)
google-pasta (0.1.8)
grpcio (1.26.0)
h5py (2.10.0)
html5lib (0.9999999)
idna (2.8)
Keras-Applications (1.0.8)
Keras-Preprocessing (1.1.0)
Markdown (3.1.1)
mock (3.0.5)
numpy (1.18.1)
opencv-python (4.1.0.25)
Pillow (6.1.0)
pip (9.0.1)
pkg-resources (0.0.0)
protobuf (3.11.2)
requests (2.22.0)
scipy (1.2.0)
setuptools (45.1.0)
six (1.14.0)
tensorboard (1.14.0)
tensorflow-estimator (1.14.0)
tensorflow-gpu (1.14.0)
termcolor (1.1.0)
tqdm (4.32.2)
urllib3 (1.25.7)
Werkzeug (0.16.0)
wheel (0.33.6)
wrapt (1.11.2)
有人知道这些工具的特定版本可以用来运行stylegan吗?
即使在注释部分中也提供了解决方案(答案部分),但这样做是为了社区的利益。
首先需要删除所有cuDNN文件
rm -f /usr/include/cudnn.h
rm -f /usr/lib/x86_64-linux-gnu/*libcudnn*
rm -f /usr/local/cuda-/lib64/*libcudnn
现在从here中提取新的cuDNN
为了下载cuDNN,请确保您已注册NVIDIA Developer Program。
请检查Tensorflow GPU here的经过测试的构建配置>
将以下文件复制到CUDA Toolkit目录中,并更改文件权限
sudo cp cuda/include/cudnn.h /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
有关详细信息,请参阅cuDNN安装指南here
[[注
:更新cuDNN后,如果TensorFlow抱怨,则相应地更新Tensorflow