GPU可在PyTorch中运行但TensorFlow无法调用的问题求助

环境配置 #

当前conda环境的关键包版本：

(myen2v) C:\Users\Jan>conda list cudnn
# packages in environment at D:\BitDownlD\Anaconda8\envs\myen2v:
#
# Name                    Version                   Build  Channel
cudnn                     8.9.2.26               cuda11_0    anaconda

(myen2v) C:\Users\Jan>conda list cuda
# packages in environment at D:\BitDownlD\Anaconda8\envs\myen2v:
#
# Name                    Version                   Build  Channel
cudatoolkit               11.8.0               hd77b12b_0

(myen2v) C:\Users\Jan>conda list torch
# packages in environment at D:\BitDownlD\Anaconda8\envs\myen2v:
#
# Name                    Version                   Build  Channel
pytorch                   2.0.1           cpu_py38hb0bdfb8_0
torch                     2.1.0                    pypi_0    pypi

(myen2v) C:\Users\Jan>conda list tensor
# packages in environment at D:\BitDownlD\Anaconda8\envs\myen2v:
#
# Name                    Version                   Build  Channel
tensorboard               2.13.0                   pypi_0    pypi
tensorboard-data-server   0.7.1                    pypi_0    pypi
tensorboard-plugin-wit    1.8.1            py38haa95532_0
tensorflow                2.13.0                   pypi_0    pypi
tensorflow-base           2.3.0           eigen_py38h75a453f_0
tensorflow-estimator      2.13.0                   pypi_0    pypi
tensorflow-gpu            2.3.0                    pypi_0    pypi
tensorflow-gpu-estimator  2.3.0                    pypi_0    pypi
tensorflow-io-gcs-filesystem 0.31.0                   pypi_0    pypi

PyTorch GPU运行正常 #

执行以下代码可成功在GPU上运行：

# Create tensors on GPU
a = torch.tensor([1, 2, 3], device="cuda")
b = torch.tensor([4, 5, 6], device="cuda")

# Perform operations on GPU
c = a + b
print(c)

输出：

tensor([5, 7, 9], device='cuda:0')

TensorFlow运行报错 #

测试代码1及报错 #

import tensorflow as tf

physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs:", len(physical_devices))

报错信息：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[12], line 1
----> 1 physical_devices = tf.config.list_physical_devices('GPU')
      2 print("Num GPUs:", len(physical_devices))

AttributeError: module 'tensorflow' has no attribute 'config'

测试代码2及报错 #

import tensorflow as tf
print("Num of GPUs available: ", len(tf.test.gpu_device_name()))

报错信息：

--------------------------------------------------------------------------- AttributeError                            Traceback (most recent call
> last) Cell In[13], line 2
>       1 import tensorflow as tf
> ----> 2 print("Num of GPUs available: ", len(tf.test.gpu_device_name()))
> 
> AttributeError: module 'tensorflow' has no attribute 'test'

问题原因及解决步骤 #

问题原因 #

环境中同时安装了多个版本的TensorFlow包（2.13.0和2.3.0），且混装了tensorflow、tensorflow-base、tensorflow-gpu，导致包冲突，TensorFlow模块加载异常。

解决步骤 #

1. 卸载所有TensorFlow相关包
   在conda环境中执行以下命令：

   pip uninstall tensorflow tensorflow-base tensorflow-gpu tensorflow-estimator tensorflow-gpu-estimator tensorflow-io-gcs-filesystem
   

   若部分包由conda安装，可改用conda卸载：

   conda remove tensorflow tensorflow-base tensorflow-gpu tensorflow-estimator tensorflow-gpu-estimator tensorflow-io-gcs-filesystem
   

2. 安装与CUDA版本兼容的TensorFlow
   当前CUDA版本为11.8，TensorFlow 2.13.0官方支持CUDA 11.8，直接安装主包即可（TensorFlow 2.x之后无需单独安装tensorflow-gpu，主包已包含GPU支持）：

   pip install tensorflow==2.13.0
   

3. 验证安装
   执行以下代码验证GPU是否可用：

   import tensorflow as tf
   print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
   # 进一步验证GPU计算
   tf.debugging.set_log_device_placement(True)
   a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
   b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
   c = tf.matmul(a, b)
   print(c)
   

   若输出中包含GPU设备信息，则说明配置成功。

内容的提问来源于stack exchange，提问作者user22758952