在没有 AVX 的 CPU 上运行 TensorFlow 2.0

问题描述

我想安装和使用 TensorFlow 2.0.我有一台装有 Windows 10 的 PC、Geforce GTX 1080 Ti GPU 和旧的 Intel Xeon X5660 CPU，不支持 AVX.

I would like to install and use TensorFlow 2.0. I have a PC with Windows 10, a Geforce GTX 1080 Ti GPU and an old Intel Xeon X5660 CPU, which doesn't support AVX.

现在，我的问题是每当我尝试在这台机器上运行任何 TensorFlow 代码时都会出现 DLL 导入错误.我知道 此存储库 为旧版 CPU 提供解决方案，但不幸的是我找不到任何TensorFlow 2.0 包在那里.

Now, my problem is that there is a DLL Import error whenever I attempt to run any TensorFlow code on this machine. I know about this repository providing a solution for legacy CPUs but unfortunately I can't find any TensorFlow 2.0 packages there.

任何帮助将不胜感激.谢谢.

Any help would be highly appreciated. Thank you.

推荐答案

仓库中有一个全新的wheel文件:

There is a brand new wheel file in the repository:

https://github.com/fo40225/tensorflow-windows-wheel

以下文件运行良好:

https://github.com/fo40225/tensorflow-windows-wheel/blob/master/2.0.0/py37/GPU/cuda101cudnn76sse2/tensorflow_gpu-2.0.0-cp37-cp37m-win_amd64.whl

如 Readme.md 中所述:

As stated in the Readme.md:

第一次执行TensorFlow时，编译需要时间."

"It will take time for compiling when execute TensorFlow first time."

看看这个测试:

>>>import tensorflow as tf
tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

>>>print(tf.__version__)
2.0.0

>>>from tensorflow.python.client import device_lib
>>>print(device_lib.list_local_devices())

tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.531
GPU libraries are statically linked, skip dlopen check.
Adding visible gpu devices: 0
Device interconnect StreamExecutor with strength 1 edge matrix:
     0
0:   N
Created TensorFlow device (/device:GPU:0 with 1340 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1)

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 4456898788177247918
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 1406107238
locality {
  bus_id: 1
  links {
  }
}
incarnation: 3224787151756357043
physical_device_desc: "device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1"
]

这篇关于在没有 AVX 的 CPU 上运行 TensorFlow 2.0的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！