Run TensorFlow 2.0 on CPU without AVX

you can download and install NO AVX whl from source: https://github.com/yaroslavvb/tensorflow-community-wheels/issues/174

There is a brand new wheel file in the repository:

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

The following file is working very well:

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

As stated in the Readme.md:

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

Take a look at this test:

>>>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"
]