Tensorflow не видит gpu windows 10

So as of 2022-04, the tensorflow package contains both CPU and GPU builds. To install a GPU build, search to see what’s available:

λ conda search tensorflow
Loading channels: done
# Name                       Version           Build  Channel
tensorflow                    0.12.1          py35_1  conda-forge
tensorflow                    0.12.1          py35_2  conda-forge
tensorflow                     1.0.0          py35_0  conda-forge
…
tensorflow                     2.5.0 mkl_py39h1fa1df6_0  pkgs/main
tensorflow                     2.6.0 eigen_py37h37bbdb1_0  pkgs/main
tensorflow                     2.6.0 eigen_py38h63d3545_0  pkgs/main
tensorflow                     2.6.0 eigen_py39h855417c_0  pkgs/main
tensorflow                     2.6.0 gpu_py37h3e8f0e3_0  pkgs/main
tensorflow                     2.6.0 gpu_py38hc0e8100_0  pkgs/main
tensorflow                     2.6.0 gpu_py39he88c5ba_0  pkgs/main
tensorflow                     2.6.0 mkl_py37h9623b36_0  pkgs/main
tensorflow                     2.6.0 mkl_py38hdc16138_0  pkgs/main
tensorflow                     2.6.0 mkl_py39h31650da_0  pkgs/main

You can see that there are builds of TF 2.6.0 that support Python 3.7, 3.8 and 3.9, and that are built for MKL (Intel CPU), Eigen, or GPU.

To narrow it down, you can use wildcards in the search. This will find any Tensorflow 2.x version that is built for GPU, for instance:

λ conda search tensorflow=2*=gpu*
Loading channels: done
# Name                       Version           Build  Channel
tensorflow                     2.0.0 gpu_py36hfdd5754_0  pkgs/main
tensorflow                     2.0.0 gpu_py37h57d29ca_0  pkgs/main
tensorflow                     2.1.0 gpu_py36h3346743_0  pkgs/main
tensorflow                     2.1.0 gpu_py37h7db9008_0  pkgs/main
tensorflow                     2.5.0 gpu_py37h23de114_0  pkgs/main
tensorflow                     2.5.0 gpu_py38h8e8c102_0  pkgs/main
tensorflow                     2.5.0 gpu_py39h7dc34a2_0  pkgs/main
tensorflow                     2.6.0 gpu_py37h3e8f0e3_0  pkgs/main
tensorflow                     2.6.0 gpu_py38hc0e8100_0  pkgs/main
tensorflow                     2.6.0 gpu_py39he88c5ba_0  pkgs/main

To install a specific version in an otherwise empty environment, you can use a command like:

λ conda activate tf

(tf) λ conda install tensorflow=2.6.0=gpu_py39he88c5ba_0

…

The following NEW packages will be INSTALLED:

  _tflow_select      pkgs/main/win-64::_tflow_select-2.1.0-gpu
  …
  cudatoolkit        pkgs/main/win-64::cudatoolkit-11.3.1-h59b6b97_2
  cudnn              pkgs/main/win-64::cudnn-8.2.1-cuda11.3_0
  …
  tensorflow         pkgs/main/win-64::tensorflow-2.6.0-gpu_py39he88c5ba_0
  tensorflow-base    pkgs/main/win-64::tensorflow-base-2.6.0-gpu_py39hb3da07e_0
  …

As you can see, if you install a GPU build, it will automatically also install compatible cudatoolkit and cudnn packages. You don’t need to manually check versions for compatibility, or manually download several gigabytes from Nvidia’s website, or register as a developer, as it says in other answers or on the official website.

After installation, confirm that it worked and it sees the GPU by running:

λ python
Python 3.9.12 (main, Apr  4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'2.6.0'
>>> tf.config.list_physical_devices()
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Getting conda to install a GPU build and other packages you want to use is another story, however, because there are a lot of package incompatibilities for me. I think the best you can do is specify the installation criteria using wildcards and cross your fingers.

This tries to install any TF 2.x version that’s built for GPU and that has dependencies compatible with Spyder and matplotlib’s dependencies, for instance:

λ conda install tensorflow=2*=gpu* spyder matplotlib

For me, this ended up installing a two year old GPU version of tensorflow:

  matplotlib         pkgs/main/win-64::matplotlib-3.5.1-py37haa95532_1
  spyder             pkgs/main/win-64::spyder-5.1.5-py37haa95532_1
  tensorflow         pkgs/main/win-64::tensorflow-2.1.0-gpu_py37h7db9008_0

I had previously been using the tensorflow-gpu package, but that doesn’t work anymore. conda typically grinds forever trying to find compatible packages to install, and even when it’s installed, it doesn’t actually install a gpu build of tensorflow or the CUDA dependencies:

λ conda list
…
cookiecutter              1.7.2              pyhd3eb1b0_0
cryptography              3.4.8            py38h71e12ea_0
cycler                    0.11.0             pyhd3eb1b0_0
dataclasses               0.8                pyh6d0b6a4_7
…
tensorflow                2.3.0           mkl_py38h8557ec7_0
tensorflow-base           2.3.0           eigen_py38h75a453f_0
tensorflow-estimator      2.6.0              pyh7b7c402_0
tensorflow-gpu            2.3.0                he13fc11_0

An truing to get TensorFlow to recognize that there is a GPU installed on the PC.
Windows10 Pro 64bit version
Nvidia GTX1660 TI with latest drivers
Tensorflow — 2.3.0-dev20200615
CUDA v10.0
cudnn 7.6.5.32

print(device_lib.list_local_devices())

print(device_lib.list_local_devices())
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 4283633287383166880
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 5791820499534504026
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 15245658627817172966
physical_device_desc: "device: XLA_GPU device"
]

so system sees that there isa GPU installed, however

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
Num GPUs Available:  0

Trying to get a model to train using the GPU to complete faster — my current training takes 5 hours per epoch…..please help as its for a college project and i really need the speed of the GPU to help.

>>>from tensorflow.python.client import device_lib
        >>>print(device_lib.list_local_devices())
                    [name: "/device:CPU:0"
                device_type: "CPU"
                memory_limit: 268435456
                locality {
                }
                incarnation: 7692219132769779763
                ]

>>>tf.debugging.set_log_device_placement(True)
    >>>a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
2020-12-13 10:11:30.902956: 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.
>>>b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>>c = tf.matmul(a, b)
>>>print(c)
tf.Tensor(
[[22. 28.]
[49. 64.]], shape=(2, 2), dtype=float32)

C:WINDOWSsystem32>wmic path win32_VideoController get name
Name
NVIDIA GeForce 940MX
Intel(R) HD Graphics 620

>>> import tensorflow as tf
>>> tf.__version__
'2.3.0'

Python 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.

4 ответа

Лучший ответ

conda install tensorflow-gpu=2.1
pip install tensorflow-gpu==2.3.1

conda create --name tf_gpu tensorflow-gpu 

import tensorflow as tf