WIN10 anaconda 安装 tensorflow-gpu不出错的最佳解决办法（不在系统安装CUDA）

来源：https://www.pugetsystems.com/labs/hpc/The-Best-Way-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-1187/

由于使用根据百度出来的WIN10下面使用anaconda和CUDA安装tensorflow-gpu的解决办法，总会报如下错误：

<> import tensorflow as tf
Traceback (most recent call last):
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in 
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in 
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 1, in 
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_init_.py", line 22, in 
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python_init_.py", line 49, in 
from tensorflow.python import pywrap_tensorflow
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 74, in 
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in 
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in 
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.

因而参考出错信息中推荐的网站 https://www.tensorflow.org/install/errors （需要fang-qiang才能打开）

找到最佳解决办法：https://www.pugetsystems.com/labs/hpc/The-Best-Way-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-1187/

以下是英文内容：

The Best Way to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA)

Written on June 21, 2018 by Dr Donald Kinghorn

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---

A couple of weeks ago I wrote a post titled Install TensorFlow with GPU Support on Windows 10 (without a full CUDA install). What you are reading now is a replacement for that post.

In that older post I couldn't find a way around installing at least some of CUDA. I tried to minimize it by installing only the essential DLL's to make things work. It did require making changes to the "User PATH" which I would rather not have done. I have now found a way around this using only Anaconda Python packages.

I was doing a pytorch install on Linux and Windows 10 and I noticed that they were adding the needed CUDA and cuDNN libraries with a separate, single, Anaconda package (pytorch/cuda90 or 91). I tried that package with TensorFlow but it didn't work. However, I searched Anaconda Cloud and found Anaconda supported packages for CUDA 9.0 and cuDNN 7 that did work! All I had to do was install these two packages in the conda virtual environment for TensorFlow. [ The Linux TensorFlow Anaconda package includes CUDA and cuDNN internally in the same package. ]

The focus here is to get a good GPU accelerated work environment for TensorFlow (with Keras and Jupyter notebook) up and running for Windows 10. You will not need to install CUDA for this!

In this post I'll walk you through the best way I have found so far to get a good TensorFlow work environment on Windows 10 including GPU acceleration. I'll also go through setting up Anaconda Python and create an environment for TensorFlow and how to make that available for use with Jupyter notebook. As a "non-trivial" example of using this setup we'll go through training LeNet-5 with Keras using TensorFlow with GPU acceleration. We'll get a setup that is 18 times faster than using the CPU alone.

Python Environment Setup with Anaconda Python

I highly recommend you use Anaconda Python. If you need some arguments for using Python take a look at my post Should You Learn to Program with Python. For arguments on why you should use the Anaconda Python distribution see, How to Install Anaconda Python and First Steps for Linux and Windows. The best reason for using Anaconda Python in the context of installing GPU accelerated TensorFlow is that by doing so you will not have to do a CUDA install on the system.

Anaconda is focused toward data-science and machine learning. It installs cleanly on your system in a single directory so it doesn't make a mess in your systems application and library directories. It is also performance optimized and links important numerical packages like numpy to Intel's MKL.

---

Install Anaconda Python

1) Download and check the installer

• Go to the Anaconda downloads page https://www.anaconda.com/downloads and get the Python 3.6 version.

• It's good practice to check the file hash to be sure you got a good copy. [ I have to admit that I don't always do this myself. ]

  • Open Powershell and cd to the directory where you downloaded the Anaconda installer exe file. In my case that is the Downloads directory.

cd Downloads

Then run

 Get-FileHash .\Anaconda3-5.2.0-Windows-x86_64.exe -Algorithm SHA256

• Look up the hash for the file you downloaded. and check that it matches.

Run the installer

Since you have Powershell open in the directory with the Anaconda installer exe file you can start it by just typing it's name (type A and hit tab to expand the name) and hitting return. [ You could alternatively just double click on the download install exe. from your file browser. ]

.\Anaconda3-5.1.0-Windows-x86_64.exe

The installer GUI should now be running.

• You will be asked to accept a license agreement ...
• "Select Install Type" I recommend you chose "Just Me" since this is part of your personal development environment.
• "Chose Install Location" I recommend you keep the default which is at the top level of you user directory.
• "Advanced Installation Options"
  My recommendation is to check both boxes. Make Anaconda Python 3 your default Python. And, as a developer you really should be aware of your PATHenvironment variable. So yes, go ahead and let the installer add the Anaconda bin directory to your PATH. If you haven't looked at your environment variables in awhile you should have a look. Do a search from the Windows menu for "environment variables". You should find a settings panel that will show your account environment and system wide environment. After the Anaconda install you will see that its application and library directories have been prepended to your user PATH. We'll look at it, and modify it, after installing the CUDA libraries.

VSCode?

• Next you will be asked if you want to install Microsoft VSCode. VSCode is a really good editor and it is available for free on Windows, Linux and MacOS. However, if you are interested in trying it out I would recommend that you go to the VSCode website and check it out first. If you think you want to try it, then go ahead and download it and install it yourself. I like VSCode but I usually use the Atom editor which also runs on Windows, Linux and MacOS. If you are checking out editors I recommend you try both of these as well as Sublime Text. They are all great editors!

Check your install

If you still have Powershell open you will need to close it and restart it so that it will re-read your environment variables and pick up your PATH which now includes the Anaconda Python directories. With Powershell reopened you can check that you now have Anaconda Python 3 as your default Python.

python --version

Python 3.6.5 :: Anaconda custom (64-bit)

Update your base Anaconda packages

conda is a powerful package and environment management tool for Anaconda. We'll use conda from Powershell to update our base Python install. Run the following commands. It may take some time to do this since there are a lot of modules to update.

conda update conda
conda update anaconda
conda update python
conda update --all

That should bring your entire base Anaconda install up to the latest packages. (Anaconda 5.2 had just been released when I wrote this and nearly everything was fully up-to-date.)

Anaconda Navigator

There is a GUI for Anaconda called anaconda-navigator. I personally find it distracting/confusing/annoying and prefer using conda from the command-line. Your taste may differ! ... and my opinion is subject to change if they keep improving it. If you are new to Anaconda then I recommend you read up on conda even (or especially!) if you are thinking about using the "navigator" GUI.

---

Create a Python "virtual environment" for TensorFlow using conda

You should set up an environment for TensorFlow separate from your base Anaconda Python environment. This keeps your base clean and will give TensorFlow a space for all of it's dependencies. It is in general good practice to keep separate environments for projects especially when they have special package dependencies. Think of it as a separate "name-space" for your project.

There are many possible options when creating an environment with conda including adding packages with specific version numbers and specific Python base versions. This is sometimes useful if you want fine control and it also helps with version dependencies resolution. Here we will keep it simple and just create a named environment and then activate that environment and install the packages we want inside of that.

From a command line do,

conda create --name tf-gpu

I named the environment 'tf-gpu' but you can use any name you want.

"activate" the environment

Now activate the environment, (I'll show my full terminal prompt and output instead of just the commands)

Note: for some reason Powershell will not run the "activate" script! You will need to start "CMD" shell to do this. You can start CMD shell from Powershell (notice how the "PS" that was at the beginning of the Powershell prompt disappears). Having to switch to CMD is an annoyance but you can easily switch back and forth in a Powershell window

PS C:\Users\don> cmd
Microsoft Windows [Version 10.0.16299.461]
(c) 2017 Microsoft Corporation. All rights reserved.

C:\Users\don> activate tf-gpu

(tf-gpu) C:\Users\don>

You can see that my CMD shell prompt is now preceded by the the name of the environment (tf-gpu). Any conda package (or pip) installs will now be local to this environment.

Install TensorFlow-GPU from the Anaconda Cloud Repositories

My preference would be to install the "official" Anaconda maintained TensorFlow-GPU package like I did for Ubuntu 18.04, unfortunately the Anaconda maintained Windows version of TensorFlow is way out-of-date (version 1.1). There is a current CPU-only version 1.8 for Windows but we want GPU acceleration.

A search for "tensorflow" on the Anaconda Cloud will list the available packages from Anaconda and the community. There is a package "aaronzs / tensorflow-gpu 1.8.0" listed near the top that has builds for Linux and Windows. This is the only up-to-date package I know of that is working correctly with Windows 10. This package was built by, and is being nicely maintained by, Aaron Sun. You can check out his GitHub page for the project.

Lets install TensorFlow with GPU acceleration in this conda environment.

(tf-gpu) C:\Users\don> conda install -c aaronzs tensorflow-gpu

Now, we can do the CUDA and cuDNN dependencies,

(tf-gpu) C:\Users\don> conda install -c anaconda cudatoolkit
(tf-gpu) C:\Users\don> conda install -c anaconda cudnn

Note that I explicitly use the -c flag to specify the "anaconda" "channel". That would be default if you leave out the channel name but in this case I wanted to be explicit about where the packages came from. The links are, cudatoolkit current is 9.0 and cudnn current is 7.1.4. You should check version numbers when you install.

That's it! You do not need to do a CUDA install on your system.

---

Check That TensorFlow is working with your GPU

Close any Powershell or CMD shells you had open and reopen one. You need to do that so that your new PATH settings get read in. You can use a CMD shell to activate your tf-gpu environment start Python and run the following lines,

>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))

My session including the output looked like this, (there was a long delay during this "first run" session startup )

PS C:\Users\don> cmd
Microsoft Windows [Version 10.0.16299.461]
(c) 2017 Microsoft Corporation. All rights reserved.

C:\Users\don> activate tf-gpu

(tf-gpu) C:\Users\don>python
Python 3.6.5 |Anaconda custom (64-bit)| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow')
>>> sess = tf.Session()
2018-06-01 16:37:57.666250: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-01 16:37:57.967130: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.645
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.62GiB
2018-06-01 16:37:57.975868: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-01 16:40:10.162112: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-01 16:40:10.168554: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929]      0
2018-06-01 16:40:10.171214: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0:   N
2018-06-01 16:40:10.174162: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6400 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
>>> print(sess.run(hello))
b'Hello, TensorFlow'
>>>

Yea! PATH's are correct and everything is working. You can see that it is has GPU support.

Next we will do something a little more useful and fun with Keras, after we configure Jupyter notebook to use our 'tf-gpu' environment.

---

Create a Jupyter Notebook Kernel for the TensorFlow Environment

You can work with an editor and the command line and you often want to do that, but, Jupyter notebooks are great for doing machine learning development work. In order to get Jupyter notebook to work the way you want with this new TensorFlow environment you will need to add a "kernel" for it.

With your tf-gpu environment activated do,

(tf-gpu) C:\Users\don>conda install ipykernel

Now create the Jupyter kernel,

(tf-gpu) C:\Users\don>python -m ipykernel install --user --name tf-gpu --display-name "TensorFlow-GPU"

With this "tf-gpu" kernel installed, when you start Jupyter notebook you will now have an option to to open a new notebook using this kernel.

 

 

---

An Example using Keras with TensorFlow Backend

In order to check everything out lets setup LeNet-5 using Keras (with our TensorFlow backend) using a Jupyter notebook with our "TensorFlow-GPU" kernel. We'll train the model on the MNIST digits data-set and then open TensorBoard to look at some plots of the job run.

Install Keras

With the tf-gpu environment activated do,

(tf-gpu) C:\Users\don\projects>conda install keras-gpu

You now have Keras installed utilizing your GPU accelerated TensorFlow.

Launch a Jupyter Notebook

With the tf-gpu environment activated start Jupyter,

(tf-gpu) C:\Users\don>jupyter notebook

From the 'New' drop-down menu select the 'TensorFlow-GPU' kernel that you added (as seen in the image in the last section). You can now start writing code!

MNIST example

Following are Python snippets you can copy into cells in your Jupyter notebook to setup and train LeNet-5 with MNIST digits data.

Import dependencies

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Flatten,  MaxPooling2D, Conv2D
from keras.callbacks import TensorBoard

Load and process the MNIST data

(X_train,y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000,28,28,1).astype('float32')
X_test = X_test.reshape(10000,28,28,1).astype('float32')

X_train /= 255
X_test /= 255

n_classes = 10
y_train = keras.utils.to_categorical(y_train, n_classes)
y_test = keras.utils.to_categorical(y_test, n_classes)

Create the LeNet-5 neural network architecture

model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(28,28,1)) )
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())          
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

Compile the model

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

Set log data to feed to TensorBoard for visual analysis

tensor_board = TensorBoard('./logs/LeNet-MNIST-1')

Train the model

model.fit(X_train, y_train, batch_size=128, epochs=15, verbose=1,
          validation_data=(X_test,y_test), callbacks=[tensor_board])

The results

After running that training for 15 epochs the last epoch gave,

Epoch 15/15
60000/60000 [==============================] - 6s 102us/step - loss: 0.0192 - acc: 0.9936 - val_loss: 0.0290 - val_acc: 0.9914

Not bad! Training accuracy 99.36% and Validation accuracy 99.14%

---

Look at the job run with TensorBoard

You will need "bleach" for TensorBoard so install it first,

(tf-gpu) C:\Users\don>conda install bleach

Start TensorBoard

 (tf-gpu) C:\Users\don\projects>tensorboard --logdir=./logs --port 6006

It will give you an address similar to http://stratw:6006 Open that in your browser and you will be greeted with (the wonderful) TensorBoard. These are the plots it had for that job run,

 

That was a model with 1.2 million training parameters and a dataset with 60,000 images. It took 1 minute and 26 seconds utilizing the NVIDIA GeForce 1070 in my laptop system! For reference it took 26 minutes using all cores at 100% of the Intel 6700HQ CPU in that system. That's an 18 fold speedup on the GPU!

Happy computing! --dbk

 

 重点安装过程的中文翻译如下：

使用以下方法为TensorFlow创建Python“虚拟环境”conda

您应该为TensorFlow设置一个环境，将其与基本的AnacondaPython环境分开。这将保持基本的清洁，并将为TensorFlow的所有依赖项提供空间。通常情况下，为项目保留单独的环境是很好的做法，特别是当项目具有特殊的包依赖关系时。把它想象成一个单独的“名称-空间”为您的项目。

在使用Conda创建环境时，有许多可能的选项，包括添加带有特定版本号和特定Python基础版本的包。如果您想要良好的控制，这有时是有用的，而且它也有助于解决版本依赖关系。在这里，我们将保持简单，只需创建一个命名环境，然后激活该环境，并在其中安装我们想要的包。

从命令行做，

conda create --name tf-gpu

我命名环境‘TF-GPU’，但你可以使用任何你想要的名字。

“激活”环境

现在激活环境(我将显示完整的终端提示符和输出，而不仅仅是命令)

注意：出于某些原因，Powershell将不运行“激活”脚本！您需要启动“CMD”shell才能做到这一点。您可以从Powershell启动CMDshell(注意Powershell提示符开头的“ps”是如何消失的)。必须切换到cmd是一个麻烦，但您可以轻松地在powershell窗口中来回切换。

PS C:\Users\don> cmd
Microsoft Windows [Version 10.0.16299.461]
(c) 2017 Microsoft Corporation. All rights reserved.

C:\Users\don> activate tf-gpu

(tf-gpu) C:\Users\don>

您可以看到，现在我的cmd shell提示符前面是环境的名称。(tf-gpu)。任何Conda包(或PIP)安装现在都是此环境的本地安装。

从Anaconda云存储库安装TensorFlow-GPU

我的首选是安装“官方”Anaconda维护的TensorFlow-GPU包，就像我为Ubuntu 18.04所做的那样，不幸的是，Anaconda维护的Windows版本TensorFlow已经过时(1.1版)。有一个当前的CPU-只有1.8版本的Windows，但我们希望GPU加速。

探索Anaconda云上的“TensorFlow”将列出来自Anaconda和社区的可用软件包。有一个包裹“aaronzs/TensorFlow-GPU 1.8.0”列出的顶部附近，有构建Linux和Windows。这是唯一最新的包裹我知道这在Windows 10中是正确的。这个包是由，亚伦·孙。您可以查看项目的GitHub页面。

首先更换conda的国内软件源：

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

让我们在这个Conda环境中安装带有GPU加速的TensorFlow。

(tf-gpu) C:\Users\don> conda install tensorflow-gpu==1.10.0

 

现在，我们可以做CUDA和cuDNN依赖，

(tf-gpu) C:\Users\don> conda install  cudatoolkit
(tf-gpu) C:\Users\don> conda install  cudnn

注意，我显式地使用了-c标志指定“anaconda”通道“。如果您省略了通道名称，这将是默认的，但在本例中，我希望明确说明包来自何处。链接是，Cudatoolkit电流是9.0和抱抱电流为7.1.4。安装时应检查版本号。

就这样您不需要在系统上安装CUDA。

---

检查TensorFlow是否与您的GPU一起工作

关闭打开的任何Powershell或CMD外壳并重新打开一个。您需要这样做，以便读取新的路径设置。您可以使用CMDshell激活TF-GPU环境，启动Python并运行以下行，

>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))

我的会话(包括输出)如下所示(在“第一次运行”会话启动过程中出现了很长的延迟)

PS C:\Users\don> cmd
Microsoft Windows [Version 10.0.16299.461]
(c) 2017 Microsoft Corporation. All rights reserved.

C:\Users\don> activate tf-gpu

(tf-gpu) C:\Users\don>python
Python 3.6.5 |Anaconda custom (64-bit)| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow')
>>> sess = tf.Session()
2018-06-01 16:37:57.666250: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-01 16:37:57.967130: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.645
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.62GiB
2018-06-01 16:37:57.975868: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-01 16:40:10.162112: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-01 16:40:10.168554: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929]      0
2018-06-01 16:40:10.171214: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0:   N
2018-06-01 16:40:10.174162: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6400 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
>>> print(sess.run(hello))
b'Hello, TensorFlow'
>>>

耶！路径是正确的，一切正常。您可以看到它是有GPU支持的。