前言：

看到網(wǎng)上大部分TensorFlowGPU教程都需要看配置需要裝一大堆東西，然后就來更新一篇我知道的最簡便的安裝GPU版本的方法了，不用安裝CUDA CUDNN VS啥的，可能目前內(nèi)容上有點(diǎn)粗糙，后續(xù)會慢慢完善的。個人安裝的目的就是為了重新下載一個與keras版本匹配的TensorFlow，具體可以通過這個網(wǎng)址https://docs.floydhub.com/guides/environments/查看Keras與TensorFlow的對應(yīng)版本，然后我卸載了之前的TensorFlowGPU版本，順便來記錄一下安裝過程，當(dāng)然，讀者可以根據(jù)自己的需要去下載其他的TensorFlow-GPU

一、準(zhǔn)備階段

1.設(shè)備環(huán)境：win10+你電腦上有GPU。。。（雖然聽起來有點(diǎn)無腦）

2.安裝anaconda，這部分就不多說了，需要下載的請直接到官網(wǎng)下載，然后直接安裝，不放心的話去看看其他博客教程

3.卸載TensorFlow-GPU+Keras這一步是我自己需要做的，如果你原先沒有Tensorflow-gpu就不用管了，直接跳到下一步。

卸載指令：conda uninstall tensorflow-gpu==1.14.0

                  conda uninstall keras==1.0.8

二、安裝

1.如果你剛安裝好anaconda，或者出于其他原因，第一步就從建立一個虛擬環(huán)境開始吧。

按照下圖的順序來，②的命名自己隨意，③的選項(xiàng)建議選3.6或7吧，或者有更高版本也好，盡量別選2.X，2.7的據(jù)說都快要停止維護(hù)了，對python新手而言也不要選版本太高的，可能不穩(wěn)定。

點(diǎn)擊創(chuàng)建之后可能會等一段時間（因?yàn)闀鋫湎嚓P(guān)必要的庫所以可能有點(diǎn)慢）。

2.虛擬環(huán)境建完之后，操作如下圖：  

再點(diǎn)擊“open terminal”進(jìn)入終端界面：

3.接下來就是重頭戲了，安裝TensorFlow-GPU：

在終端輸入：conda install -c aaronzs tensorflow-gpu

默認(rèn)應(yīng)該是下載最新版本，如果要特定版本，就conda install -c aaronzs tensorflow-gpu==xxx

但是我發(fā)現(xiàn)這個方法無法下載2.2.0版本，所以我退而求其次地下載了2.2.1了

這個過程可能會報warning什么的，如果可以運(yùn)行就忽略，有報什么錯歡迎在評論區(qū)留言。

然后就是漫長地等待了

完成后再輸入 conda list看看

4.安裝成功?。〗Y(jié)束了?。。?！對就是這么簡單?。。?！

但是，最后我們還是測試一下到底有沒有配置成功：

因?yàn)槲矣玫氖荘ycharm,這里再附加一下Pycharm配置Anaconda環(huán)境的過程：

按照下圖所示，或者直接Ctrl+Alt+S

打開設(shè)置之后就按照下圖操作，選完之后就全點(diǎn)"OK"

操作之后，可以看到右下角會顯示當(dāng)前的環(huán)境：

然后關(guān)鍵的一步測試了！操作如下:

新建一個python文件，然后輸入以下代碼，然后運(yùn)行：

import tensorflow as tf

import os

#os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

a = tf.constant(1.)

b = tf.constant(2.)

print(a+b)

print(tf.__version__)

print('GPU:', tf.config.list_physical_devices('GPU'))

print(tf.test.is_gpu_available())

輸出結(jié)果為：

tf.Tensor(3.0, shape=(), dtype=float32)

2.1.0

GPU: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

True

當(dāng)然，，由于是用GPU跑的，所以會有這些信息：

2021-01-06 11:54:57.365737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2021-01-06 11:55:00.178440: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2021-01-06 11:55:00.862896: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: 

pciBusID: 0000:01:00.0 name: GeForce GTX 1050 computeCapability: 6.1

coreClock: 1.493GHz coreCount: 5 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 104.43GiB/s

2021-01-06 11:55:00.863272: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2021-01-06 11:55:00.868083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2021-01-06 11:55:00.872729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2021-01-06 11:55:00.874234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2021-01-06 11:55:00.879152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2021-01-06 11:55:00.881920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2021-01-06 11:55:00.892460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2021-01-06 11:55:00.892777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0

2021-01-06 11:55:00.893388: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

2021-01-06 11:55:00.895935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: 

pciBusID: 0000:01:00.0 name: GeForce GTX 1050 computeCapability: 6.1

coreClock: 1.493GHz coreCount: 5 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 104.43GiB/s

2021-01-06 11:55:00.896318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2021-01-06 11:55:00.896510: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2021-01-06 11:55:00.896701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2021-01-06 11:55:00.896891: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2021-01-06 11:55:00.897081: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2021-01-06 11:55:00.897269: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2021-01-06 11:55:00.897458: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2021-01-06 11:55:00.897699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0

2021-01-06 11:55:01.557072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:

2021-01-06 11:55:01.557299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0 

2021-01-06 11:55:01.557426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N 

2021-01-06 11:55:01.557723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1335 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1)

然后就結(jié)束了！?。⊥瓿桑。?！