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TensorFlow安裝CPU版本和GPU版本的實(shí)現(xiàn)步驟

 更新時(shí)間：2025年03月12日 09:41:54   作者：席惜兮兮  
本文主要介紹了TensorFlow安裝CPU版本和GPU版本的實(shí)現(xiàn)步驟,文中通過圖文示例介紹的非常詳細(xì),對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)學(xué)習(xí)吧

前言

下載的Anaconda是Anaconda3-2024.02-1-Windows-x86_64版本

一、TensorFlow安裝CPU版本

本例子，下載的Python版本為3.11.7和tensorflow版本為2.16.1

1.新建虛擬環(huán)境

打開Anaconda Prompt，輸入

conda create -n myenvname python=3.11.7

“myenvname”為自己的虛擬環(huán)境名字

在這里插入圖片描述

2.激活虛擬環(huán)境

繼續(xù)輸入

activate myenvname

“myenvname”為自己的虛擬環(huán)境名字

在這里插入圖片描述

3.下載tensorflow

直接安裝tensorflow會(huì)遇到以下報(bào)錯(cuò)，這是提示有一些依賴沒有安裝

在這里插入圖片描述

所以我先安裝了依賴再下載tensorflow

pip install joblib==1.2.0 scipy==1.11.4 tabulate==0.9.0 tqdm==4.65.0 tensorflow==2.16.1 -i https://mirrors.aliyun.com/pypi/simple

在這里插入圖片描述

4.驗(yàn)證是否下載成功

輸入ipython，進(jìn)入交互環(huán)境（要是報(bào)錯(cuò)，那可能是沒有ipython，可以pip list查看一下，沒有的話需要下載一個(gè)）
導(dǎo)入tensorflow

import tensorflow as tf

在這里插入圖片描述

成功

二、TensorFlow安裝GPU版本

本例子，下載的CUDA版本是11.5.2，cuDNN的版本是8.3.2，Python環(huán)境是3.9，tensorflow-gpu的版本是2.7.0。注：CUDA、cuDNN、python的環(huán)境要對(duì)應(yīng)，不然會(huì)安裝失?。ê苤匾。。。?。

1.新建虛擬環(huán)境

打開Anaconda Prompt，輸入

conda create -n myenvname python=3.9

“myenvname”為自己的虛擬環(huán)境名字

在這里插入圖片描述

2.激活虛擬環(huán)境

activate myenvname

“myenvname”為自己的虛擬環(huán)境名字

在這里插入圖片描述

3.安裝tensorflow-gpu

pip install tensorflow-gpu==2.7.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

在這里插入圖片描述

4.驗(yàn)證是否下載成功

進(jìn)入python環(huán)境，導(dǎo)入tensorflow

 import tensorflow as tf

要是遇到這個(gè)問題，提示protobuf版本過低

(tensorflow2) C:\Users\asus>python
Python 3.9.19 (main, May  6 2024, 20:12:36) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\__init__.py", line 41, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\__init__.py", line 41, in <module>
    from tensorflow.python.eager import context
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\eager\context.py", line 33, in <module>
    from tensorflow.core.framework import function_pb2
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\core\framework\function_pb2.py", line 16, in <module>
    from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\core\framework\attr_value_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\core\framework\tensor_pb2.py", line 16, in <module>
    from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\core\framework\resource_handle_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\core\framework\tensor_shape_pb2.py", line 36, in <module>
    _descriptor.FieldDescriptor(
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\google\protobuf\descriptor.py", line 553, in __new__
    _message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
>>>

輸入exit()退出python環(huán)境，回到虛擬環(huán)境

pip install protobuf==3.19.6 -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn

在這里插入圖片描述

再次進(jìn)入python環(huán)境，輸入“import tensorflow as tf”，要是遇到如下問題，提示TensorFlow與NumPy的版本不兼容

(tensorflow2) C:\Users\asus>python
Python 3.9.19 (main, May  6 2024, 20:12:36) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "<stdin>", line 1, in <module>
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\__init__.py", line 41, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\__init__.py", line 41, in <module>
    from tensorflow.python.eager import context
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\eager\context.py", line 38, in <module>
    from tensorflow.python.client import pywrap_tf_session
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\client\pywrap_tf_session.py", line 23, in <module>
    from tensorflow.python.client._pywrap_tf_session import *
AttributeError: _ARRAY_API not found

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "<stdin>", line 1, in <module>
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\__init__.py", line 41, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\__init__.py", line 46, in <module>
    from tensorflow.python import data
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\__init__.py", line 25, in <module>
    from tensorflow.python.data import experimental
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\__init__.py", line 98, in <module>
    from tensorflow.python.data.experimental import service
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\service\__init__.py", line 374, in <module>
    from tensorflow.python.data.experimental.ops.data_service_ops import distribute
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\ops\data_service_ops.py", line 27, in <module>
    from tensorflow.python.data.experimental.ops import compression_ops
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\ops\compression_ops.py", line 20, in <module>
    from tensorflow.python.data.util import structure
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\util\structure.py", line 26, in <module>
    from tensorflow.python.data.util import nest
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\util\nest.py", line 40, in <module>
    from tensorflow.python.framework import sparse_tensor as _sparse_tensor
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\sparse_tensor.py", line 28, in <module>
    from tensorflow.python.framework import constant_op
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\constant_op.py", line 29, in <module>
    from tensorflow.python.eager import execute
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\eager\execute.py", line 27, in <module>
    from tensorflow.python.framework import dtypes
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\dtypes.py", line 30, in <module>
    from tensorflow.python.lib.core import _pywrap_bfloat16
AttributeError: _ARRAY_API not found
ImportError: numpy.core._multiarray_umath failed to import
ImportError: numpy.core.umath failed to import
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\__init__.py", line 41, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\__init__.py", line 46, in <module>
    from tensorflow.python import data
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\__init__.py", line 25, in <module>
    from tensorflow.python.data import experimental
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\__init__.py", line 98, in <module>
    from tensorflow.python.data.experimental import service
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\service\__init__.py", line 374, in <module>
    from tensorflow.python.data.experimental.ops.data_service_ops import distribute
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\ops\data_service_ops.py", line 27, in <module>
    from tensorflow.python.data.experimental.ops import compression_ops
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\experimental\ops\compression_ops.py", line 20, in <module>
    from tensorflow.python.data.util import structure
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\util\structure.py", line 26, in <module>
    from tensorflow.python.data.util import nest
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\data\util\nest.py", line 40, in <module>
    from tensorflow.python.framework import sparse_tensor as _sparse_tensor
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\sparse_tensor.py", line 28, in <module>
    from tensorflow.python.framework import constant_op
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\constant_op.py", line 29, in <module>
    from tensorflow.python.eager import execute
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\eager\execute.py", line 27, in <module>
    from tensorflow.python.framework import dtypes
  File "C:\Users\asus\.conda\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\dtypes.py", line 33, in <module>
    _np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type()
TypeError: Unable to convert function return value to a Python type! The signature was
        () -> handle
>>>

輸入exit()退出python環(huán)境，回到虛擬環(huán)境

pip install numpy==1.21.6 -i https://pypi.tuna.tsinghua.edu.cn/simple/

在這里插入圖片描述

進(jìn)入python環(huán)境，輸入

import tensorflow as tf
tf.__version__
tf.test.is_gpu_available()

在這里插入圖片描述

查看版本2.7.0，版本正確。末尾顯示True，TensorFlow檢測(cè)到可用的GPU，安裝成功，exit()退出python環(huán)境

后續(xù)我想用ipython查看是否安裝成功，出現(xiàn)以下問題進(jìn)入ipython環(huán)境，輸入

import tensorflow as tf
tf.__version__
tf.test.is_gpu_available()

在這里插入圖片描述

創(chuàng)建虛擬環(huán)境的時(shí)候指定python版本為3.9，但是這里卻顯示3.11.7。
末尾顯示False，TensorFlow沒有檢測(cè)到可用的GPU。
猜測(cè)可能是這個(gè)虛擬環(huán)境沒有ipython，可能用了其他環(huán)境的ipython。
解決方案，可以在虛擬環(huán)境中用pip list查看虛擬環(huán)境中是否有ipython，要是沒有，需要安裝一個(gè)，然后就可以解決了

到此這篇關(guān)于TensorFlow安裝CPU版本和GPU版本的實(shí)現(xiàn)步驟的文章就介紹到這了,更多相關(guān)TensorFlow安裝CPU和GPU內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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