我正在尝试使用pip安装TensorFlow:

$ pip install tensorflow --user
Collecting tensorflow
Could not find a version that satisfies the requirement tensorflow (from versions: )
No matching distribution found for tensorflow

我做错了什么?到目前为止,我使用Python和pip没有任何问题。


当前回答

使用Git更容易,他们提供了网站上的方法,但链接访问可能不是很重要,你可以从中读取

引用https://www.tensorflow.org/install/source_windows

git clone https://github.com/tensorflow/tensorflow.git

我的Python版本是3.9.7。我也使用Windows 10,要求如下:

1. Microsoft C++ Retribution installed from Microsoft Visual Studio that matches with x64bits as required in the list.

1.1 Microsoft Visual C++ 2012 Redistribution ( x64 ) and updates    
1.2 Microsoft Visual C++ 2013 Redistributable (x64) - 12.0.40664
1.3 Microsoft Visual C++ 2015-2019 Redistributable (x64) - 14.29.30133
1.4 vs_community__1795732196.1624941787.exe updates

2. Python and AI learning 
tensorboard                2.6.0
tensorboard-data-server    0.6.1
tensorboard-plugin-profile 2.5.0
tensorboard-plugin-wit     1.8.0
***tensorflow                 2.6.0
tensorflow-datasets        4.4.0
tensorflow-estimator       2.6.0
***tensorflow-gpu             2.6.0
tensorflow-hub             0.12.0
tensorflow-metadata        1.2.0
tensorflow-text            2.6.0
***PyOpenGL                   3.1.5
pyparsing                  2.4.7
python-dateutil            2.8.2
python-slugify             5.0.2
python-speech-features     0.6
PyWavelets                 1.1.1
PyYAML                     5.4.1
scikit-image               0.18.3
scikit-learn               1.0.1
***gym                        0.21.0

其他回答

在Windows中安装TensorFlow的URL,下面是URL。这对我来说很有效。

python -m pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0-py3-none-any.whl

目前PIP没有32位版本的tensorflow,当我卸载python 32位并安装x64时,它可以工作

如果你正在使用Anaconda Python安装,pip install tensorflow将给出上面所述的错误,如下所示:

Collecting tensorflow
Could not find a version that satisfies the requirement tensorflow (from versions: )
No matching distribution found for tensorflow

根据TensorFlow安装页面,当运行pip install时,你需要使用——ignore-installed标志。

然而,在此之前,请参阅此链接 确保TF_BINARY_URL变量与你想要安装的TensorFlow版本相关的设置正确。

我的环境:Win 10, python 3.6

pip3 install --upgrade tensorflow
pip install --upgrade tensorflow

错误:

> Collecting tensorflow Could not find a version that satisfies the
> requirement tensorflow (from versions: ) No matching distribution
> found for tensorflow

我还尝试了pip install tensorflow和pip install tensorflow-gpu。 但错误:

> Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
> Could not find a version that satisfies the requirement tensorflow-gpu (from versions: ) No matching distribution found for tensorflow-gpu

使用Step (https://www.tensorflow.org/install/install_windows)尝试安装OK

Follow the instructions on the Anaconda download site to download and install Anaconda. https://www.continuum.io/downloads Create a conda environment named tensorflow by invoking the following command: C:> conda create -n tensorflow pip python=3.5 Activate the conda environment by issuing the following command: C:> activate tensorflow (tensorflow)C:> # Your prompt should change Issue the appropriate command to install TensorFlow inside your conda environment. To install the CPU-only version of TensorFlow, enter the following command: (tensorflow)C:> pip install --ignore-installed --upgrade tensorflow To install the GPU version of TensorFlow, enter the following command (on a single line): (tensorflow)C:> pip install --ignore-installed --upgrade tensorflow-gpu

当我试图在我的Mac上安装(使用Python 2.7)时,我遇到了同样的错误。根据Yash Kumar Verma在本页上的不同回答,我在这里给出的类似解决方案似乎也适用于Windows 8.1上的Python 3

解决方案

第一步:进入TensorFlow安装页面的TensorFlow Python包的URL部分,复制相关链接的URL用于您的Python安装。

第二步:打开终端/命令提示符,执行以下命令: PIP install -upgrade[在这里粘贴复制的url链接]

所以对我来说是这样的: PIP安装——升级https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl

更新(2017年7月21日):我和其他一些在Windows机器上运行Python 3.6的人尝试了这一点,他们必须将第2步中的行更改为: Python -m PIP install[在这里粘贴复制的url链接]

更新(2018年7月26日):对于Python 3.6.2(不是3.7,因为在TF文档中的3.6.2中),您还可以在步骤2中使用pip3 install——upgrade[粘贴复制的URL]。