tensorflow #python3.10 tried many times but no improvement - tensorflow

i have installed 3.10 version of python and now facing problem to inastall tensorflow C:\Python\python310>pip install tensorflow ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)

Tf-nightly Python3.10 wheels supports Linux and MacOS. You can get them via pip install tf-nightly on the corresponding operating system.
Take a look at this #comment

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Installing tensorflow with pip results in errors

I'm trying to install TensorFlow with pip, but it shows me the following output:
pip install tensorflow
Defaulting to user installation because normal site-packages is not writeable
ERROR: Could not find a version that satisfies the requirement tensorflow
ERROR: No matching distribution found for tensorflow
I'm using Python 3.9.1 on Arch Linux.
Python 3.9 is not supported as far as I can tell: https://www.tensorflow.org/install/pip#system-requirements
You would need to use a supported version of the Python interpreter. The current supported interpreters seem to be: Python 3.5–3.8

Installation Error:Collecting tensorflow ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)

Error raised when installing tensorflow using pip
C:\Users\Lenovo>python --version
Python 3.6.0
C:\Users\Lenovo>pip --version
pip 19.1.1 from c:\users\lenovo\appdata\local\programs\python\python36-32\lib\site-packages\pip (python 3.6)
C:\Users\Lenovo>pip install tensorflow
Collecting tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
You can use Python3.5.2 to install Tensorflow, but notice that it can not be installed on your environment and you should use base interpreter to install it then you can make your environment and inherit form the basic interpreter and use it in your environment.

How to install Keras with gpu support?

I installed Tensorflow for GPU using: pip install tensorflow-gpu
But when I tried the same for Keras pip install keras-gpu, it pulled me an error: could not find the version that satisfies the requirements.
Adding to the answer below which is the correct answer in terms of recommending to use Anaconda package manager, but out of date in that there is now a keras-gpu package on Anaconda Cloud.
So once you have Anaconda installed, you simply need to create a new environment where you want to install keras-gpu and execute the command:
conda install -c anaconda keras-gpu
This will install Keras along with both tensorflow and tensorflow-gpu libraries as the backend. (There is also no need to install separately the CUDA runtime and cudnn libraries as they are also included in the package - tested on Windows 10 and working).
There is not any keras-gpu package [UPDATE: now there is, see other answer above]; Keras is a wrapper around some backends, including Tensorflow, and these backends may come in different versions, such as tensorflow and tensorflow-gpu. But this does not hold for Keras itself, which should be installed simply with
pip install keras
independently of whatever backend is used (see the PyPi docs).
Additionally, and since you have tagged the question as anaconda, too, be informed that it is generally not advisable to mix your package managers (i.e pip with conda), and you may be better off installing Keras from the Anaconda cloud with
conda install -c conda-forge keras
Finally, you may be also interested to know that recent versions of Tensorflow include Keras as a subpackage, so you can use it without any additional installation; see https://www.tensorflow.org/guide/keras
For installing tensorflow-gpu from Anaconda cloud, you should use
conda install -c anaconda tensorflow-gpu
before installing Keras. Be sure you do it in a different virtual environment, or after having uninstalled other versions (i.e. pip-installed ones), as there have been reported problems otherwise.
Adding to the above two answers, ensure your TensorFlow/Keras environment is using Python 3.6. Keras/TensorFlow doesn't work very well with Python 3.7, as of May 10, 2019.
I tried to use Keras/TensorFlow with Python 3.7 and I ended up having to reinstall Anaconda, since it sort of broke my Anaconda Prompt.
To install tensorflow-gpu with particular cuda version 9.0, use:
conda install tensorflow-gpu cudatoolkit==9.0 -c anaconda
Similarly for keras-gpu

with python 3.6, Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow

C:\WINDOWS\system32>pip install tensorflow
Collecting tensorflow
Could not find a version that satisfies the requirement tensorflow (from versions: )
No matching distribution found for tensorflow
I installed the Python (3.6 64-bit), and wanna install tensorflow in Anaconda3.
And I upgraded pip to the latest version, 19.0.1.
Requirement already up-to-date: pip in c:\anaconda3\lib\site-packages (19.0.1)
So, how can I solve this problem?
if you have anaconda do conda install tensorflow. anaconda version of tensorflow is faster than pip anyways.

How to downgrade tensorflow version in colab?

I am using pip3 install tensorflow==1.8.0, but it doesn't have GPU support.
So I am using pip3 install tensorflow-gpu==1.8.0, but it still raises an exception
libcudart.so.VERSION No such file.
Should I use colab to install tensorflow from source?
After pip3 list:
tensorboard 1.10.0
tensorflow 1.10.0
tensorflow-hub 0.1.1
Google recommends you not to do pip installs!!!!
use this instead: %tensorflow_version 1.x
Restart the Runtime and check if its changed:
import tensorflow
print(tensorflow.__version__)
Here is a link to the main article:
https://colab.research.google.com/notebooks/tensorflow_version.ipynb#scrollTo=8UvRkm1JGUrk
You can downgrade Tensorflow to a previous version without GPU support on Google Colab. I ran:
!pip install tensorflow==1.14.0
import tensorflow as tf
print(tf.__version__)
which initially returned
2.0.0-dev20190130
but when I returned to it after a few hours, I got the version I requested:
1.14.0
Trying to downgrade to a version with GPU support:
!pip install tensorflow-gpu==1.14.0
requires restarting the runtime and fails, as importing import tensorflow as tf returns:
ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory
Update
When the import fails you can always downgrade CUDA to version 9.0 using following commands
!wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
!apt-get update
!apt-get install cuda=9.0.176-1
You can check the version of CUDA by running:
!nvcc --version
Second update
This code now seems to fail, see the follow-up question at How to downgrade to tensorflow-gpu version 1.12 in google colab
Google gives quite a simple solution to downgrade to the previously used Colab tf v.1.15.2. Just run the following magic line in Colab:
%tensorflow_version 1.x
Ther recommend "against using pip install to specify a particular TensorFlow version for both GPU and TPU backends. Colab builds TensorFlow from the source to ensure compatibility with our fleet of accelerators. Versions of TensorFlow fetched from PyPI by pip may suffer from performance problems or may not work at all". This means if you need GPU support, use one of the two given TF versions. The other versions will not necessary work I guess even for CPU.
The build process for GPU-enabled tensorflow is involved. In particular, old versions of TensorFlow use (or require) older versions of CUDA, which itself depends on system libraries and configuration beyond the scope of a pip install.
I suspect that downgrading TensorFlow on a VM configured for a newer version is going to be an involved process, perhaps involving downgrades / reinstalls of system libraries.
If it's practical, it might be simpler to update your code to use the latest version of TensorFlow, at least until Colab supports persistent backend enivronments.
It seems that only tensorflow 2 is supported by Colab, but that's not true, you still can use pip to uninstall tensorflow 2 and install a specific version of tf1. !yes|pip uninstall tensorflow, !pip install tensorflow==1.15.5 Maybe you should install other dependencies. So use !pip install -r requirements.txt Attention! You must restart the runtime in order to use newly installed versions.
%tensorflow_version 1.x no longer works.
%tensorflow_version 1.x
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-8d2919c1d33c> in <module>
----> 1 get_ipython().run_line_magic('tensorflow_version', '1.x')
1 frames
/usr/local/lib/python3.8/dist-packages/google/colab/_tensorflow_magics.py in _tensorflow_version(line)
33
34 if line.startswith("1"):
---> 35 raise ValueError(
36 # pylint: disable=line-too-long
37 textwrap.dedent("""\
ValueError: Tensorflow 1 is unsupported in Colab.
Your notebook should be updated to use Tensorflow 2.
See the guide at https://www.tensorflow.org/guide/migrate#migrate-from-tensorflow-1x-to-tensorflow-2.