I know there are a lot of tutorials of installing keras on win. But I am faced with a weird problem and I cannot find a solution on Google.
The version of python I used is 2.7(anaconda 64bit).
When I typed 'pip install keras' in cmd, an error occurred.
It is said that no matching distribution found for tensorflow(from keras).
I googled it and one saying goes that this problem occurred when python is 32bit on windows. However, my python is 64-bit, so this saying does not hold.
I want to know how to handle this, given that I want to use theano as keras backend instead of tensorflow.
I think the problem is that TensorFlow on Windows only support Python 3.5.X.
You should try installing anaconda3.
Source: https://www.tensorflow.org/install/install_windows
using commad pip install -v -v -v keras may offer you more information about this
and I find this commad may download Keras tar file in site https://pypi.python.org/simple/keras/
and after download file, it will check the requires.txt in Keras.egg-info
then i find in Keras 2.0.0 it require tensorflow instead of theano in this version, otherwise you may fail to install
so the solution is, download the other keras in site which requires theano not tensorflow, and unpack it, then cd its directory, and use python setup.py install to install this version of keras
Related
I am working on a project on Reinforcement Learning - and completely new at this. I installed keras-rl as
pip install keras-rl, however it caused an error as many has mentioned:
TypeError: Keras symbolic inputs/outputs do not implement `__len__`. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.
And the solution for this is to used keras-rl2. Howver I cannot install keras-rl2 on my Mac M1 as the output is following:
ERROR: Cannot install keras-rl2==1.0.0, keras-rl2==1.0.1, keras-rl2==1.0.2, keras-rl2==1.0.3, keras-rl2==1.0.4 and keras-rl2==1.0.5 because these package versions have conflicting dependencies.
When I tried a specific version, i.e
pip install keras-rl2==1.0.5, the output is:
ERROR: Could not find a version that satisfies the requirement tensorflow (from keras-rl2) (from versions: none)
ERROR: No matching distribution found for tensorflow
I have tensorflow 2.6.0 installed by Apple Developer guide. Any idea how I can install keras-rl2?
1. To install Tensorflow on M1 Macs
https://developer.apple.com/metal/tensorflow-plugin/
2. To Install Keras-rl2
Open a terminal window and run these commands from:
https://github.com/keras-rl/keras-rl (last line changed)
git clone https://github.com/wau/keras-rl2.git
cd keras-rl
python3 setup.py install
The setup.py script will still tell you that it can't find a TensorFlow version but you will still be able to import it using:
import rl
I'm trying to use tensorflow more accurately keras but it used tensorflow backend. I have had to convert my python to 3.6.2 in order to download tensorflow and even though it installs when I go to import it doesnt work saying :
ModuleNotFoundError: No module named 'tensorflow'
So i have tried installing Anaconda and performing this input:
conda create -n tensorflow_cpu pip python=3.6 #also tried with just tensorflow
activate tensorflow_cpu
pip install --ignore-installed --upgrade tensorflow
Then I get this error:
ERROR: Could not find a version that satisfies the requirement tensorflow_cpu (from versions: none)
ERROR: No matching distribution found for tensorflow_cpu
So now I am completely stuck. Why is it so hard to just import a module. Why hasn't tensorflow made the effort to make it work with newer versions of python. I am trying to perform machine learning projects but this issue is demotivating me massively and is turning me away from it all together.
If anyone has a solution or reason could you please let me know it simple matters so i can follow thoroughly. Thanks for your time.
If you are on Windows install Python 3.6.x and then Tensorflow as of 8-13-2018 and if you are on Linux just follow this link's instruction https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html .
I have set up a Ubuntu 18.04 and tried to make Tensorflow 2.2 GPU work (I have an Nvidia/CUDA graphic card) with Python.
Even after reading the documentation https://www.tensorflow.org/install/gpu#linux_setup, it failed (see below for details about how it failed).
Question: would you have a canonical "todo" list (starting point: freshly installed Ubuntu server) on how to install tensorflow-gpu and make it work, with a few steps?
Notes:
I have read many similar forum posts, and I think that having a canonical "todo" (from a fresh Ubuntu install to having tensorflow-gpu working) would be interesting, with a few steps/bash commands
the documentation I used involved
export LD_LIBRARY_PATH...
# Add NVIDIA package repository
sudo apt-key adv --fetch-keys http://developer.download...
...
# Install CUDA and tools. Include optional NCCL 2.x
sudo apt install cuda9.0 cuda...
Even after a lot of trial and errors (I don't copy/paste all the different errors here, would be too long), then at the end:
import tensorflow
always failed. Some reasons included `ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory. I have already read the relevant question here, or this very long (!) Github issue.
After some trial and error, import tensorflow works, but it doesn't use the GPU (see also Tensorflow not running on GPU).
Well, I was facing the same problem. The first thing to do is to look up, which Tensorflow version is required. In your case Tensorflow 2.2. requires CUDA 10.1. The correct cuDNN version is also important. In your case it would be cuDNN 7.4. An additional point is the installed python version. I would recommend Python 3.5-3.8. If one those mismatch, a fully compatibility is almost impossible.
So if you want a check list, here you go:
Install CUDA 10.1 by installing nvidia-cuda-toolkit.
Install the cuDNN version compatible with CUDA 10.1.
Export CUDA environment variables.
If Bazel is not installed, you will be asked on that.
Install TensorFlow 2.2 using pip. I would highly recommend the usage of a virtual environment.
You can find the compatibility check list of Tensorflow and CUDA here
You can find the CUDA Toolkit here
Finally get cuDNN in the correct version here
That's all.
I faced the problem as well when using the Google Cloud Platform for two projects involving deep learning. They provide servers with nothing but a freshly installed Ubuntu OS. Regarding my experience, I recommend doing the following steps:
Look up the cuda and cuDNN version supported by the current Tensorflow release on the Tensorflow page.
Install the targeted cuda version from the deb package retrieved from Nvidias cuda page and be careful that more recent cuda versions might not work! This will automatically install the corresponding Nvidia drivers.
Install the targeted cuDNN version from this page and again be careful that a more recent cuDNN version might not work.
Install tensorflow-gpu using pip.
This should work. Your problem is probably that you are using a more recent cuda version than targeted by the current Tensorflow release.
To install tensorflow-gpu, the guidelines which are provided on official website are very tedious for beginers, instead we can do these simple steps:
Note : NVIDIA driver must be installed before this(you can verify this using command nvidia-smi).
Install Anaconda https://www.anaconda.com/distribution/?
Create an virtual environment using command "conda create -n envname"
Then activate env using command "conda activate envname"
Finally install tensorflow using command "conda install tensorflow-gpu"
With the given code
import tensorflow as tf
if tf.test.gpu_device_name():
print('Default GPU Device{}'.format(tf.test.gpu_device_name()))
else:
print("not using gpu")
You can find the tutorial on link given below
https://www.pugetsystems.com/labs/hpc/Install-TensorFlow-with-GPU-Support-the-Easy-Way-on-Ubuntu-18-04-without-installing-CUDA-1170/?
I would suggest to first check the availability of GPU using nvidia-smi command.
I had faced the same issue, i was able to resolve it by using docker container, you can install docker using Install Docker Engine on Ubuntu or use the Digital Ocean guide (i used this one) How To Install and Use Docker on Ubuntu 18.04
After that it is simple just run the following command based on the requirements
NV_GPU='0' nvidia-docker run --runtime=nvidia -it -v /path/to/folder:/path/to/folder/for/docker/container nvcr.io/nvidia/tensorflow:17.11
NV_GPU='0' nvidia-docker run --runtime=nvidia -it -v /storage/research/:/storage/research/ nvcr.io/nvidia/tensorflow:20.12-tf2-py3
Here '0' represents the GPU number, if you want to use more than one GPU just use '0,1,2' and so on ....
Hope this solves the issue.
I have been trying to install Tensorflow for a really long time now, but I never seem to make it work. I have tried to install Tensorflow via pip, virtual environment and anaconda so far. The installation process seem to run smoothly with all three methods. But as soon as I try to validate the installation by running "import tensorflow" I get the following error. I know it looks kind of chaotic, I wasn't sure how to pose the question.
By now, all help is appreciated
Thanks
As mentioned, you have to install python 3.5.X first
Secondly, I strongly recommend you to use anaconda. You should install anaconda 4.4.0 for python 3.6 version and 64-bit installer.
Then, you should run the following command
conda create -n tensorflow python=3.5
By the way, would you watch the tensorflow installation tutorial ?
TensorFlow versions 1.2 and later are compatible with Python 3.6. The error message points to the actual problem:
ImportError: libcudnn.so.5: cannot open shared object file: No such file or directory
This implies two things:
You have installed the tensorflow-gpu package, which requires a CUDA-capable GPU and a working installation of CUDA and cuDNN.
TensorFlow cannot find cuDNN.
This answer explains how to fix your cuDNN installation.
Update:
I get a similar error but the message in the bottom is now:
"ImportError libnvidia-fatbinaryloader.so.384.47: cannot open shared object file: No such file or directory"
Also when I enter "which nvcc" it returns /usr/bin/nvcc
Well, I am so tired of trying to install Tensorflow in Anaconda, I have installed anaconda successfully, but when I am going to install form this link, Anaconda installation on Step 4, I am seeing some exception.
I have executed this line of command
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0-cp35-cp35m-win_x86_64.whl
However it's terminating showing a red single line exception.
Later I have searched for some solution on here and there. I have found some solution too. In this solution is done for only MAC OX and Linux only, not for Windows. Later I found somewhere to use the python version 2.7 so I have downgraded the Python in lower version, however it was same. But this time I can able to to execute the MAC OX command successfully and in my prompt it shows that install successful. But when I had gone to import Tensorflow then again an error is threading, like this
I have also found the some other solutions link1 link2, however none of them are done in which process I'm approaching. I am in a quiet confusion what kind of error or exception it is? And how do I overcome it?
Update:
Tensorflow has been installed successfully from this link, Now a different problem is being facing. When I am going to import tensorflow in my workspace it providing this error.
It looks like it cannot find the required CUDA libraries. Did you install the CUDA Toolkit 8.0 and cuDNN 5.1?
You will have to install the CUDA Toolkit 8.0 e.g. from here and the cuDNN package from e.g. here and then put the cuDNN-installation-path/win10/bin on your path (depending on your windows version of course)
Also the following two posts might help you out:
On Windows, running "import tensorflow" generates No module named "_pywrap_tensorflow" error
TensorFlow on Windows: "Couldn't open CUDA library cudnn64_5.dll"