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CUDA_HOME environment variable is not set

I have a working environment for using pytorch deep learning with gpu, and i ran into a problem when i tried using mmcv.ops.point_sample, which returned :
ModuleNotFoundError: No module named 'mmcv._ext'
I have read that you should actually use mmcv-full to solve it, but i got another error when i tried to install it:
pip install mmcv-full
OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
Which seems logic enough since i never installed cuda on my ubuntu machine(i am not the administrator), but it still ran deep learning training fine on models i built myself, and i'm guessing the package came in with minimal code required for running cuda tensors operations.
So my main question is where is cuda installed when used through pytorch package, and can i use the same path as the environment variable for cuda_home?
Additionaly if anyone knows some nice sources for gaining insights on the internals of cuda with pytorch/tensorflow I'd like to take a look (I have been reading cudatoolkit documentation which is cool but this seems more targeted at c++ cuda developpers than the internal working between python and the library)

you can chek it and check the paths with these commands :
which nvidia-smi
which nvcc
cat /usr/local/cuda/version.txt

ImportError: Could not find 'cudart64_100.dll

I'm trying to install tensorflow-gpu==2.0.0-beta1 on my Windows 10 machine and got this error:
ImportError: Could not find 'cudart64_100.dll'. TensorFlow requires
that this DLL be installed in a directory that is named in your %PATH%
environment variable. Download and install CUDA 10.0 from this URL:
https://developer.nvidia.com/cuda-90-download-archive
I made all things from:
official documentation: https://www.tensorflow.org/install/gpu
from here: https://medium.com/#teavanist/install-tensorflow-gpu-on-windows-10-5a23c46bdbc7
Checked PATH variable: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
also have CUDA_PATH : C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0 in variables
file C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin\cudart64_100.dll exists
Did system restart
But error still occurs
How can I fix this?

The simplest way to fix is to install the latest ‘NVIDIA GPU Computing Toolkit’, because if it's not there, you'll be missing the 'cudart64_100.dll' library.
The only issue is that the latest copy of CUDA has this particular library upgraded to 'cudart64_101.dll', while the latest TensorFlow still requires the older 'cudart64_100.dll'.
Anyways, one way to deal with this issue is to install the latest CUDA + CUDA from September 2018 and then copy 'cudart64_100.dll' library from old install to the new one.
Or just visit my site where I linked the 'cudart64_100.dll' library downloaded from the CUDA Toolkit 10.0 (Sept 2018), to make it easier to copy it into the latest CUDA directory.
Here are some screenshots to illustrate the process: https://www.joe0.com/2019/10/19/how-resolve-tensorflow-2-0-error-could-not-load-dynamic-library-cudart64_100-dll-dlerror-cudart64_100-dll-not-found/

I have faced similar issue. I have added the directory of the cudart64_100.dll file to the PATH variable but still it prompts the error "cudart64_100.dll" not found. In the end I finally manage to make it work by adding the following codes. Hope it helps.
import ctypes
hllDll = ctypes.WinDLL("C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.0\\bin\\cudart64_100.dll")

I had similar error:
cudart64_101.dll not found
It is because the latest version of CUDA requires older CUDA-version.dll files to work properly. The solution would be try installing the previous version of CUDA.
Once you have downloaded CUDA 10.1 run the .exe file which will first extract the necessary files in C:\Users\your_user_name\AppData\Local\Temp\CUDA.
Once the extraction is completed do not proceed with the installation navigate to the directory C:\Users\your_user_name\AppData\Local\Temp\CUDA\cudart\bin and here you will find the missing DLL file cudart64_101.dll and cudart32_101.dll copy both the files to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin and then cancel the installation.
Follow the same steps for any CUDA version will work for sure. Hope this helps thank you!!!

I also was dealing with the current situation that tensorflow expects cudart64_101.dll and NVIDIA offers Version 10.2 as main version (including cudart64_102.dll).
I simply installed both versions. I have both versions in the windows path. Beside disc space, I did not have problems so far and GPU is used in tensorflow.

*EDIT
Every Tensorflow version requires a specific version of CUDA.
The easiest way is to open https://www.tensorflow.org/install/gpu and read which version did you need.
ex. CUDA® Toolkit —TensorFlow supports CUDA® 11.2 (TensorFlow >= 2.5.0)
If you want to install Tensor Flow v 2.5.0 you must have exactly CUDA v 11.2 installed.
TensorFlow 2.2 try to import cudart64_101.dll, cusparse64_10.dll and cublas64_10.dll but they are part from CUDA 10.1 If you have OTHER cuda version will get ImportError: Could not find 'cudart64_100.dll as this files are only available in cuda 10.1.
If you want to use older tensorflow version you have to use it with appropriate cuda version

Just rename
\program files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin\cudart64_102.dll
to cudart100.dll

you can find the cudart64_100.dll file in this website link.
and extract it, add cudart64_100.dll to your C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin
after you run your python script you will see:
Successfully opened dynamic library cudart64_100.dll

NVidia maintains a download archive of older CUDA driver releases. I would recommend downloading the installer from here instead of a third-party archive:
https://developer.nvidia.com/cuda-toolkit-archive
That page has a link to a page for downloading version 10.0:
https://developer.nvidia.com/cuda-10.0-download-archive
For the CUDA-NN libraries, the archive may be found here:
https://developer.nvidia.com/rdp/cudnn-archive
You need to be a member of the NVidia developer program to access this archive, but it is free to sign-up.
There should be no problem installing multiple versions at once, since the CUDA installer uses a separate installation directory for each version and the library files have different names. You will, however, want to select a custom installation to avoid replacing your display drivers, since you're installing older versions.
The CUDA-NN libraries (at least for Windows) are distributed as zip files. Copy the contents where you would like them (I installed each as a subdirectory under the corresponding CUDA driver installation directory).
Finally, add all of the various CUDA and CUDA-NN directories to your PATH, if they aren't already there so Python and Tensorflow can find them.

The actual fix is to download and install CUDA Toolkit
But if you're in hurry, you can also disable gpu, before importing tensorflow.
import os
# BAD IDEA ::::: Disabling gpu . The actual fix is to download and install cuda toolkit.
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf

Renaming the libraries might find a way to solve this issue, but that is not advised, as the root cause for this issue is due to version mismatch of CDNN and cuDNN. It's advised to install the right CDNN and cuDNN versions.
I too have faced similar issues and realized that the issue was with CUDA and CUDNN version mismatch.
Can refer here for the proper CDNN and cuDNN versions. From the reference below for TensorFlow 2.4.0 it is recommended to use CUDA 11.0 and cuDNN 8.0.
Or you can refer here to download cuDNN for suitable CUDA.

Installing Tensorflow and Keras on Intel Pentium

For a university we are supposed to implement a TensorFlow project using the python libraries for tensorflow and keras. I can install both of them just fine using pip3, but executing any piece of code results in some kind of error.
I've settled on testing the very complicated code:
import keras
Using python 3.6 and the newest tensorflow and keras (pip3 install tensorflow keras) I get the error ModuleNotFoundError: No module named 'tensorflow.python'; 'tensorflow' is not a package. I checked, and import tensorflow finds the package, but returns some error about AVX instructions and dumps the core.
I researched, and my CPU does not support AVX instructions which are part of tensorflow >= 1.6.0. I could not find a precompiled version that runs on my laptop without AVX, and I don't have the time to compile myself.
I tried downgrading to tensorflow == 1.5.0 and keras == 2.1.3 which was the version when tensorflow == 1.5.0 was around, but I still get missing errors, for each version and import statement a different one.
For example when I use the code:
import keras
from keras.datasets import mnist
I instead get the error AttributeError: module 'keras.utils' has no attribute 'Sequence'. I'm on an Intel Pentium, which I assume is the problem. I am fully aware that my setup is in no way suitable for machine learning, and it isn't supposed to be, but nevertheless I'd like to work on that assignment.
Anyone got experience with installing TensorFlow on older machines?
System:
Ubuntu 18.04.2 LTS
Intel(R) Pentium(R) 3556U # 1.70GHz (Dual Core)
4GB RAM

I had the same trouble, but it seems to have solved it. (However, the Python version shall be 3.5. )
For CPUs that do not support AVX, the tensorflow must be version 1.5 or lower.
If you want to install Tensorflow 1.5, the Python version must be 3.5 or lower.
The successful procedure is as follows.
(1) Uninstall your Anaconda.
(2) Download the following version of Anaconda from the following
URL. Version: Anaconda3-4.2.0-Windows-x86_64.exe
URL:https://repo.anaconda.com/archive/ or https://repo.anaconda.com/archive/Anaconda3-4.2.0-Windows-x86_64.exe
(3) Double-click the anaconda icon of “(2)” above, and install the
anaconda according to the GUI instructions.
(4) Start Anaconda Prompt
(5) Enter “pip install tensorflow==1.5” in Anaconda Prompt and press
the return key. Wait for the installation to finish. (See the log)
(6) Enter "pip install keras==2.2.4" in Anaconda Prompt and press the
return key. Wait for the installation to finish.(See the log)
This completes the installation. If you Enter " import tensorflow " on Jupiter notebook, some future error may displayed.(See this log.)
System:
My PC does not support AVX like your PC. My PC's specs are as follows.
PC:Surface Go
CPU:Intel(R) Pentium(R) CPU 4415Y　＠　1.60 GHz
Windows10:64bit
How to test ?
Enter and execute the following command on Jupiter Note. Or use this file.
import tensorflow as tf
print(tf.__version__)
print(tf.keras.__version__)
or
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
If your install is successful, then following message will be displayed on your Jupiter notebook
1.5.0
2.1.2-tf
P.S.
I'm not very good at English, so I'm sorry if I have some impolite or unclear expressions.

Sticking to the Pentium configuration is not recommended for default tensorflow builds because of AVX dependencies. Also many recent advances in this area are not available in earlier builds of TF and you will find it difficult to replicate research works. Options below:
Get a Google Colab (https://colab.research.google.com/) notebook, install Keras and TF and get going with your work
There have been genuine requests for this support, refer to this link [https://github.com/tensorflow/tensorflow/issues/18689] where unofficial builds are provided. See if one of them works
Build Tensorflow from scratch (very hard option), with the right set of flags for Bazel (remove all AVX/threading options)

The kernel appears to have died. It will restart automatically python 3

Whenever I try to import tensorflow in my windows machine, its saying that The kernel appears to have died. It will restart automatically and then its not even working.
The below is the following message given by the jupyter terminal.
Warning! HDF5 library version mismatched error
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.10.1, library is 1.10.2
what could solve this problem.
My version of Python is 3.6.3
and I updated the conda package also.
I have a Windows 10 Machine with 16GB RAM, so it cant be a memory issue also.
I was working with tensorflow previously, but now its not working.
This started to happen like 2 months back! when i was working on my university assignment this happened. The same code was working properly and I ran the code once again on the very same day jupyter notebook it crashed, since then I'm facing this problem.
I also tried to import tensorflow in the command prompt, its still showing the same error.
Has anyone encountered the same problem? What could be the fix?

Message says, Headers are 1.10.1, library is 1.10.2
You need to install 1.10.1 version
conda install -c anaconda hdf5=1.10.1

Python kernel dies when importing tensorflow 1.7

I want to use tensorflow insinde a Jupyter notebook. However, running
import tensorflow as tf
in a Jupyter notebook immediately triggers a pop-up:
The kernel appears to have died. It will restart automatically.
This issue only began after updating to tensorflow 1.7. I had not used tensorflow for a few weeks so it might also be due to an update to anaconda 5.1 with Python 3.6.
I use a Mid-2010 MacBookPro with "High Sierra 10.13.4". Removing and reinstalling anaconda 5.1 with Python 3.6, followed by installation of tensorflow (and not a single other library) via
pip3 install --upgrade tensorflow
did not resolve the issue. I do not use an isolated environment. The "anaconda3" folder is not in my home folder but directly in "Macintosh HD".
Before reinstalling anaconda, I removed it via these instructions
https://docs.anaconda.com/anaconda/install/uninstall. I also did not try to run tensorflow outside Jupyter, simply because I do not know how. But even if I did, I would still like to use Jupyter.

I'm also running a Mid-2010 MacbookPro and have been facing the same issue. It seems the only solution is to downgrade to Tensorflow 1.5. You can do so by running the following:
pip3 uninstall tensorflow
pip3 install tensorflow==1.5
Credit given to the solution to this post.

I was facing the same issue with Tensorflow 2 '2.0.0-beta1'. I found out that when you have multiple notebooks with Tensorflow running, this problem occurs. Also, simply closing the unused notebook windows wont work, they're still running in the background, you'll have to 'Shutdown' the notebooks
Here are the steps to shutdown a notebooks:
> Go to Home (of Jupyter notebook)
> Select 'Running' tab
> Select the unused notebooks
> Click 'Shutdown' button
You will notice in the Jupyter Home that the active notebook icon is green while inactive ones are gray

I have also faced a similar issue.
I was using python 3.7 and Tensorflow version 1.5
For we moving to Python 3.5 solve the issue.