Tensorflow is an open-source artificial intelligence library using data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. Failed to load the native TensorFlow runtime; the name of Tensorflow is derived from its core framework: the tensor.
All of the simulations at Tensorflow involve tensors. A tensor is an n-dimensional vector or matrix representing all data types. All values in a tensor hold a known (or partially known) shape of the same data type.
The data structure is an array dimension of space. It is a Python library that allows users to express arbitrary computation as a graph of data flows. TensorFlow is the best library since it is designed to be available to all; the Tensorflow library integrates numerous APIs to be applied to deep learning architecture, such as CNN or RNN, on a scale.
TensorFlow is based on graph computation as it allows the developer to visualize with Tensorboard the installation of the human brain. This method is useful in testing the software. It’s called Tensorflow because the input is taken as a multi-level set, also known as tensors.
Create a kind of operations process flow (called a graph) that you want to execute at that input. The input goes in at one end, flows through this various operation system, and comes out as output to the other.
When you start installation in Windows, or you want to launch and use an application, it can happen that the message “Failed to load the native TensorFlow runtime” is displayed on your screen. In such a case, finding the exact cause of the problem takes time and effort.
However, there are numerous solutions to such problems. Below we see some of the causes of the problem along with their solutions.
Problems and a guide on how to resolve Failed to load the native TensorFlow runtime
TensorFlow runtime due to missing library:
Libraries are generally very important when using software; a missing library can cause errors. An issue that could cause this error to occur could be a missing library. The missing library is “MSVCP140.dll”. However, there always exists a solution to the problem.
To overcome this problem, you can add the library to your system by downloading the MSVCP140.dll file and saving it to your system in the “C:\\Windows\System32 directory”.
A reason for the missing library could be that you accidentally deleted the file, and it should be in the recycle bin as long as you did not enter “Shift + Delete.” You can open the recycle bin on your computer and restore it after right-clicking on it.
However, you can update your windows if the problem has not been solved. Windows update provides smooth operation of the device.
To update the Windows and fix the error, you need to follow a couple of steps which include:
Once done, you can install all the updates and reboot the system. The error would have gone most likely.
Tensorflow runtime fixed by making Visual C++ Redistribution:
Another possible reason for this error can be caused by a missing Visual C++ Redistribution for the visual studio 2015 installation. For this, the solution is to download Visual C++ Redistribution. This can be downloaded from the Microsoft website.
These packages install the components of Visual C++ libraries on the computer, specifically for those computers that do not have Visual C++ installed in the system. These libraries are very necessary to run applications. Then install the “vc_redist.x64.exe”. Most probably, the error will no longer be there; if it is still there, you can follow the following way to overcome it.
Fix Tensorflow error by version downgrade:
Another possible way to fix the error is with the help of a version downgrade. The TensorFlow version is downgraded in this solution. To do some steps need to be followed. First, enter the command “pip3 install-upgradetensorflow==1.5.0” in your console.
Doing this will replace your current version with a downgraded version, and eventually, your problem will be solved. The tensor flow is applicable in many useful areas. It is used widely in many industries and sectors. It is a course for “Google Maps” and “Google Translators.” Moreover, projects involving artificial intelligence also use the TensorFlow library.
Conclusion
In this article, we have provided some of the causes and solutions to the problem of the TensorFlow error. We have listed three solutions to the problem. It is so that different computers have different requirements and solutions to those problems.
So, if one of the solutions does not work in your system, you can always try another one to overcome the problem. With the help of the above solutions given in simple steps, your issue will be resolved, and you can work peacefully.
We hope this article was helpful for you and was enough to solve your problems. However, we appreciate any input from your side and would like to know your comments. Our customers are very valuable to us, and we would surely like to hear from you.
