System error is a common problem with various system today. In fact, it is normal to have system failure issues. The problem that results in these errors are numerous and that is why the solutions vary depending on the type of error as well. An understanding of the nature of the problem is important when solving the problem. Also, it is necessary to solve the problems to minimize complaints from customers.
Whenever the ImportError: DLL load failed: The specified module could not be found error occurs, you need to check your CUDA installation or perhaps check for bugs in the system. Most such error occurs as a result of the two problems. The key aspect is that Tensorflow 2.1.0 is designed to support CUDA 10.1. However, ImportError: DLL load failed: issues have become rampant. It arises due to system functionality problems. The error is destroying the market as clients keep on complaining about the error despite numerous solutions being offered to remedy the situation.
A bug can stress the ability of a system to function. Bug cleaning is crucial in preventing and managing the DLL load failed. New Tensorflow versions that should be compatible with the CUDA have been recommended. For instance, the 2.10rc2 version which is available in pipy can help solve the problem. Instructions about system paths have been provided for this version and numerous tries have been initiated in the market. However, it proved not helpful even though the manufacturers claimed the version is compatible with the CUDA the error has persisted and it is frustrating the users.
Most users are having the same issue recurring despite various methods of correcting the error being proposed. Nothing seems to work and everybody is giving up. In fact we have had cases where people have given up trying the suggested solutions and are resolved to wait for the next new version. Looking at other people’s comments on this, it seems to be working well for some. One of the users commented that he tried CUDA 10.1 and used CUDnn 7.6.5 and it worked. His system path indicated bin/,libnvvp/ and CUPTI\lib64. This path worked perfectly well for TensorFlow 2.0., and he was able to adapt the paths for the new CUDA version. Others have condemned the platform for failure and the effects it has had on their work.
Have you tried any of the recommended solutions? Did they work or disappoint? How long did the system error last for you? We are interested in your comments as it will provide as many details as possible to help us figure out why the issue is hard to correct. Maybe it is time to look at the problem from a different angle other than the CUDA installation or bug issue.
Also, we want you to know that we value your comments and are here to make your work easier with our system that is friendly and stronger. Share your comments and questions through our website and we will respond as soon as possible. Thank you for being a trusted client.