VidCutter is lightweight, using minimal system resources, allowing you to edit videos on even low-spec machines.VidCutter is fast and stable, so you can edit your videos without experiencing any lag or freezing.VidCutter is easy to install and use, making it an ideal video editing tool for beginners and professionals.Here are some of the unique benefits of VidCutter against its competitors: Method 2 Install VidCutter with Snapcraft.You only need to uninstall the latest version and install the old version, which can fix them.įinally, you can run the python file in this environment without any problems.
Most reasons of errors are the versions of packages confliction. There might have some other error messages but not for cuda or cudnn, you can figure them out by google the errors. Install the packages together can reduce the conflicts. Then, you can install other packages to complete the settings of virtual environment by pip install. If there is no other errors and just warnings or no message except the version of tensorflow. You can also test whether tensorflow working by python You can check the versions of packages in your environment by the codes below. There is no need to install cuda or cudnn, because both of them are installed automatically by installing tensorflow-gpu. The specific versions of cuda and cudnn to match tensorflow-gpu=1.12 are cuda=9.0 and cudnn=7.6.5 conda create -n YourEnvironmentName python=3.6 My current system: Ubuntu 20.04, nvidia-smi: driver 470.130.01, cuda 11.4 (Tips: this version is just the latest version of cuda you can install, not the cuda has already been installed) Here are my steps to install tensorflow-gpu by miniconda, while other packages can be installed by pip in the virtual environment. It is not necessary to download cuda and cudnn from Nvidia website. I have the same question with you and it took me many months to figure it out.Īfter attempting many methods, I would recommend using virtual environment to install cuda and cudnn.
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