![]() Subsystem: Gigabyte Technology Co., Ltd GP106 įlags: bus master, fast devsel, latency 0, IRQ 100 Be sure to include the correct address lspci -s XX.XX.X -v: :~$ lspci | grep -i vgaĠ7:00.0 VGA compatible controller: NVIDIA Corporation GP106 (rev a1)Ġ7:00.0 VGA compatible controller: NVIDIA Corporation GP106 (rev a1) (prog-if 00 ) Take note, the lspci command contains a unique PCI bus address. Let’s determine the exact GPU installed, and check the kernel modules it’s using. When I ran DLC again, this time it loaded with the GUI.GPUs with a CUDA compute capability > 5.0 are recommended, but GPUs with less will still work.Īfterwards, make sure you have contrib & non-free* components are enabled in your network Repositories and that your system is fully up-to-date: :~$ grep "contrib non-free" /etc/apt/sources.listĭeb kali-rolling main contrib non-free non-free-firmware.This installation worked for me, but would not load the GUI.pip install -upgrade tensorflow=2.10.0.DLC might flag that you don’t have the latest version of conda.Follow DLC conda installation instructions.Everything from here is done in the Anaconda Prompt. In my experience, only uninstalling all previous CUDA versions allowed me to switch to the most recently installed version.In cmd, run nvidia-smi, and check that CUDA 11.8 is the version being used by the GPU.Place extracted files in C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8.When finished, download and extract cuDNN version 8.4.1 for CUDA 11.Do the express install, which will also install the appropriate GPU driver.Now install the version of CUDA recommended in the comments section.If you run nvidia-smi, in the cmd it should return an error.I also removed anything associated with nvidia.Uninstall any version of CUDA you have.Once removed, now install the latest version of Anaconda.There will be you have to find them and get rid of them. Also making sure that the installation procedure does not leave any remnants behind.This means removing any old versions from your computer.This doesn’t include many days of failed attempts… I hope this also works for you, good luck! So this is a little invovled, but here is everything I had to do to get this thing functional. Can I still follow the normal install instructions (git clone, conda create env), but somehow ensure that the new env gets a unique name (i.e. Ideally I’d like to set up a new conda environment for the latest DLC now to start fresh, and not delete/remove any old envs. The “tested build configurations” (for building from source) don’t show any CUDA greater than 11.2, even for TF2.11: Twórz ze źródła w systemie Windows | TensorFlowĬan I go ahead and install DLC or should I upgrade, or even downgrade? Recall I’m currently on CUDA 11.7 and GPU 516.94.įinally: There seems to be an DLC install on this computer. ’s install guide for Windows suggests an older CUDA, it seems:Ĭonda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin” The latest Tensorflow version seems to be 2.11 (but not Win10 compatible?)Īt, I see the following (scary) message: “TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. The latest CUDA version is 12.1 (I currently have 11.7) The latest driver for my GPU (RTX 2070 Super, Windows 10) is 528.49 (I currently have 516.94). All of the TensorFlow versions work with DeepLabCut.Install CUDA (versions up to CUDA11 are supported, together with TF2.5).Note, DeepLabCut is up to date with the latest CUDA and tensorflow versions!.Here are some of the relevant texts from the install page (which I find confusing/inconsistent): In short, what are the current recommendations for the GPU install? I’ve carefully read the installation instructions and tips and followed links to external (stackoverflow) threads, but I feel the install docs are not consistent. I recall from earlier installations that this is tricky business, and want to get it right and avoid a mess. Hi, I’m trying to get up and running with the latest DLC (use case is single subject) and am stuck on the GPU-related aspects. ![]()
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