Getting the GPU working with TF is one small step - if you do not batch your input pipeline properly, your computer will run slower with ML training running on GPU. 19, 2022 - NVIDIA announces the newest release of the CUDA development environment, CUDA 11.6. Now you can print(tf.config.list_physical_devices(‘GPU’)) and hopefully you see device:0 (i.e., tf using the GPU) ![]() If not, then once you create a new project, open venv terminal and follow step 7 above. MAKE SURE THAT YOU SELECT / CHOOSE ‘INHERIT GLOBAL PACKAGES’ checkbox. If step 7 did not work, install tensorflow 2.10 globally (i.e., in cmd terminal) and then create a fresh new project. this gets complicated if you are using Jupiter/no IDE - search in google how to create/work with venv in this case. Install tensorflow 2.10 in the ‘venv’ of your project, e.g., open your project in pycharm/VSC, go to terminal and type pip3 install tensorflow=2.10. Go to environment variables and add your CUDA folder paths to ‘Path’ (in both sections):Ĭ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\圆4Ĭ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\includeĬ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\binĬ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvpĪlso, ensure “CUDA_PATH” and “CUDA_PATH_V11_2” variables are automatically added to System Variables. Install python 3.10 version as tf 2.10 is not supported in later versions of pythonĭownload CUDA 11.2 for Windows, and install it (cuda_11.2.0_460.89_win10)ĭownload cuDNN 8.1 for CUDA 11.2 (cudnn-11.2-windows-圆4-v8.1.1.33)Įxtract cuDNN, copy all files from the “lib, include and bin” folders from the extracted cuDNN folder to the respective folders (with the same name) in the CUDA directory (in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2) This version is hard to find, but, without this, Cuda does not work, good luck.ĢB. You can install other workloads, but they are not mandatory. Install Visual Studio 2017 community (Mandatory do not install later versions as they are incompatible with TF2.10 and CUDA 11.2) with the “Desktop Development with C++” workload. Restart the computer and install the latest compatible NVIDIA drivers from the official website.ĢA. Uninstall every NVIDIA driver, including CUDA (This will cause issues, but they are temporary.) CUDA Toolkit and Corresponding Driver Versions Toolkit Driver Version CUDA Toolkit Linux x8664 Driver Version Windows x8664 Driver Version CUDA 11.7 GA >515.43.04 >516.01 CUDA 11.6 Update 2 >510.47.03 >511. However, getting this working is not an easy job, apparently, there is a sequence that you should follow. The version of the development NVIDIA GPU Driver packaged in each CUDA Toolkit release is shown below. The config I suggested in my post should work, even with older GPUs (my laptop has GEFORCE 940MX, which is not listed even under NVIDIA-CUDA-supported devices).
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