This article record some key procedures for me to compileTensorFlow-GPU on Linux (WSL2) and on Windows. Because of the convenience ofMiniConda, we can abstract the compiling process into a number of steps that
For local run on Windows + WSL, WSL Ubuntu distro 18.4 or greater should be installed and is set to default prior to using AI Toolkit.Learn more how to install Windows subsystem for Linuxandchanging default distributionor I have explained it step-wise in one of the previous blog...
When it goes to the NVIDIA website to download the installer, there isn’t one for Windows 11, just Windows 10 and some server versions. How do I install CUDA 11.0 on Windows 11? Please note - I am not trying to install it in WSL2 - I’m trying to install CUDA in Wi...
02. Install the NVIDIA CUDA Driver, Toolkit, cuDNN, and TensorRT03. Install the Jupyter Notebook Server04. Install Virtual Environments in Jupyter Notebook05. Install the Python Environment for AI and Machine LearningWSL2:01. Install Windows Subsystem for Linux 202. Install ...
motionblur-1 | CUDA Toolkit: 11.5, Driver: 12.2 motionblur-1 | Devices: motionblur-1 | "cpu" | x86_64 motionblur-1 | "cuda:0" | NVIDIA GeForce RTX 4090 (sm_89) motionblur-1 | Kernel cache: /root/.cache/warp/1.0.0-beta.2 ...
export CUDA_VERSION=118 Using CUDA 12.1 here in WSL2, and issuing pip install .[triton] I am able to compile the CUDA extension as well as use Triton and get ~2.5 t/s on my 3090 with TheBloke/WizardLM-Uncensored-Falcon-40B model. Otherwise, I can barely scrape 1 t/s after a ...
02.install the nvidia cuda driver, toolkit, cudnn, and tensorrt 03.install the jupyter notebook server 04.install virtual environments in jupyter notebook 05.install the python environment for ai and machine learning 06.install the fastai course requirements# wsl 2 ...
In order to start using GPTQ models with langchain, there are a few important steps: Set up Python Environment Install the right versions of Pytorch and CUDA toolkit Correctly set upquant_cuda Download the GPTQ models from HuggingFace
On my notebook (Windows 10, 64 bits) with the latest version of VisualStudio 2022 and NVIDA drivers (CUDA 11.8 Runtime - VS22 template), I installed the one API Base Toolkit which is now part of the VS22.I am writing and running CUDA programs in VS22 (CUDA 11.8 ...
Unified Memoryand NVLink represent a powerful combination for CUDA® programmers. Unified Memory provides you with a single pointer to data and automatic migration of that data between the CPU and GPU. With 80 GB/s or higher bandwidth on machines with NVLink-connected CPUs and GPUs, that mea...