I’m trying to enhance my deep model training build using tensorflow by utilizing A-100 GPU installed on kuberenetes server. I was trying to use nvidia/cuda images from dockerhub with installing tensorflow and other packages on top of it. cuda version: 11.03 cuDNN version: 220.127.116.11 tensorflow: 2.4.1 After building and deploying the docker image when I ..
I am using docker to run pytorch or tensorflow model with dependency of CUDA10.1. But if there is no CUDA in the docker image, the container will use CUDA 11 in my local system, which makes some errors. So I have this question: Is it possible, that the docker use only CUDA in the image? ..
I’m trying to run a GPU docker image with TF 2.4.1 and CUDA 11.0 while the host has CUDA 11.3. The GPU on the host is RTX 3070 and I’ve read that support for NVIDIA GeForce RTX 30 Series started with CUDA 11.1. When I try to do the training on the docker image tensorflow ..
I have a requirment.yml : name: ocrvn ` channels: pytorch conda-forge defaults dependencies: python=3.8.8 pip=21.0.1 numpy==1.20.0 conda-forge:nodejs=14.15.4 conda-forge:jupyterlab=3.0.9 conda-forge:pycocotools=2.0.2 conda-forge:cudatoolkit-dev==11.0.3 pytorch:pytorch=1.7.1 pytorch:torchaudio=0.7.2 pytorch:torchvision=0.8.2 scipy=1.6.2 onnx=1.8.1 pandas=1.2.3 flask fastapi uvicorn python-multipart gunicorn==20.0.4 gevent==20.9.0 ` Have Exception when install ‘cudatoolkit-dev==11.0.3’. it notified : enter image description here Source: Docker..
I am using repo2docker to build a Docker image of a GitHub repo. The resulting image needs to include CUDA-CNN so that containers can use GPUs on a host if they are available. As I understand it, I basically need to use nvidia/cuda as a base image. However, there is an open bug in repo2docker ..
How do I get three docker containers to talk to one another 1) a cuda container 2) a Tensorflow container and 3) a Python container? so I can run multiple versions of all three in various combinations. Source: Docker..
Windows-WSLg-Ubuntu-DOCKER(maybe DooD))-NVIDIADRIVER(version x,y,z) -CUDA(ver(a,b,c))-Python(ver(r,s,t))-Tensorflow(ver(l,m,n)) working together Hi! I am running a windows 10 (developer version) latest with a Ubuntu guest and with docker. I can successfully get a container to see the nvidia card and process AI software. However the solution is a pain because of versional conflicts between NVIDIA versions CUDA VERSIONS Python VERSIONS ..
I am trying to access my host GPUs in a docker container. However, while running a basic check to see if docker container can access the GPU, it keeps failing with the below error: Dockerfile # FROM nvidia/cuda:10.2-base FROM nvidia/cuda:11.2.0-cudnn8-runtime-ubi7 CMD ["nvidia-smi"] # docker build -f Dockerfile_NVIDIA –tag nvidia-test . # docker run –gpus=all nvidia-test ..
I’m trying to set up a docker container from a windows-based image derived from FROM mcr.microsoft.com/dotnet/framework/sdk:4.8-windowsservercore-ltsc2019. I want to install cuda toolkit inside it, but don’t really know how to do it from the command line. My best and only attempt has been to install with RUN choco install cuda –version=11.0.2 -y… But this was ..
I’m trying to use distroless image for project with CUDA. I’m building a multi-stage dockerfile, when stage 1 have all needed for development, and stage 2 is based on gcr.io/distroless/cc-debian10:debug and copy from stage 1 only what needed for runtime. When I run the image (based on stage 2) I get this error: CUDA error: ..