I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:150] kernel reported version is: 352.93 I tensorflow/core/common_runtime/gpu/gpu_init.cc:81] No GPU devices available on machine. tensorflow cannot access GP
Use cuda-dl-base as default base image for docker builds Ubuntu 24.04 as default remove all dependencies. Nsight, UCX are already present in base image Add option to pass in python-versions use ...
This is all the code you need to expose GPU drivers to Docker. In that Dockerfile we have imported the NVIDIA Container Toolkit image for 10.2 drivers and then we have specified a command to run when we run the container to check for the drivers. Now we build the image like so withdo...
Then I try to use ffmpeg with nvidia hardware decoder in docker. Here is the simplest dockerfile to build the ffmpeg with nvidia acceleration. Dockerfile is inspired by thisdocumentation FROMnvidia/cuda:11.8.0-devel-ubuntu22.04ENVTZ=Asia/ShanghaiENVDEBIAN_FRONTEND=noninteractiveRUNln -snf /usr/sh...
docker run --runtime=nvidia -it nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 And everything “just worked”. Although nvidia-smi in the container still reports driver 396.37 (this is expected), you can compile and run CUDA codes normally using the installed cuda 10.1 too...
I installed Jetpack 5.0.2 on Jetson Xavier NX developer kit but cuda is not detected in every library such as torch or opencv. (For example, torch.cuda.is_available() or cv2.cuda.getCudaEnabledDeviceCount() returns False and 0. For 2 weeks I th...
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root@debian:~# sudo systemctl restart docker Verify that the GPU is available in container. root@debian:~# docker run --rm --gpus all nvidia/cuda:11.4.0-basenvidia-smiMon Feb 20 10:26:17 2023 +---+ | NVIDIA-SMI 470.161.03 Driver Version: 470.161.03 CUDA Version: 11.4 | |---+...
You are probably using a workstation or cloud instance with Linux. The really interesting part is shown on the right side of the image: Containers with the nvidia-docker runtime. Those containers can use the GPU of the host system. You just need a CUDA enabled GPU and the drivers on...
com.nvidia.workbench.application.<application-name>.stop-command com.nvidia.workbench.application.jupyterlab.stop-command="jupyternotebookstop8888" com.nvidia.workbench.application.<application-name>.user-msg com.nvidia.workbench.application.jupyterlab.user-msg="Application{{.Name}}isrunningat{{.URL}}"...