Comments (5)
Hi,
new tags are available such as v1.5_cuda-12.0_ubuntu-22.04_python-only
and v1.5_cuda-11.8_ubuntu-22.04_python-only
which are based on 12.0.1-cudnn8-devel-ubuntu22.04 rsp. 11.8.0-cudnn8-devel-ubuntu22.04. Both are built with with PyTorch 2 and the latest compatible Tensorflow version.
Thiss issue should be solved with PR #124
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Just to be clear, thinking that PyTorch2 is installed in the container was a mistake of mine.
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hm. i've been using torch 2 since release and don't recall rebuilding my images. are you able to just pip install it?
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The installation is ultimately unsuccessful because of the CUDA dependency. Of course, this only matters if using on GPU.
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@hlapp You may be interested in b-data's/my GPU accelerated JupyterLab docker stacks.
(currently) Based on nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04; including code-server – aka VS Code in the browser.
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Related Issues (20)
- Files are displayed empty after restart of the container HOT 1
- no 'latest' tag on docker hub HOT 1
- image build fails on Debian 11 when trying follow instructions to build for nvidia/cuda:12.1.0-base-ubuntu20.04 HOT 3
- Error when running your current image with host drivers cuda 12.1 : "Could not load dynamic library 'libnvinfer.so.7'" HOT 3
- jupyternbextension-not found HOT 9
- How to add new packages into the image ? HOT 2
- Unable to change conda environment in kernel HOT 4
- Update CUDA to 11.8 HOT 17
- CUDA version incompatibility HOT 10
- TensorFlow throws missing libdevice errors
- Update to Jupyterlab 4.0.10 HOT 6
- Static Token HOT 1
- torch gpu problem HOT 3
- Upgrade to latest versions (e.g. CUDA 12.3) HOT 3
- Suggest way to use latest pytorch (2.2.2) HOT 4
- Container not accessible from the network with Podman instead of Docker HOT 1
- Use other servers like Jupyverse HOT 1
- tensorflow couldn't use nvidia GPU in v1.7_cuda-12.3_ubuntu-22.04 HOT 4
- CUDA 12.1 support? HOT 3
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