Comments (4)
Thanks! I will have a look at it when next time working on infra.
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Hi @fzyzcjy ,
the suggested way to update a package within the container is pip install [package]==[version]
.
The flag -U
will upgrade the package to the latest version that might not support the current CUDA version pip flag -U.
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Thank you! Then another question is, how should I upgrade CUDA - should I wait for the image or there are other methods?
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@fzyzcjy You could use my/b-data's GPU accelerated Python images:
glcr.b-data.ch/jupyterlab/cuda/python/scipy:3.12
- derived from
nvidia/cuda:12.4.1-devel-ubuntu22.04
- including a custom cuDNN 8 installation
Install TensorFlow: pip install tensorflow
Install PyTorch: pip install torch torchvision torchaudio
What is different about my/b-data's images:
- Multi-arch:
linux/amd64
,linux/arm64/v8
- Derived from
nvidia/cuda:12.4.1-devel-ubuntu22.04
- including development libraries and headers
- TensortRT and TensorRT plugin libraries
- including development libraries and headers
- IDE: code-server next to JupyterLab
- Just Python – no Conda / Mamba
There are also Python 3.11 images, which are still derived from nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
:
glcr.b-data.ch/jupyterlab/cuda/python/scipy:3.11
- derived from
nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
Install TensorFlow: pip install tensorflow==2.14.1
Install PyTorch: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Cross reference:
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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
- PyTorch 2 needs CUDA 11.7+ HOT 5
- 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
- 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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