Comments (1)
ChristophSchranz
commented on June 21, 2024
Hi @edurenye ,
there are reasons, why this isn't useful at the moment:
- the GPU stack requires a robust data-science stack, yes, 'scipy' and 'datascience' would fit here.
- the tensorflow installation of the docker-stacks is a one-liner and it is not planed to further improve it. It seems that this repository is an extension for GPU processing.
I think the best option is to fork this repository and modify the installations in generate_Dockerfile.sh and the Dockerfiles. In generate_Dockerfile.sh you can uncomment those installations you won't need, like the R and Julia installation in the 'datascience-notebook'.
I hope my answer helps you!
from gpu-jupyter.
Related Issues (20)
- provide token if no password is specified
- nvcc issue HOT 3
- 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
- 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
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