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rebase to nvidia/cuda:10.1 as 10.2 makes problems, typo, no image in docker-compose required

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This commit is contained in:
Christoph Schranz 2020-02-23 12:00:57 +01:00
parent 5b54ba148c
commit f3306521c2
3 changed files with 3 additions and 4 deletions
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4
.build/Dockerfile
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@ -1,6 +1,6 @@
# Use NVIDIA CUDA as base image and run the same installation as in the other packages. # Use NVIDIA CUDA as base image and run the same installation as in the other packages.
# The version of cudatoolkit must match those of the base image, see Dockerfile.pytorch # The version of cudatoolkit must match those of the base image, see Dockerfile.pytorch
FROM nvidia/cuda:10.2-base-ubuntu18.04 FROM nvidia/cuda:10.1-base-ubuntu18.04
LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>" LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
############################################################################ ############################################################################
@ -380,7 +380,7 @@ RUN conda install --quiet --yes \
# Install PyTorch, version of cudatoolkit must match those of the base image # Install PyTorch, version of cudatoolkit must match those of the base image
RUN conda install -y -c pytorch \ RUN conda install -y -c pytorch \
cudatoolkit=10.2 \ cudatoolkit=10.1 \
'pytorch=1.3.1' \ 'pytorch=1.3.1' \
torchvision && \ torchvision && \
conda clean --all -f -y && \ conda clean --all -f -y && \

2
README.md
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@ -75,7 +75,7 @@ In order to stop the local deployment, run:
A Jupyter instance often requires data from other services. A Jupyter instance often requires data from other services.
If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons, If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons,
then connecting the data-source with *GPU-Jupyter* within a Docker Swarm is a great option! \ then connecting the data-source with *GPU-Jupyter* within a Docker Swarm is a great option!
### Set up Docker Swarm and Registry ### Set up Docker Swarm and Registry

1
docker-compose.yml
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@ -1,7 +1,6 @@
version: "3.4" version: "3.4"
services: services:
gpu-jupyter: gpu-jupyter:
image: 127.0.0.1:5001/gpu-jupyter
build: .build build: .build
ports: ports:
- ${JUPYTER_PORT}:8888 - ${JUPYTER_PORT}:8888