Revert header to nvidia/cuda:10.1-cudnn7-runtime for tensorflow incom.

.build/Dockerfile

@@ -7,8 +7,8 @@
 
 # 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
-FROM nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04
-LABEL authors="Christoph Schranz <[email protected]>, Mathematical Michael <[email protected]>"
+ FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+ LABEL authors="Christoph Schranz <[email protected]>, Mathematical Michael <[email protected]>"
 # This is a concatenated Dockerfile, the maintainers of subsequent sections may vary.
 RUN chmod 1777 /tmp && chmod 1777 /var/tmp
 
@@ -398,22 +398,21 @@ WORKDIR $HOME
 
 LABEL maintainer="Christoph Schranz <[email protected]>"
 
-# Install Tensorflow, check compatibility here: https://www.tensorflow.org/install/gpu
+# Install Tensorflow, check compatibility here:
+# https://www.tensorflow.org/install/source#gpu
 # installation via conda leads to errors in version 4.8.2
 RUN pip install --upgrade pip && \
     pip install --no-cache-dir "tensorflow==2.3.2" && \
     pip install --no-cache-dir keras
 
-# Update cudatoolkit and install PyTorch with dependencies
+# Install PyTorch with dependencies
 RUN conda install --quiet --yes \
     pyyaml mkl mkl-include setuptools cmake cffi typing
-# RUN conda install --quiet --yes \
-#    cudatoolkit=10.1 -c pytorch
 
-# Check compatibility here: https://pytorch.org/get-started/locally/
-# Installation via conda leads to errors installing cudatoolkit=10.2
-RUN conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch
-# RUN pip install torch torchvision torchaudio torchviz
+# Check compatibility here:
+# https://pytorch.org/get-started/locally/
+# Installation via conda leads to errors installing cudatoolkit=10.1
+RUN pip install torch torchvision torchaudio torchviz
 
 # Clean installation
 RUN conda clean --all -f -y && \
@@ -436,6 +435,48 @@ RUN git clone https://github.com/Syllo/nvtop.git /run/nvtop && \
 
 RUN fix-permissions /home/$NB_USER
 
+USER $NB_UID
+
+  ############################################################################
+  ############################ Useful packages ###############################
+  ############################################################################
+
+LABEL authors="Christoph Schranz <[email protected]>, Mathematical Michael <[email protected]>"
+
+USER root
+
+RUN pip install --no-cache-dir ipyleaflet "plotly>=4.14.3" "ipywidgets>=7.5"
+
+# Install important packages and Graphviz
+RUN set -ex \
+ && buildDeps=' \
+    graphviz==0.11 \
+' \
+ && apt-get update \
+ && apt-get -y install htop apt-utils iputils-ping graphviz libgraphviz-dev openssh-client \
+ && pip install --no-cache-dir $buildDeps
+
+# Install various extensions
+RUN fix-permissions $CONDA_DIR
+# jupyterlab/github Does not support jlab 3.x yet, install well maintained alternative instead.
+# RUN jupyter labextension install @jupyterlab/github
+RUN pip install jupyterlab-git
+RUN pip install jupyterlab-drawio
+RUN jupyter nbextension enable --py --sys-prefix ipyleaflet
+RUN jupyter labextension install jupyterlab-plotly
+RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager plotlywidget
+# RUN pip install --no-cache-dir jupyter-tabnine  --user && \
+#   jupyter nbextension install --py jupyter_tabnine --user && \
+#   jupyter nbextension enable --py jupyter_tabnine --user && \
+#   jupyter serverextension enable --py jupyter_tabnine --user
+RUN pip install --no-cache-dir jupyter_contrib_nbextensions \
+  jupyter_nbextensions_configurator rise
+#  jupyter nbextension enable codefolding/main
+RUN jupyter labextension install @ijmbarr/jupyterlab_spellchecker
+
+RUN fix-permissions /home/$NB_USER
+
+# Switch back to jovyan to avoid accidental container runs as root
 USER $NB_UID
 
 # Copy jupyter_notebook_config.json

README.md

@@ -26,12 +26,12 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
  and [Docker Compose](https://docs.docker.com/compose/install/) version **1.28.0+**.
 3.  Get access to your GPU via CUDA drivers within Docker containers.
     You can be sure that you can access your GPU within Docker, 
-    if the command `docker run --gpus all nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04 nvidia-smi`
+    if the command `docker run --gpus all nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 nvidia-smi`
     returns a result similar to this one:
     ```bash
     Fri Feb 26 12:45:19 2021       
     +-----------------------------------------------------------------------------+
-    | NVIDIA-SMI 460.39       Driver Version: 460.39       CUDA Version: 10.2     |
+    | NVIDIA-SMI 460.39       Driver Version: 460.39       CUDA Version: 10.1     |
     |-------------------------------+----------------------+----------------------+
     | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
     | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
@@ -54,20 +54,31 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
     The CUDA toolkit is not required on the host system, as it will be 
     installed within the Docker containers using [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker).
     It is also important to keep your installed CUDA version in mind, when you pull images. 
-    **You can't run images based on `nvidia/cuda:11.2` if you have only CUDA version 10.2 installed.**
+    **You can't run images based on `nvidia/cuda:11.2` if you have only CUDA version 10.1 installed.**
     Check your host's CUDA-version with `nvcc --version` and update to at least 
     the same version you want to pull.
     
 4. Pull and run the image. This can last some hours, as a whole data-science 
     environment will be downloaded:
    ```bash
    cd your-working-directory 
-   docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root cschranz/gpu-jupyter:v1.3_cuda-10.2_ubuntu-18.04_python-only
+   docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root cschranz/gpu-jupyter:v1.3_cuda-10.1_ubuntu-18.04_python-only
    ```
-   This starts an instance with of *GPU-Jupyter* the tag `v1.3_cuda-10.2_ubuntu-18.04_python-only` at [http://localhost:8848](http://localhost:8848) (port `8484`).
+   This starts an instance with of *GPU-Jupyter* the tag `v1.3_cuda-10.1_ubuntu-18.04_python-only` at [http://localhost:8848](http://localhost:8848) (port `8484`).
    The default password is `gpu-jupyter` (previously `asdf`) which should be changed as described [below](#set-password). 
    Furthermore, data within the host's `data` directory is shared with the container.
-   Other versions of GPU-Jupyter are available and listed on Dockerhub under  [Tags](https://hub.docker.com/r/cschranz/gpu-jupyter/tags?page=1&ordering=last_updated).
+   The following images of GPU-Jupyter are available on Dockerhub:
+     - `v1.3_cuda-10.1_ubuntu-18.04` (full image)
+     - `v1.3_cuda-10.1_ubuntu-18.04_python-only` (only with a python interpreter and without Julia and R)
+     - `v1.3_cuda-10.1_ubuntu-18.04_python-only` (only with a python interpreter and without additional packages)
+     - `v1.3_cuda-11.0_ubuntu-18.04` (full image)
+     - `v1.3_cuda-11.0_ubuntu-18.04_python-only` (only with a python interpreter and without Julia and R)
+     - `v1.3_cuda-11.0_ubuntu-18.04_python-only` (only with a python interpreter and without additional packages)
+
+    The version, e.g. `v1.3`, specifies a certain commit of the underlying docker-stacks.
+   The Cuda version, e.g. `cuda-10.1`, has to match the host's driver version 
+   and must be supported by the gpu-libraries. 
+   These and older versions of GPU-Jupyter are listed on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter/tags?page=1&ordering=last_updated).
 
    
 Within the Jupyterlab instance, you can check if you can access your GPU by opening a new terminal window and running
@@ -81,7 +92,7 @@ If you want to learn more about Jupyterlab, check out this [tutorial](https://ww
 First, it is necessary to generate the `Dockerfile` in `.build`, that is based on 
 the NIVIDA base image and the [docker-stacks](https://github.com/jupyter/docker-stacks).
 As soon as you have access to your GPU within Docker containers 
-(make sure the command `docker run --gpus all nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04 nvidia-smi` 
+(make sure the command `docker run --gpus all nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 nvidia-smi` 
 shows your GPU statistics), you can generate the Dockerfile, build and run it.
 The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) 
 with the default password `gpu-jupyter` (previously `asdf`).

build_push_all.sh

@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 cd $(cd -P -- "$(dirname -- "$0")" && pwd -P)
 
-export TAGNAME="v1.3_cuda-10.2_ubuntu-18.04"
+export TAGNAME="v1.3_cuda-10.1_ubuntu-18.04"
 
 
 ###################### build, run and push full image ##########################

src/Dockerfile.gpulibs

@@ -1,6 +1,7 @@
 LABEL maintainer="Christoph Schranz <[email protected]>"
 
-# Install Tensorflow, check compatibility here: https://www.tensorflow.org/install/gpu
+# Install Tensorflow, check compatibility here:
+# https://www.tensorflow.org/install/source#gpu
 # installation via conda leads to errors in version 4.8.2
 RUN pip install --upgrade pip && \
     pip install --no-cache-dir "tensorflow==2.3.2" && \
@@ -10,10 +11,10 @@ RUN pip install --upgrade pip && \
 RUN conda install --quiet --yes \
     pyyaml mkl mkl-include setuptools cmake cffi typing
 
-# Check compatibility here: https://pytorch.org/get-started/locally/
-# Installation via conda leads to errors installing cudatoolkit=10.2
-RUN conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch
-# RUN pip install torch torchvision torchaudio torchviz
+# Check compatibility here:
+# https://pytorch.org/get-started/locally/
+# Installation via conda leads to errors installing cudatoolkit=10.1
+RUN pip install torch torchvision torchaudio torchviz
 
 # Clean installation
 RUN conda clean --all -f -y && \

src/Dockerfile.header

@@ -1,6 +1,6 @@
 # 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
-FROM nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04
-LABEL authors="Christoph Schranz <[email protected]>, Mathematical Michael <[email protected]>"
+ FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+ LABEL authors="Christoph Schranz <[email protected]>, Mathematical Michael <[email protected]>"
 # This is a concatenated Dockerfile, the maintainers of subsequent sections may vary.
 RUN chmod 1777 /tmp && chmod 1777 /var/tmp