Merge branch 'master' into conda_outdated

base-notebook/Dockerfile

@@ -84,6 +84,7 @@ RUN cd /tmp && \
     conda config --system --prepend channels conda-forge && \
     conda config --system --set auto_update_conda false && \
     conda config --system --set show_channel_urls true && \
+    conda config --system --set channel_priority strict && \
     if [ ! $PYTHON_VERSION = 'default' ]; then conda install --yes python=$PYTHON_VERSION; fi && \
     conda list python | grep '^python ' | tr -s ' ' | cut -d '.' -f 1,2 | sed 's/$/.*/' >> $CONDA_DIR/conda-meta/pinned && \
     conda install --quiet --yes conda && \
@@ -126,9 +127,7 @@ ENTRYPOINT ["tini", "-g", "--"]
 CMD ["start-notebook.sh"]
 
 # Copy local files as late as possible to avoid cache busting
-COPY start.sh /usr/local/bin/
-COPY start-notebook.sh /usr/local/bin/
-COPY start-singleuser.sh /usr/local/bin/
+COPY start.sh start-notebook.sh start-singleuser.sh /usr/local/bin/
 COPY jupyter_notebook_config.py /etc/jupyter/
 
 # Fix permissions on /etc/jupyter as root

docs/using/selecting.md

@@ -160,4 +160,9 @@ The core stacks are just a tiny sample of what's possible when combining Jupyter
 
 * [sage-notebook](https://github.com/sharpTrick/sage-notebook) is a community Jupyter Docker Stack image with the [sagemath](https://sagemath.org) kernel on top of the minimal-notebook image. Click here to launch it on [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sharpTrick/sage-notebook/master).
 
+* [GPU-Jupyter](https://github.com/iot-salzburg/gpu-jupyter/): Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
+This is done by generating a Dockerfile, that consists of the **nvidia/cuda** base image, 
+the well-maintained **docker-stacks** that is integrated as submodule 
+and GPU-able libraries like **Tensorflow**, **Keras** and **PyTorch** on top of it.
+
 See the [contributing guide](../contributing/stacks.md) for information about how to create your own Jupyter Docker Stack.

r-notebook/Dockerfile

@@ -25,26 +25,25 @@ USER $NB_UID
 
 # R packages
 RUN conda install --quiet --yes \
-    'r-base=3.6.1' \
+    'r-base=3.6.2' \
     'r-caret=6.0*' \
     'r-crayon=1.3*' \
-    'r-devtools=2.0*' \
-    'r-forecast=8.7*' \
-    'r-hexbin=1.27*' \
-    'r-htmltools=0.3*' \
-    'r-htmlwidgets=1.3*' \
-    'r-irkernel=1.0*' \
+    'r-devtools=2.2*' \
+    'r-forecast=8.11*' \
+    'r-hexbin=1.28*' \
+    'r-htmltools=0.4*' \
+    'r-htmlwidgets=1.5*' \
+    'r-irkernel=1.1*' \
     'r-nycflights13=1.0*' \
     'r-plyr=1.8*' \
     'r-randomforest=4.6*' \
-    'r-rcurl=1.95*' \
+    'r-rcurl=1.98*' \
     'r-reshape2=1.4*' \
-    'r-rmarkdown=1.14*' \
+    'r-rmarkdown=2.1*' \
     'r-rodbc=1.3*' \
     'r-rsqlite=2.1*' \
-    'r-shiny=1.3*' \
-    'r-sparklyr=1.0*' \
-    'r-tidyverse=1.2*' \
+    'r-shiny=1.4*' \
+    'r-tidyverse=1.3*' \
     'unixodbc=2.3.*' \
     && \
     conda clean --all -f -y && \