2020-01 tutorial updates (#451)

* tutorial setup text updates, Dockerfile updates

src/docs/sphinx/Tutorial.rst

@@ -47,7 +47,7 @@ Tutorial
 
 This tutorial introduces how to use Ascent, including basics about:
 
- - Formating mesh data for Ascent
+ - Formatting mesh data for Ascent
  - Using Conduit and Ascent's Conduit-based API
  - Using and combining Ascent's core building blocks: Scenes, Pipelines, Extracts, Queries, and Triggers
  - Using Ascent with the Cloverleaf3D example integration
@@ -59,10 +59,10 @@ You can find the tutorial source code and notebooks in your Ascent install direc
 `Ascent Tutorial  Intro Slides [pdf] <https://www.ascent-dav.org/2019_11_17_SC19_insitu_tutorial_ascent_intro_slides.pdf>`_
 
 Scheduled Tutorials:
- * `In Situ Analysis and Visualization with SENSEI and Ascent @ SC19 <https://sc19.supercomputing.org/presentation/?id=tut141&sess=sess199>`_ - Nov 2019, Denver, CO
- * ECP 2020 Annual Meeting - Feb 2020, Houston, TX
+  * ECP 2020 Annual Meeting - Feb 2020, Houston, TX
 
 Past Tutorials:
+ * `In Situ Analysis and Visualization with SENSEI and Ascent @ SC19 <https://sc19.supercomputing.org/presentation/?id=tut141&sess=sess199>`_ - Nov 2019, Denver, CO
  * ECP 2018 Annual Meeting - Feb 2018, Knoxville, TX
  * ECP 2019 Annual Meeting - Jan 2019, Houston, TX
 

src/docs/sphinx/Tutorial_Setup.rst

@@ -48,11 +48,11 @@ Tutorial Setup
 
 The tutorial examples are installed with Ascent to the subdirectory ``examples/ascent/tutorial/``.  Below are several options for using pre-built Ascent installs and links to info about building Ascent. If you have access to Docker, the easiest way to test the waters is via the ``alpinedav/ascent`` Docker image.
 
-SC19 Tutorial Cloud Option
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Tutorial Cloud Option
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-For SC19, we plan to have several instances of our Ascent Docker image up and running the jupyter notebook server.
-We will provide IP addresses and login info to attendees.
+For in person tutorials (at Supercomputing, the ECP Annual Meeting, etc), we provide HTTP access to several instances of our Ascent Docker image running the jupyter notebook server.
+We hand out IP addresses and login info to attendees during these events.
 
 Using Docker
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -69,7 +69,7 @@ After the download completes, create and run a container using this image:
 
 .. code::
 
-    docker run -p 8000:8000 run -p 8888:8888 -p 10000:10000 -t -i alpinedav/ascent
+    docker run -p 8000:8000 -p 8888:8888 -p 10000:10000 -t -i alpinedav/ascent
 
 (The ``-p`` is used to forward ports between the container and your host machine, we use these ports to allow web servers on the container to serve data to the host.)
 
@@ -92,7 +92,7 @@ To launch the a jupyter notebook server run:
 
     ./ascent_docker_run_jupyter.sh
 
-This will launch a notebook server on port 8888. Assuming you forwarded port 8888 from the Docker container to your host machine, you should be able to connect to the notebook server using http://localhost:8888. The current password for the notebook server is: ``ascentsc19``
+This will launch a notebook server on port 8888. Assuming you forwarded port 8888 from the Docker container to your host machine, you should be able to connect to the notebook server using http://localhost:8888. The current password for the notebook server is: ``learn``
 
 
 NERSC Cori Install
@@ -137,44 +137,44 @@ You can copy the tutorial examples from this install and use them as follows:
     cd python
     python ascent_first_light_example.py  
 
-
-SC19 Tutorial VM Option
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Ascent is also installed on the SC19 SENSEI + Ascent Example VM Image. The install is located at ``/home/in-situ-user/ascent/current``. You can use the tutorial examples as follows:
-
-.. code::
-
-    #
-    # source helper script with Ascent paths
-    #
-    source /home/in-situ-user/ascent/current/setup_ascent_env.sh
-
-    #
-    # build cpp examples and run the first one
-    #
-    cd /home/in-situ-user/ascent/current/ascent-install/examples/ascent/tutorial/ascent_intro/cpp
-    make
-    ./ascent_first_light_example
-
-    #
-    # run a python example
-    #
-    cd ..
-    cd python
-    python ascent_first_light_example.py  
-
-
-This install also includes jupyter, you can launch the notebook server with:
-
-.. code::
-
-   jupyter notebook
-
-
-The jupyter examples are at:
-
-`/home/in-situ-user/ascent/current/ascent-install/examples/ascent/tutorial/ascent_intro/`
+..
+.. SC19 Tutorial VM Option
+.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+..
+.. Ascent is also installed on the SC19 SENSEI + Ascent Example VM Image. The install is located at ``/home/in-situ-user/ascent/current``. You can use the tutorial examples as follows:
+..
+.. .. code::
+..
+..     #
+..     # source helper script with Ascent paths
+..     #
+..     source /home/in-situ-user/ascent/current/setup_ascent_env.sh
+..
+..     #
+..     # build cpp examples and run the first one
+..     #
+..     cd /home/in-situ-user/ascent/current/ascent-install/examples/ascent/tutorial/ascent_intro/cpp
+..     make
+..     ./ascent_first_light_example
+..
+..     #
+..     # run a python example
+..     #
+..     cd ..
+..     cd python
+..     python ascent_first_light_example.py
+..
+..
+.. This install also includes jupyter, you can launch the notebook server with:
+..
+.. .. code::
+..
+..    jupyter notebook
+..
+..
+.. The jupyter examples are at:
+..
+.. `/home/in-situ-user/ascent/current/ascent-install/examples/ascent/tutorial/ascent_intro/`
 
 
 Build and Install

src/examples/docker/ubuntu/Dockerfile

@@ -111,11 +111,11 @@ RUN cd /home/user/ascent && python scripts/uberenv/uberenv.py \
        --spec "%gcc+mpi+mfem ^[email protected]" \
        --spack-config-dir=scripts/uberenv/spack_configs/docker/ubuntu/
 
-# install jupyter, matplotlib, and bash_kernel via pip
+# install jupyter, matplotlib, etc via pip
 RUN /home/user//ascent/uberenv_libs/spack/opt/spack/*/*/python*/bin/pip \
     install \
     --trusted-host pypi.org --trusted-host files.pythonhosted.org \
-    jupyter matplotlib bash_kernel
+    jupyter matplotlib bash_kernel scipy scikit-learn torch
 
 # configure a debug build with cmake
 RUN cd /home/user/ascent && mkdir build-debug
@@ -126,7 +126,7 @@ RUN cd /home/user/ascent/build-debug && \
     -C ../uberenv_libs/*.cmake \
     ../src
 
-# build, test, and install conduit
+# build, test, and install ascent
 RUN cd /home/user/ascent/build-debug && make
 RUN cd /home/user/ascent/build-debug && env CTEST_OUTPUT_ON_FAILURE=1 make test
 RUN cd /home/user/ascent/build-debug && make install
@@ -152,7 +152,9 @@ RUN chmod 0600 ~/.ssh/authorized_keys
 
 # setup jupyter password
 RUN mkdir /home/user/.jupyter/
-RUN echo "c.NotebookApp.password = 'sha1:d8baf2aa447b:aa60264fe892fc31c80c4650d49b1adeaa09944e'" > /home/user/.jupyter/jupyter_notebook_config.py
+# the password is:
+#  learn
+RUN echo "c.NotebookApp.password = 'sha1:9777986fd066:283f673e1a311e2d5ef58c174eaebf3e1cb536dd'" > /home/user/.jupyter/jupyter_notebook_config.py
 
 # gen env script that points to spack installs of tpls
 RUN cd /home/user/ascent && python scripts/gen_spack_env_script.py cmake mpi python