Update module versions, X11 info, OpenMPI BTL

https://www.open-mpi.org/faq/?category=openfabrics#ib-btl
NOTE: Prior versions of Open MPI used an sm BTL for shared memory. sm was effectively replaced with vader starting in Open MPI v3.0.0.

Switching to "vader" BTL from "sm" prevents runtime error:
# --------------------------------------------------------------------------
# As of version 3.0.0, the "sm" BTL is no longer available in Open MPI.

# Efficient, high-speed same-node shared memory communication support in
# Open MPI is available in the "vader" BTL.  To use the vader BTL, you
# can re-run your job with:

#     mpirun --mca btl vader,self,... your_mpi_application
# --------------------------------------------------------------------------

Author: felker

docs/PrincetonUTutorial.md

@@ -1,11 +1,13 @@
 ## Tutorials
+*Last updated 2019-10-16*
 
 ### Login to TigerGPU
 
 First, login to TigerGPU cluster headnode via ssh:
 ```
 ssh -XC <yourusername>@tigergpu.princeton.edu
 ```
+Note, `-XC` is optional; it is only necessary if you are planning on performing remote visualization, e.g. the output `.png` files from the below [section](#Learning-curves-and-ROC-per-epoch). Trusted X11 forwarding can be used with `-Y` instead of `-X` and may prevent timeouts, but it disables X11 SECURITY extension controls. Compression `-C` reduces the bandwidth usage and may be useful on slow connections. 
 
 ### Sample usage on TigerGPU
 
@@ -15,21 +17,29 @@ git clone https://github.com/PPPLDeepLearning/plasma-python
 cd plasma-python
 ```
 
-After that, create an isolated Anaconda environment and load CUDA drivers:
+After that, create an isolated Anaconda environment and load CUDA drivers, an MPI compiler, and the HDF5 library:
 ```
 #cd plasma-python
-module load anaconda3/4.4.0
+module load anaconda3
 conda create --name my_env --file requirements-travis.txt
 source activate my_env
 
-export OMPI_MCA_btl="tcp,self,sm"
-module load cudatoolkit/8.0
-module load cudnn/cuda-8.0/6.0
-module load openmpi/cuda-8.0/intel-17.0/2.1.0/64
-module load intel/17.0/64/17.0.5.239
+export OMPI_MCA_btl="tcp,self,vader"
+# replace "vader" with "sm" for OpenMPI versions prior to 3.0.0
+module load cudatoolkit cudann 
+module load openmpi/cuda-8.0/intel-17.0/3.0.0/64
+module load intel
+module load hdf5/intel-17.0/intel-mpi/1.10.0
+```
+As of the latest update of this document, the above modules correspond to the following versions on the TigerGPU system, given by `module list`:
+```
+Currently Loaded Modulefiles:
+  1) anaconda3/2019.3                       4) openmpi/cuda-8.0/intel-17.0/3.0.0/64   7) hdf5/intel-17.0/intel-mpi/1.10.0
+  2) cudatoolkit/10.1                       5) intel-mkl/2019.3/3/64
+  3) cudnn/cuda-9.2/7.6.3                   6) intel/19.0/64/19.0.3.199
 ```
 
-and install the `plasma-python` package:
+Next, install the `plasma-python` package:
 
 ```bash
 #source activate my_env
@@ -44,7 +54,7 @@ Common issue is Intel compiler mismatch in the `PATH` and what you use in the mo
 you should see something like this:
 ```
 $ which mpicc
-/usr/local/openmpi/cuda-8.0/2.1.0/intel170/x86_64/bin/mpicc
+/usr/local/openmpi/cuda-8.0/3.0.0/intel170/x86_64/bin/mpicc
 ```
 
 If you source activate the Anaconda environment after loading the openmpi, you would pick the MPI from Anaconda, which is not good and could lead to errors. 
@@ -93,20 +103,20 @@ For batch analysis, make sure to allocate 1 MPI process per GPU. Save the follow
 #SBATCH -c 4
 #SBATCH --mem-per-cpu=0
 
-module load anaconda3/4.4.0
+module load anaconda3
 source activate my_env
-export OMPI_MCA_btl="tcp,self,sm"
-module load cudatoolkit/8.0
-module load cudnn/cuda-8.0/6.0
-module load openmpi/cuda-8.0/intel-17.0/2.1.0/64
-module load intel/17.0/64/17.0.4.196
+export OMPI_MCA_btl="tcp,self,vader"
+module load cudatoolkit cudann 
+module load openmpi/cuda-8.0/intel-17.0/3.0.0/64
+module load intel
+module load hdf5/intel-17.0/intel-mpi/1.10.0
 
 srun python mpi_learn.py
 
 ```
 where `X` is the number of nodes for distibuted training.
 
-Submit the job with:
+Submit the job with (assuming you are still in the `examples/` subdirectory):
 ```bash
 #cd examples
 sbatch slurm.cmd
@@ -131,7 +141,7 @@ where the number of GPUs is X * 4.
 Then launch the application from the command line:
 
 ```bash
-mpirun -npernode 4 python examples/mpi_learn.py
+mpirun -npernode 4 python mpi_learn.py
 ```
 
 ### Understanding the data
@@ -205,7 +215,7 @@ python -m tensorflow.tensorboard --logdir /mnt/<destination folder name on your
 ```
 You should see something like:
 
-![alt text](https://github.com/PPPLDeepLearning/plasma-python/blob/master/docs/tb.png)
+![tensorboard example](https://github.com/PPPLDeepLearning/plasma-python/blob/master/docs/tb.png)
 
 #### Learning curves and ROC per epoch