How to specify path to jupyter kernels when in different directory than jupyter itself

Cross-posted [here](http://stackoverflow.com/questions/43754834/how-to-specify-path-to-jupyter-kernels-when-in-different-directory-than-jupyter).

We are running jupyter hub installed locally on a server serverA. This server has web access. We would like the notebooks to use kernels located in a conda environment on a different server, serverB, that does not have web accesss. However, it is where we keep our code base.

The conda path on serverB with the kernels is mounted as an NFS share on serverA.

While we keep our code on serverB, we would like to execute the actual computation on serverA, where jupyter is installed.

browser
/\
||
|| web access
||
\/
serverA | serverB
-------------------------------------------------------------------
|
opt/conda/bin/jupyter |
/\ |
|| |
computation || |
|| |
\/ |
<------------------ .../conda/bin/
NFS Mount
|
|
-------------------------------------------------------------------

Our sysadmins have determined that it would be unstable to run the jupyter web service over a mapped drive. Therefore we need to run the jupyter service on serverA but use the kernels on serverB.

**EDIT**
There are very good instructions for accessing a kernel over ssh. We could use this approach, but this seems unnecessary in our case because the drive to the executables is actually mapped.

Start with the serverA/serverB topology in the issue and review the linked instructions for accessing a kernel over SSH. Determine whether the NFS-mounted kernels can be supported while Jupyter runs on serverA, then document the supported configuration and required setup if one exists.

Written by the indexing model from the issue text.