Connect to dask-scheduler from local workstation (maybe update documentation?)

> I connect to dask-scheduler from my local workstation to submit my computations (using a ssh tunnel).

I have had other people tell me a similar thing (third bullet point of https://github.com/dask/dask-jobqueue/issues/186#issuecomment-542027057). If you manage to make that work, please let us know (ideally in a separate issue).

_Originally posted by @lesteve in https://github.com/dask/dask-jobqueue/issues/186#issuecomment-568265386_

This helps circumvent problems where the login node of a cluster has very limited computing power and where you are not allowed to submit jobs from interactive nodes.

The idea is that the login node runs `dask-scheduler` and we submit the computations to it from a local workstation. The steps to do this:

1. start `ipython` on the login node and start the cluster, e.g.

```
from dask_jobqueue import PBSCluster
cluster = PBSCluster(…)
cluster.scale(100)
```

2. check the address of the cluster

```
In [2]: cluster
Out[2]: PBSCluster('tcp://192.168.57.5:43704', workers=100, threads=100, memory=250GB)
```

3. Taking note of the port of the scheduler (43704), now on the local machine setup an ssh tunnel to the cluster (and the dashboard)

```
ssh -N -L 8786:localhost:43704 -L 8787:localhost:8787 login.cluster
```

4. You should be able to access the dashboard on the local machine (localhost:8787)

5. Setup the `Client` on your local machine as follows:

```
from dask.distributed import Client
client = Client("tcp://localhost:8786")
```

Now you should be able to submit computations to the cluster from your local machine.