add conda env set up for ondemand

_sources/environment_set_up/OpenOnDemand.md

@@ -3,18 +3,25 @@
 This is a custom service provided by the USGS ARC team. It is the easiest to use (no configuration needed on your part), and provides reasonable compute resources via the `tallgrass` and `hovenweep` hosts:
 
 * To log in to OnDemand, select the appropriate login link from the `OnDemand` section of `https://hpcportal.cr.usgs.gov/`. Note that you must be on the VPN to access this host. Denali/Tallgrass share one disk for data storage and Hovenweep has a different disk. If you have data stored on the HPCs, you will want to choose whichever resource is attached to where your data is stored. If you are accessing data from a different, publicly accessible storage location, you can choose either option.
-* From the OnDemand landing page, choose `Interactive Apps`. If you are using `Hovenweep`, select the `Jupyter` option from this dropdown. If you are using `Tallgrass`, you can either select `Jupyter` or you can launch the `HyTEST Jupyter` server app, which will include a conda environment pre-configured with the packages you need to run the workflows in this JupyterBook. If you do not use our pre-configured environment (if you selected `Jupyter`), you will need to build your own. You can learn more about how to set up your own conda environment [here](https://hpcportal.cr.usgs.gov/hpc-user-docs/guides/software/environments/python/Python_Environment_Setup_with_Conda.html) in the HPC user docs.
+* From the OnDemand landing page, choose `Interactive Apps`. If you are using `Hovenweep`, select the `Jupyter` option from this dropdown. If you are using `Tallgrass`, you can either select `Jupyter` or you can launch the `HyTEST Jupyter` server app, which will include a conda environment pre-configured with the packages you need to run the workflows in this JupyterBook. If you do not use our pre-configured environment (if you selected `Jupyter`), you will need to build your own once your connect to the HPC. This process is described below in [Conda Environment Set Up](#conda-environment-set-up) 
 * Fill in the form to customize the allocation in which the Jupyter Server will execute.
+  * For light duty work (i.e. tutorials), a `Viz` node is likely adequate in your allocation request.  If you
+will be doing heavier processing, you may want to request a compute node.  None of the HyTEST tutorials
+utilize GPU code; a GPU-enabled node is not necessary.
   * You may want to consider adding the git and/or aws modules if you plan to use them during your session. You will just need to type `module load git` and/or `module load aws` in the `Module loads` section.
   * If you expect to run code in parallel on multiple compute nodes, you have two options. (1) You can use the form to request the number of cores you need and then run a [Dask Local Cluster](./Start_Dask_Cluster_Denali.ipynb) on those cores, or (2) you can request the standard 2 cores, and then use a [Dask SLURMCluster](./Start_Dask_Cluster_Tallgrass.ipynb) to submit new jobs to the SLURM scheduler, giving you access to additional compute nodes.
 * Click Submit
 * Once your server is ready, a `Connect to Jupyter` button will appear that you can click to start your session.
 
 The Jupyter Server will run in an allocation on `tallgrass` or `hovenweep`. This server will have access to your home
 directory/folder on that host, which is where your notebooks will reside.
+
+## Conda Environment Set Up
+If you need to set up your own conda environment on the HPCs, please refer to the [HPC User Docs](https://hpcportal.cr.usgs.gov/hpc-user-docs/guides/software/environments/python/Python_Environment_Setup_with_Conda.html). You can stop right before they create the sample python environment with `conda create env --name py310 python=3.10` and instead create whatever conda environment you need to use for your work. Once you have created your conda environment, you will need to take one additional step to make this conda environment visible as a kernel in your Jupyter Notebook:
+* activate your environment with `conda activate your_environment_name`
+* make sure `ipykernel` is installed in your conda environment
+* run `python -m ipykernel install --user --name your_environment_name --display-name "your_environment_name"`
 
-For light duty work (i.e. tutorials), a `Viz` node is likely adequate in your allocation request.  If you
-will be doing heavier processing, you may want to request a compute node.  None of the HyTEST tutorials
-utilize GPU code; a GPU-enabled node is not necessary.
+Now, you will be able to see the environment you just built as an available kernel from your Jupyter notebook.
 
-##### Note: The code to build this app is in [this repository](https://code.chs.usgs.gov/sas/arc/arc-software/ood/bc_jupyter_hytest); however, this repo is only visible on the internal USGS network.
+##### Note: The code to build the HyTEST Jupyter app is in [this repository](https://code.chs.usgs.gov/sas/arc/arc-software/ood/bc_jupyter_hytest); however, this repo is only visible on the internal USGS network.