Merge pull request #74: simplify README

- Short description of tool
- link to documentation site
- support information

README.md

@@ -5,58 +5,8 @@ Repository to benchmark CABLE. The benchmark will run the exact same configurati
 The code will: (i) check out; (ii) build; and (iii) run branches across N standard science configurations. It is possible to produce some plots locally. But the outputs should be uploaded to [the modelevaluation website](https://modelevaluation.org/) for further benchmarking and evaluation.
 
 For the moment, the benchmarking only works on NCI supercomputer.
-## Permissions
-To run the benchmarking, you will need access to the following projects at NCI:
-* ks32
-* wd9
-* hh5
 
-You can request access via [my.nci.org.au](https://my.nci.org.au/mancini/login?next=/mancini/). All of those projects accept all requests by default.
-
-## Create a work directory.
-1. **Choose a work directory.** 
-You can run the benchmark from any directory you want under /scratch or /g/data. /scratch is preferred as the data in the run directory does not need to be preserved for the long run. The code will create sub-directories as needed. Please ensure you have enough space to store the CABLE outputs in your directory, at least temporary until you upload them to [modelevaluation.org](https://modelevaluation.org/). You will need about 70GB.
-
-1. **Create a config file in the work directory.** 
-   The default name is `config.yaml` but any name can be specified at run time. This file follows the YAML format. You can find an example configuration file [here](https://github.com/CABLE-LSM/bench_example.git) 
-
-1. **Create a science configuration file in the work directory.**
-   The default name is `site_configs.yaml` but any name can be specified at run time. This file follows the YAML format. You can find an example science configuration file [here](https://github.com/CABLE-LSM/bench_example.git)
-
-## Run the benchmarking
-### Start the benchmarking process
-Once you have a configuration file, you need to load the modules for Python:
-```
-module use /g/data/hh5/public/modules
-module load conda/analysis3-unstable
-```
-Then you simply launch the benchmarking:
-```
-benchcab
-```
-For help on the available options for the benchmarking:
-```
-benchcab -h
-```
-
-Note: This line
-```
-module use /g/data/hh5/public/modules
-```
-can be safely added anywhere in your $HOME/.bashrc file. You then need to log out and back in for it to take effect. If you do so, you can simply load the Python module with `module load` and you do not have to type the `module use` line everytime. It is not recommended to put any `module load` lines in your $HOME/.bashrc file however.
-
-### Check the status
-The benchmarking will follow the steps:
-1. Checkout both branches. The codes will be stored under `src/` directory in your work directory. The directories are created automatically.
-1. Compile the source code from both branches
-1. Setup and launch a PBS job to run the simulations in parallel. The simulation inputs and outputs are stored under `runs/site/`. The directories are created automatically.
-
-When the benchmarking will launch the PBS job, it will print out the job id to screen. You can check the status of the job with `qstat`.
-## Use modelevaluation.org
-Once the benchmarking has finished running all the simulations, you need to upload the output files to modelevaluation.org. The output files can be found under `runs/site/outputs`.
-
-**Process still to be documented**
-
-## Contacts
-Please enter your questions as issues or discussions on the Github repository.
+You can see the full documentation to use it at https://benchcab.readthedocs.io/en/latest/
 
+## Support
+Please leave your questions as [Issues](https://github.com/CABLE-LSM/benchcab/issues) to receive support