Quick guide for LLNL AIMS4 or ACME1 (v1)

If you’re pressed for time, just follow this quick guide to run e3sm_diags on aims4 or acme1.

  1. Log on to aims4: 

ssh -Y aims4.llnl.gov

or acme1: 

ssh -Y acme1.llnl.gov

2. If you don’t have Anaconda installed, follow this guide.

  1. Make sure you are using bash 

bash

Installing and creating an environment

The steps below detail how to create your own environment with e3sm_diags. However, it is possible to use the E3SM Unified Environment instead. If you decide to use the unified environment, please do so and skip to step 5.

  1. Allow Anaconda to download packages, even with a firewall. 

conda config --set ssl_verify false
binstar config --set verify_ssl False

  1. Update Anaconda.

conda update conda

  1. Get the yml file to create an environment.

wget https://raw.githubusercontent.com/E3SM-Project/e3sm_diags/master/conda/e3sm_diags_env.yml

  1. Remove any cached Anaconda packages. This will ensure that you always get the latest packages. 

conda clean --all

8. Use Anaconda to create a new environment with e3sm_diags installed. Tip: You can change the name of the environment by adding -n new_env_name to the end of conda env create .... 

conda env create -f e3sm_diags_env.yml
source activate e3sm_diags_env

Running the entire Latitude-longitude contour set

9. Copy and paste the below code into myparams.py using your favorite text editor. Adjust any options as you like. 

reference_data_path = '/p/cscratch/acme/data/obs_for_e3sm_diags/climatology/'
test_data_path = '/p/cscratch/acme/data/test_model_data_for_acme_diags/climatology/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

sets = ["lat_lon"]

backend = 'mpl'  # 'mpl' is for the matplotlib plots.

results_dir = 'lat_lon_demo'  # name of folder where all results will be stored

multiprocessing = True
num_workers = 16  # Number of processes to use

  1. Run the diags.

e3sm_diags -p myparams.py

  1. Open the following webpage to view the results.

firefox --no-remote lat_lon_demo/viewer/index.html &

  • The --no-remote option uses the Firefox installed on this machine, and not the one on your machine.

Tip: Once you’re on the webpage for a specific plot, click on the ‘Output Metadata’ drop down menu to view the metadata for the displayed plot.

  • Running that command allows the displayed plot to be recreated. Changing any of the options will modify the resulting figure.

Running all of the diagnostics sets

12. To run all of the diagnostic sets that this software supports, open myparams.py and remove the sets parameter. If should look like this: 

reference_data_path = '/p/cscratch/acme/data/obs_for_e3sm_diags/climatology/'
test_data_path = '/p/cscratch/acme/data/test_model_data_for_acme_diags/climatology/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

# When commented out, this is ignored.
# We can also just delete this line.
# sets = ["lat_lon"]

backend = 'vcs'  # 'mpl' is for the matplotlib plots.

results_dir = 'lat_lon_demo'  # name of folder where all results will be stored

multiprocessing = True
num_workers = 16  # Number of processes to use

13. Now run and view the results. This will take some more time, so if you can, change the num_workers parameter to use more processors so it can be faster! 

e3sm_diags -p myparams.py
firefox --no-remote lat_lon_demo/viewer/index.html &

Advanced: Running custom diagnostics

The following steps are for ‘advanced’ users, who want to run custom diagnostics. So most users will not run the software like this.

14. By default, all of the E3SM diagnostics are ran for the sets that we defined above. This takes some time, so we’ll create our own diagnostics to be ran. Run the command 

touch mydiags.cfg

and paste the code below in mydiags.cfg. Check defining parameters for all available parameters. 

[#]
case_id = "GPCP_v2.2"
variables = ["PRECT"]
ref_name = "GPCP_v2.2"
reference_name = "GPCP (yrs1979-2014)"
seasons = ["ANN", "DJF"]
regions = ["global"]
test_colormap = "WhiteBlueGreenYellowRed.rgb"
reference_colormap = "WhiteBlueGreenYellowRed.rgb"
diff_colormap = "BrBG"
contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
diff_levels = [-5, -4, -3, -2, -1, -0.5, 0.5, 1, 2, 3, 4, 5]

[#]
case_id = "SST_CL_HadISST"
variables = ["SST"]
ref_name = "HadISST_CL"
reference_name = "HadISST/OI.v2 (Climatology) 1982-2001"
seasons = ["ANN", "MAM"]
contour_levels = [-1, 0, 1, 3, 6, 9, 12, 15, 18, 20, 22, 24, 26, 28, 29]
diff_levels = [-5, -4, -3, -2, -1, -0.5, -0.2, 0.2, 0.5, 1, 2, 3, 4, 5]

  1. Run the custom diagnostics.

e3sm_diags -p myparams.py -d mydiags.cfg

  1. Open the following webpage to view the results.

firefox --no-remote lat_lon_demo/viewer/index.html &

More Options

  • You can modify the sets parameters in myparams.py to run multiple sets. Possible options are: 'zonal_mean_xy', 'zonal_mean_2d', 'lat_lon, 'polar', 'cosp_histogram'. If the sets parameter is not defined, all of the aforementioned sets are ran. Ex: 

    sets = ['zonal_mean_xy', 'zonal_mean_2d', 'lat_lon', 'polar', 'cosp_histogram']

  • Diagnostics can be ran in parallel with multi-processing. In myparams.py, add multiprocessing = True and set num_workers to the number of workers you want to use. If num_workers is not defined, it will automatically use 4 processors processes by default on a machine. Ex: 

    # myparams.py
# In addition to your other parameters, include:
multiprocessing = True
num_workers = 4

Below figure shows a scalability test running the package for all lat_lon diagnostics on ACME1. Courtesy of Sterling Baldwin.

Performance_test

Performance test running the package with full set: “lat_lon” diagnostics on ACME1