Running the simulations locally uses Docker. Docker needs to be configured to use all (or most) of the CPUs on your machine. To do so, click on the Docker icon by the clock. It looks like a little whale with boxes on its back. Select "Preferences..." and "Advanced". Slide the CPUs available to Docker to the number of concurrent simulations you want to run (probably all of them).
Running a project file is straightforward. Call the buildstock_docker command line tool as follows:
buildstock_docker --help
Warning
In general, you should omit the -j argument, which will use all the cpus you made available to docker. Setting the -j flag for a number greater than the number of CPUs you made available in Docker will cause the simulations to run slower as the concurrent simulations will compete for CPUs.
Warning
Running the simulation with postprocessonly when there is already postprocessed results from previous run will overwrite those results.
After you have activated the appropriate conda environment on Eagle <eagle_install>, you can submit a project file to be simulated by passing it to the buildstock_eagle command.
buildstock_eagle --help
Warning
Running the simulation with postprocessonly when there is already postprocessed results from previous run will overwrite those results.
To get a project to run on Eagle, you will need to make a few changes to your project_defn. First, the output_directory should be in /scratch/your_username/some_directory or in /projects somewhere. Building stock simulations generate a lot of output quickly and the /scratch or /projects filesystem are equipped to handle that kind of I/O throughput where your /home directory is not and may cause stability issues across the whole system.
Next, you will need to add an eagle top level key to the project file, which will look something like this
eagle:
account: your_hpc_allocation
n_jobs: 100 # the number of concurrent nodes to use on Eagle, typically 100-500
minutes_per_sim: 2
sampling:
time: 60 # the number of minutes you expect sampling to take
postprocessing:
time: 180 # the number of minutes you expect post processing to takeIn general, be conservative on the time estimates. It can be helpful to run a small batch with pretty conservative estimates and then look at the output logs to see how long things really took before submitting a full batch simulation.
Running a batch on AWS is done by calling the buildstock_aws command line tool.
buildstock_aws --help
For the project to run on AWS, you will need to add a section to your config file, something like this:
aws:
# The job_identifier should be unique, start with alpha, and limited to 10 chars or data loss can occur
job_identifier: national01
s3:
bucket: myorg-resstock
prefix: national01_run01
region: us-west-2
use_spot: true
batch_array_size: 10000
# To receive email updates on job progress accept the request to receive emails that will be sent from Amazon
notifications_email: your_email@somewhere.comSee aws-config for details.
When the simulation and postprocessing is all complete, run buildstock_aws --clean your_project_file.yml. This will clean up all the AWS resources that were created on your behalf to run the simulations. Your results will still be on S3 and queryable in Athena.