# Parallel SMAC (pSMAC)¶

SMAC also provides a parallel mode to use several parallel computational resources (such as CPU cores). This variant of SMAC is called pSMAC (parallel SMAC). The general idea is that all target algorithm run evaluations are shared between the individual SMAC runs such that all SMAC are better informed and can work together.

Note

To use pSMAC, please note that it communicates via the file space, i.e., all pSMAC runs write from time to time its runhistory (all target algorithm evaluations) to disk and read the runhistories of all other pSMAC runs. So, a requirement for pSMAC is that it can write to a shared file space.

## Commandline¶

To use pSMAC via the commandline interface, please specify the following two arguments:

• –shared_model True: this will activate the information sharing between the pSMAC runs

• –input_psmac_dirs <output_path>: list of pSMAC’s output directories (you can use shell-extension with asterix-notation)

Note

pSMAC has no option to specify the number of parallel runs. You have to start as many pSMAC runs as you want to run.

On the command line an exemplary call could be:

python smac --scenario SCENARIO --seed INT --shared_model True --input_psmac_dirs smac3-output*


We recommend that each pSMAC uses a different random seed.

If you want to verify that all arguments are correct and pSMAC finds all file on the file space, please set the verbose level to DEBUG and grep in the following way

python smac --verbose DEBUG [...] | grep -E "Loaded [0-9]+ new runs"


## Usage in Python¶

The same arguments used on the commandline can also be passed to the Scenario constructor. See above for a detailed description.

scenario = Scenario({...
"shared_model": True,
"input_psmac_dirs": <output_path>
})