Usage

Running pyTAMS

Using pyTAMS requires formulating your physics problem within the API defined by the pyTAMS ABC interface.

Once your new problem class is defined, running TAMS with pyTAMS only requires a few lines of code in say myTAMSrun.py:

from pytams.tams import TAMS
from myproblem import my_problem_class

if __name__ == "__main__":

    # Define a TAMS object with your problem class
    tams = TAMS(fmodel_t = my_problem_class)

    # Run TAMS
    probability = tams.compute_probability()

and then run your code, providing a TOML input file which is described in the next section:

python myTAMSrun.py -i input.toml

Note

TAMS should run multiple times in order to provide both an estimate of the probability and the associated standard error.

Controls

Upon instantiation of a TAMS object, the code search for a TOML file containing the run parameters. The path to the TOML file can be provided using the -i option, and the default name is input.toml in the current working directory.

The TOML input file contains dictionaries associated with the various part of the algorithm:

  • TAMS algorithm parameters:

[tams]
ntrajectories = 100       # [REQ] Number of trajectories
nsplititer = 1000         # [REQ] Number of splitting iterations
loglevel = "WARNING"      # [OPT, DEF = "INFO"] Log level
walltime = 200.0          # [OPT, DEF = 86400] Total walltime in seconds
diagnostics = false       # [OPT, DEF = false] Trigger diagnostics during the splitting iterations
deterministic = false     # [OPT, DEF = false] Fix the various random seeds for reproducibility
pool_only = false         # [OPT, DEF = false] Stop after the initial pool generation

  • Trajectory parameters:

[trajectory]
end_time = 10.0          # [REQ] End time
step_size = 0.01         # [REQ] Step size
targetscore = 0.95       # [OPT, DEF = 0.95] Target score
sparse_freq = 1          # [OPT, DEF = 1] Frequency of states sampling
sparse_start = 0         # [OPT, DEF = 0] Starting index of states sampling
chkfile_dump_all = false # [OPT, DEF = false] Update trajectory checkpoint file at each step

  • Runner parameters:

[runner]
type = "asyncio"         # [REQ] Runner type
nworker_init = 2         # [OPT, DEF = 1] Number of workers for initial pool generation
nworker_iter = 2         # [OPT, DEF = 1] Number of workers for splitting iterations

  • Database parameters:

[database]
path = "TamsDB.tdb"      # [OPT, no default] The database path, in-memory database if not specified
restart = false          # [OPT, DEF = false] If true, move the existing database before starting fresh
archive_discarded = true # [OPT, DEF = false] Archive trajectories discarded during splitting iterations

Additionally, when using a Dask runner, one has to provide configuration parameters for the Dask cluster:

[dask]
backend = "slurm"             # [OPT, DEF = "local"] Dask backend
worker_walltime = "48:00:00"  # [OPT, DEF = "04:00:00"] Slurm job walltime
queue = "genoa"               # [OPT, DEF = "regular"] Slurm job queue to submit the workers to
ntasks_per_job = 64           # [OPT, DEF = 1] Number of tasks per Slurm job
job_prologue = []             # [OPT, DEF = []] List of commands to be executed before the dask worker start

Finally, note that the entire TOML file content is passed as a dictionary to the forward model initializer. The user can then simply add an [model] dictionary to the TOML file to define model-specific parameters.