Default Log Files
sim_count is the number of points evaluated. To suppress libEnsemble
from producing these two files, set
Two other libEnsemble files produced by default:
libE_stats.txt: This contains one-line summaries for each user calculation. Each summary is sent by workers to the manager and logged as the run progresses.
ensemble.log: This contains logging output from libEnsemble. The default logging level is INFO. In order to gain additional diagnostics, the logging level can be set to DEBUG. If this file is not removed, multiple runs will append output. Messages at or above MANAGER_WARNING are also copied to stderr to alert the user promptly.
To suppress libEnsemble from producing these two files, set
The libEnsemble logger uses the standard Python logging levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) plus one additional custom level (MANAGER_WARNING) between WARNING and ERROR.
The default level is INFO, which includes information about how tasks are submitted
and when tasks are killed. To gain additional diagnostics, set the logging level
to DEBUG. libEnsemble produces logging to the file
ensemble.log by default. A log
file name can also be supplied.
To change the logging level to DEBUG, provide the following in the calling scripts:
from libensemble import logger logger.set_level('DEBUG')
Logger messages of MANAGER_WARNING level or higher are also displayed through stderr by default. This boundary can be adjusted as follows:
from libensemble import logger # Only display messages with level >= ERROR logger.set_stderr_level('ERROR')
stderr displaying can be effectively disabled by setting the stderr level to CRITICAL.
Returns libEnsemble logging level
Returns libEnsemble stderr logging level
Sets logger filename if loggers not yet created, else None
Sets libEnsemble logging level
Sets logger to mirror certain messages to stderr
Working Directories for User Functions
libEnsemble features configurable output and working directory structuring for storing results at every step of a calculation, or directing workers to perform calculations on separate filesystems or in other directories. This is helpful for users performing simulations or using high-resource generator functions who want to take advantage of high-speed scratch spaces or disks, or organize their I/O by application run.
With these features enabled, each time a worker initiates a user function routine
sim_f) it automatically enters a configurable directory,
either a new directory specific to that worker and function instance or a shared
directory for all workers. Where these directories are created or what files
they contain is configurable through settings in libE_specs.
Defining any compatible settings initiates this system with default settings for
unspecified options. Each setting will be described in detail here:
'sim_dirs_make': Boolean. Enables per-simulation directories with default settings. Directories are labeled in the form
'sim0-worker1', by sim ID and initiating worker. Without further configuration, directories are placed in the ensemble directory
./ensemble, relative to where libEnsemble was launched. Default:
Truewith other sim_dir options enabled. If
False, all workers will operate within the ensemble directory without producing per-simulation directories.
'gen_dirs_make': Boolean. Enabled per-generator instance directories with default settings. Directories are labeled in the form
'gen1-worker1'. by initiating worker and how many times that worker has initiated the generator. These behave similarly to simulation directories. Default:
Truewith other gen_dir options enabled.
'ensemble_dir_path': This location, typically referred to as the ensemble directory, is where each worker places its calculation directories. If not specified, calculation directories are placed in
./ensemble, relative to where libEnsemble was launched. If
False, workers initiating simulation instances will run within this directory. This behavior is similar when
False. On supported systems, writing to local-node storage is possible and recommended for increased performance.:
libE_specs['ensemble_dir_path'] = "/scratch/my_ensemble"
'use_worker_dirs': Boolean. Sorts calculation directories into per-worker directories at runtime. Particularly useful for organization when running with multiple workers on global scratch spaces or the same node, and may produce performance benefits. Default:
Default structure with
- /ensemble_dir - /sim0-worker1 - /gen1-worker1 - /sim1-worker2 ...
libE_specs['use_worker_dirs'] = True:
- /ensemble_dir - /worker1 - /sim0 - /gen1 - /sim4 ... - /worker2 ...
'sim_dir_copy_files': A list of paths for files to copy into simulation directories. If
'sim_dirs_make'is False, these files are copied to the ensemble directory. If using the Executor to launch an application, this may be helpful for copying over configuration files for each launch.
'gen_dir_copy_files': A list of paths for files to copy into generator directories. If
'gen_dirs_make'is False, these files are copied to the ensemble directory.
'sim_dir_symlink_files': A list of paths for files to symlink into simulation directories.
'gen_dir_symlink_files': A list of paths for files to symlink into generator directories.
'ensemble_copy_back': Boolean. Instructs the manager to create an empty directory where libEnsemble was launched where workers copy back their calculation directories when a run concludes or an exception occurs. Especially useful when
'ensemble_dir_path'has been set to some scratch space or another temporary location. Default:
'sim_input_dir': A path to a directory to copy for simulation directories. This directory and its contents are copied to form the base of new simulation directories. If
'sim_dirs_make'is False, this directory’s contents are copied into the ensemble directory.
'gen_input_dir': A path to a directory to copy for generator directories. This directory and its contents are copied to form the base of new generator directories. If
'gen_dirs_make'is False, this directory’s contents are copied into the ensemble directory.
See the regression tests
test_use_worker_dirs.py for examples of many of these settings.
test_sim_input_dir_option.py for examples of using these settings
without simulation-specific directories.
postproc_scripts directory, in the libEnsemble project root directory,
contains scripts to compare outputs and create plots based on the ensemble output.
Timing analysis scripts
Note that all plotting scripts produce a file rather than opening a plot interactively.
The following scripts must be run in the directory with the
file. They extract and plot information from that file.
plot_libe_calcs_util_v_time.py: Extracts worker utilization vs. time plot (with one-second sampling). Shows number of workers running user sim or gen functions over time.
plot_libe_runs_util_v_time.py: Extracts launched task utilization v time plot (with one second sampling). Shows number of workers with active tasks, launched via the executor, over time.
plot_libe_histogram.py: Creates histogram showing the number of completed/killed/failed user calculations binned by run time.
Results analysis scripts
print_npy.py: Prints to screen from a given
*.npyfile containing a NumPy structured array. Use
doneto print only the lines containing
./print_npy.py run_libe_forces_results_History_length=1000_evals=8.npy done
print_fields.py: Prints to screen from a given
*.npyfile containing a NumPy structured array. This is a more versatile version of
print_npy.pythat allows the user to select fields to print and boolean conditions determining which rows are printed (see
./print_fields.py -hfor usage).
compare_npy.py: Compares either two provided
*.npyfiles or one provided
*.npyfile with an expected results file (by default located at ../expected.npy). A tolerance is given on floating-point results, and NANs are compared as equal. Variable fields (such as those containing a time) are ignored. These fields may need to be modified depending on the user’s history array.
plot_pareto_2d.py: Loop through objective points in f and extract the Pareto front. Arguments are an
*.npyfile and a budget.
plot_pareto_3d.py: Loop through objective points in f and extract the Pareto front. Arguments are an
*.npyfile and a budget.
print_pickle.py: Prints to screen from a given
Balsam analysis scripts
These scripts require an activated Balsam database and create plots as
plot_util_v_time.py: Shows number of active Balsam launched tasks over time
plot_tasks_v_time.py: Shows completed Balsam launched tasks over time
plot_waiting_v_time.py: Shows number of tasks waiting in Balsam database over time