Output Management

Default Log Files

The history array H and persis_info dictionary are returned to the user by libEnsemble. If libEnsemble aborts on an exception, these structures are dumped automatically to the respective files:

libE_history_at_abort_<sim_count>.npy
libE_history_at_abort_<sim_count>.pickle

where sim_count is the number of points evaluated. To suppress libEnsemble from producing these two files, set libE_specs['save_H_and_persis_on_abort'] to False.

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 libE_specs['disable_log_files'] to True.

Logger Configuration

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.

logger.get_level(): Returns libEnsemble logging level

logger.get_stderr_level(): Returns libEnsemble stderr logging level

logger.set_filename(filename): Sets logger filename if loggers not yet created, else None

logger.set_level(level): Sets libEnsemble logging level

logger.set_stderr_level(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 (gen_f or 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: True with 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: True with 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 'sim_dirs_make' is False, workers initiating simulation instances will run within this directory. This behavior is similar when 'gen_dirs_make' is 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: False.

Default structure with 'use_worker_dirs' unspecified:
```
- /ensemble_dir
    - /sim0-worker1
    - /gen1-worker1
    - /sim1-worker2
    ...
```
Structure with 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: False.
'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_sim_dirs_per_calc.py and test_use_worker_dirs.py for examples of many of these settings. See test_sim_input_dir_option.py for examples of using these settings without simulation-specific directories.

Note

The 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 libE_stats.txt 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 *.npy file containing a NumPy structured array. Use done to print only the lines containing 'returned' points. Example:
```
./print_npy.py run_libe_forces_results_History_length=1000_evals=8.npy done
```
print_fields.py: Prints to screen from a given *.npy file containing a NumPy structured array. This is a more versatile version of print_npy.py that allows the user to select fields to print and boolean conditions determining which rows are printed (see ./print_fields.py -h for usage).
compare_npy.py: Compares either two provided *.npy files or one provided *.npy file 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 *.npy file and a budget.
plot_pareto_3d.py: Loop through objective points in f and extract the Pareto front. Arguments are an *.npy file and a budget.

print_pickle.py: Prints to screen from a given *.pickle file. Example:

./print_pickle.py persis_info_length=1000_evals=1000_workers=2.pickle

Balsam analysis scripts

These scripts require an activated Balsam database and create plots as png files.

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