Example MCP outputs

[shuds13]

Discussion with Cursor on Auto mode - generated scripts not shown - to focus on AI output

Note: As template does not accept bounds the AI adjusted bounds after running the MCP.

Create libEnsemble scripts for gpCAM optimization

Exported on 11/5/2025 at 14:22:51 CST from Cursor (1.7.52)

---

User

Create libEnsemble scripts using gpCAM generator optimizing three parameters. The executable is /my/path/test.x and the directory my/input/dir where there is an input file /input.txt. The variables to alter in that file are called dim_x and dim_y. and energy. They the first two have upper and lower bounds of -1 to 1 and energy goes from 0.5 to 2.0. Create the scripts is a sub-directory called gpcam_run. Each sim uses 2 gpus. I want to do 100 simulations, with a maximum of 4 at a time.

---

Cursor

Examining the MCP server to understand the script creation parameters.

Checking the templates to understand bound handling and required parameters:

Creating the scripts. Checking if custom bounds can be passed, then generating the scripts and saving them to the specified directory.

Creating the directory and saving the generated scripts. Fixing bounds, input directory, and template variables:

Creating the corrected scripts with the proper bounds and configuration:

Created the libEnsemble scripts in the gpcam_run directory.

Summary

Created files:

• gpcam_run/run_libe.py - Main libEnsemble script
• gpcam_run/simf.py - Simulation function

Configuration:

• Generator: gpCAM (persistent_gpCAM)
• Parameters: 3 (dim_x, dim_y, energy)
• Bounds:
  • dim_x: -1.0 to 1.0
  • dim_y: -1.0 to 1.0
  • energy: 0.5 to 2.0
• Executable: /my/path/test.x
• Input directory: my/input/dir
• Input file: input.txt (will be templated with dim_x, dim_y, energy)
• GPUs: 2 per simulation
• Workers: 4 (max 4 simulations running concurrently)
• Total simulations: 100

The scripts are ready to use. The input.txt file in my/input/dir should use Jinja2 template syntax (e.g., {{ dim_x }}, {{ dim_y }}, {{ energy }}) so the values can be substituted during each simulation run.

[shuds13]

Example showing full log with scripts.

Create libEnsemble scripts for APOSMM optimization

Exported on 11/5/2025 at 14:52:24 CST from Cursor (1.7.52)

---

User

Create libEnsemble scripts using APOSMM generator optimizing three parameters. The executable is /my/path/test.x and the directory my/input/dir where there is an input file input.txt. The variables to alter in that file are called dim_x and dim_y. and energy. They the first two have upper and lower bounds of -1 to 1 and energy goes from 0.5 to 2.0. Create the scripts is a sub-directory called aposmm_run. Each sim uses 2 gpus. I want to do 100 simulations, with a maximum of 4 at a time.

---

Cursor

Reviewing the codebase to understand the script generation process.

Creating the libEnsemble scripts. The system doesn't support per-parameter bounds, so generating the scripts first, then updating the bounds. Checking the MCP tool to see how to call it:

Generating the scripts with the MCP tool, then creating the directory and files. Fixing the bounds afterward since the system doesn't support per-parameter bounds.

Creating the directory and writing the scripts with the correct settings:

Writing the corrected scripts with the proper settings:

import os
import sys

import numpy as np
from simf import run_test

from libensemble import Ensemble
from libensemble.alloc_funcs.persistent_aposmm_alloc import persistent_aposmm_alloc as alloc_f
from libensemble.executors import MPIExecutor
from libensemble.gen_funcs.persistent_aposmm import aposmm as gen_f
from libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs

if __name__ == "__main__":
    exctr = MPIExecutor()

    sim_app = "/my/path/test.x"

    if not os.path.isfile(sim_app):
        sys.exit(f"Application not found: {sim_app}")

    exctr.register_app(full_path=sim_app, app_name="test")

    num_workers = 4

    sim_input_dir = "my/input/dir"

    libE_specs = LibeSpecs(
        nworkers=num_workers,
        gen_on_manager=True,
        sim_dirs_make=True,
        sim_input_dir=sim_input_dir,
    )

    sim_specs = SimSpecs(
        sim_f=run_test,
        inputs=["x"],
        outputs=[("f", float)],
        user={"input_filename": "input.txt", "input_names": ["dim_x", "dim_y", "energy"]},
    )

    n = 3
    gen_specs = GenSpecs(
        gen_f=gen_f,
        inputs=[],
        persis_in=["sim_id", "x", "x_on_cube", "f"],
        outputs=[("x", float, n), ("x_on_cube", float, n), ("sim_id", int),
                 ("local_min", bool), ("local_pt", bool)],
        user={
            "initial_sample_size": num_workers,
            "localopt_method": "scipy_Nelder-Mead",
            "opt_return_codes": [0],
            "nu": 1e-8,
            "mu": 1e-8,
            "dist_to_bound_multiple": 0.01,
            "max_active_runs": 6,
            "lb": np.array([-1.0, -1.0, 0.5]),
            "ub": np.array([1.0, 1.0, 2.0])
        },
    )

    alloc_specs = AllocSpecs(
        alloc_f=alloc_f,
        
    )

    exit_criteria = ExitCriteria(sim_max=100)

    ensemble = Ensemble(
        libE_specs=libE_specs,
        gen_specs=gen_specs,
        sim_specs=sim_specs,
        alloc_specs=alloc_specs,
        exit_criteria=exit_criteria,
        executor=exctr
    )

    ensemble.add_random_streams()
    H, persis_info, flag = ensemble.run()

    if ensemble.is_manager:
        print("First 3:", H[["sim_id", "x", "f"]][:3])
        print("Last 3:", H[["sim_id", "x", "f"]][-3:])
        ensemble.save_output(__file__)

import numpy as np
import jinja2
# Optional status codes to display in libE_stats.txt for each gen or sim
from libensemble.message_numbers import TASK_FAILED, WORKER_DONE


def set_objective_value():
    try:
        data = np.loadtxt("test.stat")
        return data[-1]
    except Exception:
        return np.nan


def set_input_file_params(H, sim_specs, ints=False):
    """
    This is a general function to parameterize an input file with any inputs
    from sim_specs["in"]

    Often sim_specs_in["x"] may be multi-dimensional, where each dimension
    corresponds to a different input name in sim_specs["user"]["input_names"]).
    Effectively an unpacking of "x"
    """
    input_file = sim_specs["user"].get("input_filename")
    input_names = sim_specs["user"].get("input_names")
    if not input_file or not input_names:
        return
    input_values = {}
    for i, name in enumerate(input_names):
        value = int(H["x"][0][i]) if ints else H["x"][0][i]
        input_values[name] = value
    with open(input_file, "r") as f:
        template = jinja2.Template(f.read())
    with open(input_file, "w") as f:
        f.write(template.render(input_values))


def run_test(H, persis_info, sim_specs, libE_info):
    """Runs the test MPI application reading input from file"""

    calc_status = 0

    set_input_file_params(H, sim_specs)

    # Retrieve our MPI Executor
    exctr = libE_info["executor"]

    # Submit our test app for execution.
    task = exctr.submit(
        app_name="test",
        num_gpus=2,
    )

    # Block until the task finishes
    task.wait()

    # Read output and set the objective
    f = set_objective_value()

    # Optionally set the sim's status to show in the libE_stats.txt file
    if np.isnan(f):
        calc_status = TASK_FAILED
    else:
        calc_status = WORKER_DONE
    outspecs = sim_specs["out"]
    output = np.zeros(1, dtype=outspecs)
    output["f"][0] = f

    # Return final information to worker, for reporting to manager
    return output, persis_info, calc_status

Checking for linting errors:

Verifying the scripts are correctly configured:

Created the libEnsemble scripts in the aposmm_run directory. Summary:

Scripts Created

1. aposmm_run/run_libe.py — Main libEnsemble script with:

   • APOSMM generator (persistent_aposmm.aposmm)
   • 3 parameters: dim_x, dim_y, energy
   • Parameter bounds:
     • dim_x: -1.0 to 1.0
     • dim_y: -1.0 to 1.0
     • energy: 0.5 to 2.0
   • Executable: /my/path/test.x
   • Input directory: my/input/dir
   • Template file: input.txt with variables dim_x, dim_y, energy
   • 4 workers (max 4 simulations at a time)
   • Maximum 100 simulations

2. aposmm_run/simf.py — Simulation function with:

   • Template-based input file parameterization
   • 2 GPUs per simulation
   • Objective value reading from test.stat

The scripts use APOSMM to optimize the three parameters. The input file (input.txt) should use Jinja2 template syntax with {{ dim_x }}, {{ dim_y }}, and {{ energy }} placeholders that will be replaced with the generated values during each simulation.

[shuds13]

Uses the batch script templater. Note, AI adjusts max_active_runs after running MCP.

Create libEnsemble scripts for SLURM system.

Exported on 11/5/2025 at 15:47:15 CST from Cursor (1.7.52)

---

User

Create libEnsemble scripts using APOSMM generator optimizing three parameters. The executable is /global/space/test.x and the directory /global/input/dir where there is an input file input.txt, which is specified on the command line for each simulation. The variables to alter in that file are called x0 . x1 and x2. Create the scripts is a sub-directory called aposmm_perlmutter_run. Each sim uses the available nodes and GPUs for that worker. I want to do 200 simulations, with maximum of 4 workers running simulations. Create a SLURM batch script to run this on 8 nodes.

---

Cursor

Examining the existing structure to understand how scripts are generated.

Creating the libEnsemble scripts using the MCP tool. Checking how the tool is invoked, then generating the scripts.

Checking how auto_gpus is handled in the templates:

Creating the scripts using the MCP tool, then writing them to the directory:

Creating the output directory and parsing the generated scripts to fix issues. Reviewing the generated output and making corrections:

Reviewing the templates to understand the expected structure, then writing corrected scripts:

Fixing the generated scripts manually. The template rendering didn't set all required fields. Writing corrected versions:

import os
import sys

import numpy as np
from simf import run_test

from libensemble import Ensemble
from libensemble.alloc_funcs.persistent_aposmm_alloc import persistent_aposmm_alloc as alloc_f
from libensemble.executors import MPIExecutor
from libensemble.gen_funcs.persistent_aposmm import aposmm as gen_f
from libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs

if __name__ == "__main__":
    exctr = MPIExecutor()

    sim_app = "/global/space/test.x"

    if not os.path.isfile(sim_app):
        sys.exit(f"Application not found: {sim_app}")

    exctr.register_app(full_path=sim_app, app_name="test")

    num_workers = 4

    sim_input_dir = "/global/input/dir"

    libE_specs = LibeSpecs(
        nworkers=num_workers,
        gen_on_manager=True,
        sim_dirs_make=True,
        sim_input_dir=sim_input_dir,
    )

    sim_specs = SimSpecs(
        sim_f=run_test,
        inputs=["x"],
        outputs=[("f", float)],
        user={"input_filename": "input.txt", "input_names": ["x0", "x1", "x2"]},
    )

    n = 3
    gen_specs = GenSpecs(
        gen_f=gen_f,
        inputs=[],
        persis_in=["sim_id", "x", "x_on_cube", "f"],
        outputs=[("x", float, n), ("x_on_cube", float, n), ("sim_id", int),
                 ("local_min", bool), ("local_pt", bool)],
        user={
            "initial_sample_size": num_workers,
            "localopt_method": "scipy_Nelder-Mead",
            "opt_return_codes": [0],
            "nu": 1e-8,
            "mu": 1e-8,
            "dist_to_bound_multiple": 0.01,
            "max_active_runs": 6,
            "lb": np.array([0.0, 0.0, 0.0]),
            "ub": np.array([3.0, 3.0, 3.0])
        },
    )

    alloc_specs = AllocSpecs(
        alloc_f=alloc_f,
        
    )

    exit_criteria = ExitCriteria(sim_max=200)

    ensemble = Ensemble(
        libE_specs=libE_specs,
        gen_specs=gen_specs,
        sim_specs=sim_specs,
        alloc_specs=alloc_specs,
        exit_criteria=exit_criteria,
        executor=exctr
    )

    ensemble.add_random_streams()
    H, persis_info, flag = ensemble.run()

    if ensemble.is_manager:
        print("First 3:", H[["sim_id", "x", "f"]][:3])
        print("Last 3:", H[["sim_id", "x", "f"]][-3:])
        ensemble.save_output(__file__)

import numpy as np
import jinja2
# Optional status codes to display in libE_stats.txt for each gen or sim
from libensemble.message_numbers import TASK_FAILED, WORKER_DONE


def set_objective_value():
    try:
        data = np.loadtxt("test.stat")
        return data[-1]
    except Exception:
        return np.nan


def set_input_file_params(H, sim_specs, ints=False):
    """
    This is a general function to parameterize an input file with any inputs
    from sim_specs["in"]

    Often sim_specs_in["x"] may be multi-dimensional, where each dimension
    corresponds to a different input name in sim_specs["user"]["input_names"]).
    Effectively an unpacking of "x"
    """
    input_file = sim_specs["user"].get("input_filename")
    input_names = sim_specs["user"].get("input_names")
    if not input_file or not input_names:
        return
    input_values = {}
    for i, name in enumerate(input_names):
        value = int(H["x"][0][i]) if ints else H["x"][0][i]
        input_values[name] = value
    with open(input_file, "r") as f:
        template = jinja2.Template(f.read())
    with open(input_file, "w") as f:
        f.write(template.render(input_values))


def run_test(H, persis_info, sim_specs, libE_info):
    """Runs the test MPI application reading input from file"""

    calc_status = 0

    set_input_file_params(H, sim_specs)

    # Retrieve our MPI Executor
    exctr = libE_info["executor"]

    # Submit our test app for execution.
    task = exctr.submit(
        app_name="test",
        app_args=sim_specs["user"].get("input_filename"),
        auto_assign_gpus=True,
        match_procs_to_gpus=True,
    )

    # Block until the task finishes
    task.wait()

    # Read output and set the objective
    f = set_objective_value()

    # Optionally set the sim's status to show in the libE_stats.txt file
    if np.isnan(f):
        calc_status = TASK_FAILED
    else:
        calc_status = WORKER_DONE
    outspecs = sim_specs["out"]
    output = np.zeros(1, dtype=outspecs)
    output["f"][0] = f

    # Return final information to worker, for reporting to manager
    return output, persis_info, calc_status

#!/bin/bash
#SBATCH -J libE_aposmm
#SBATCH -A <myproject>
#SBATCH -p <partition_name>
#SBATCH -C <constraint_name>
#SBATCH --time 15
#SBATCH --nodes 8

# Usually either -p or -C above is used.

export MPICH_GPU_SUPPORT_ENABLED=1

python run_libe.py

Updating max_active_runs to 4 to match the requirement of 4 workers running simulations:

import os
import sys

import numpy as np
from simf import run_test

from libensemble import Ensemble
from libensemble.alloc_funcs.persistent_aposmm_alloc import persistent_aposmm_alloc as alloc_f
from libensemble.executors import MPIExecutor
from libensemble.gen_funcs.persistent_aposmm import aposmm as gen_f
from libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs

if __name__ == "__main__":
    exctr = MPIExecutor()

    sim_app = "/global/space/test.x"

    if not os.path.isfile(sim_app):
        sys.exit(f"Application not found: {sim_app}")

    exctr.register_app(full_path=sim_app, app_name="test")

    num_workers = 4

    sim_input_dir = "/global/input/dir"

    libE_specs = LibeSpecs(
        nworkers=num_workers,
        gen_on_manager=True,
        sim_dirs_make=True,
        sim_input_dir=sim_input_dir,
    )

    sim_specs = SimSpecs(
        sim_f=run_test,
        inputs=["x"],
        outputs=[("f", float)],
        user={"input_filename": "input.txt", "input_names": ["x0", "x1", "x2"]},
    )

    n = 3
    gen_specs = GenSpecs(
        gen_f=gen_f,
        inputs=[],
        persis_in=["sim_id", "x", "x_on_cube", "f"],
        outputs=[("x", float, n), ("x_on_cube", float, n), ("sim_id", int),
                 ("local_min", bool), ("local_pt", bool)],
        user={
            "initial_sample_size": num_workers,
            "localopt_method": "scipy_Nelder-Mead",
            "opt_return_codes": [0],
            "nu": 1e-8,
            "mu": 1e-8,
            "dist_to_bound_multiple": 0.01,
            "max_active_runs": 4,
            "lb": np.array([0.0, 0.0, 0.0]),
            "ub": np.array([3.0, 3.0, 3.0])
        },
    )

    alloc_specs = AllocSpecs(
        alloc_f=alloc_f,
        
    )

    exit_criteria = ExitCriteria(sim_max=200)

    ensemble = Ensemble(
        libE_specs=libE_specs,
        gen_specs=gen_specs,
        sim_specs=sim_specs,
        alloc_specs=alloc_specs,
        exit_criteria=exit_criteria,
        executor=exctr
    )

    ensemble.add_random_streams()
    H, persis_info, flag = ensemble.run()

    if ensemble.is_manager:
        print("First 3:", H[["sim_id", "x", "f"]][:3])
        print("Last 3:", H[["sim_id", "x", "f"]][-3:])
        ensemble.save_output(__file__)

Verifying the final files:

Created the libEnsemble scripts in aposmm_perlmutter_run. Summary:

Created Files:

1. run_libe.py — Main libEnsemble script:

   • Uses APOSMM generator with 3 parameters (x0, x1, x2)
   • Executable: /global/space/test.x
   • Input directory: /global/input/dir
   • 4 workers, max 200 simulations
   • max_active_runs set to 4 to limit concurrent simulations

2. simf.py — Simulation function:

   • Modifies input.txt with template variables (x0, x1, x2)
   • Passes input.txt as a command-line argument
   • Auto-assigns GPUs and matches processes to GPUs
   • Reads objective value from test.stat

3. submit_slurm.sh — SLURM batch script:

   • Requests 8 nodes
   • Enables GPU support via MPICH_GPU_SUPPORT_ENABLED=1
   • Runs the libEnsemble script

The scripts are ready to use. Update the SLURM script with your project account, partition, and constraint before submitting. The input file input.txt should use Jinja2 template syntax with {{ x0 }}, {{ x1 }}, and {{ x2 }} placeholders.

[shuds13]

Get it to create a run scripts starting from a supplied executable for each simulation and non-templated input file.

Example input given

$ cat input.txt 
num_particles = 23
num_steps = 10
rand_seed = 46

Note it used mpirun to launch libEnsemble which was not necessary but is supported.

Create and run libEnsemble scripts for forces calculations

Exported on 11/6/2025 at 16:10:50 CST from Cursor (1.7.52)

---

User

Create libEnsemble scripts using persistent random sampling generator optimizing one parameter. Each simulation should run /home/shudson/test_mcp/bin/forces.x using an input file based on /home/shudson/test_mcp/script-creator/input.txt. The variables to alter in that file is particles, which can be between 1000 and 3000. Create the scripts is a sub-directory called forces_run. Each simulation uses cores available to that worker. I want to do 16 simulations, with a maximum of 4 at a time. Once you have created the script please run it.

---

Cursor

Reviewing the input file and existing examples to understand the structure, then creating the libEnsemble scripts.

Checking available generators to understand the options:

Checking generator specs and input file structure:

Creating the libEnsemble scripts using the MCP tool. Checking the input file to confirm the variable name:

Creating the libEnsemble scripts with the MCP tool. Updating the input file to use template syntax for the particles variable:

Fixing the bounds (1000–3000), updating the input file to use template syntax, and saving the scripts to forces_run:

Creating the scripts with the correct bounds and template setup:

particles = {{ particles }}
num_steps = 10
rand_seed = 46

import os
import sys

import numpy as np
from simf import run_forces

from libensemble import Ensemble
from libensemble.alloc_funcs.start_only_persistent import only_persistent_gens as alloc_f
from libensemble.executors import MPIExecutor
from libensemble.gen_funcs.persistent_sampling import persistent_uniform as gen_f
from libensemble.specs import AllocSpecs, ExitCriteria, GenSpecs, LibeSpecs, SimSpecs

if __name__ == "__main__":
    exctr = MPIExecutor()

    sim_app = "/home/shudson/test_mcp/bin/forces.x"

    if not os.path.isfile(sim_app):
        sys.exit(f"Application not found: {sim_app}")

    exctr.register_app(full_path=sim_app, app_name="forces")

    num_workers = 4

    input_file = "/home/shudson/test_mcp/script-creator/forces_run/input.txt"


    libE_specs = LibeSpecs(
        nworkers=num_workers,
        gen_on_manager=True,
        sim_dirs_make=True,
        sim_dir_copy_files=[input_file],
    )

    sim_specs = SimSpecs(
        sim_f=run_forces,
        inputs=["x"],
        outputs=[("f", float)],
        user={"input_filename": "input.txt", "input_names": ["particles"]},
    )

    n = 1
    gen_specs = GenSpecs(
        gen_f=gen_f,
        inputs=[],
        persis_in=["sim_id", "f"],
        outputs=[("x", float, (1,))],
        user={
            "initial_batch_size": num_workers,
            "lb": np.array([1000.0]),
            "ub": np.array([3000.0]),
        },
    )

    alloc_specs = AllocSpecs(
        alloc_f=alloc_f,
        user={"async_return": True},
    )

    exit_criteria = ExitCriteria(sim_max=16)

    ensemble = Ensemble(
        libE_specs=libE_specs,
        gen_specs=gen_specs,
        sim_specs=sim_specs,
        alloc_specs=alloc_specs,
        exit_criteria=exit_criteria,
        executor=exctr
    )

    ensemble.add_random_streams()
    H, persis_info, flag = ensemble.run()

    if ensemble.is_manager:
        print("First 3:", H[["sim_id", "x", "f"]][:3])
        print("Last 3:", H[["sim_id", "x", "f"]][-3:])
        ensemble.save_output(__file__)

import numpy as np
import jinja2
# Optional status codes to display in libE_stats.txt for each gen or sim
from libensemble.message_numbers import TASK_FAILED, WORKER_DONE


def set_objective_value():
    try:
        data = np.loadtxt("forces.stat")
        return data[-1]
    except Exception:
        return np.nan


def set_input_file_params(H, sim_specs, ints=False):
    """
    This is a general function to parameterize an input file with any inputs
    from sim_specs["in"]

    Often sim_specs_in["x"] may be multi-dimensional, where each dimension
    corresponds to a different input name in sim_specs["user"]["input_names"]).
    Effectively an unpacking of "x"
    """
    input_file = sim_specs["user"].get("input_filename")
    input_names = sim_specs["user"].get("input_names")
    if not input_file or not input_names:
        return
    input_values = {}
    for i, name in enumerate(input_names):
        value = int(H["x"][0][i]) if ints else H["x"][0][i]
        input_values[name] = value
    with open(input_file, "r") as f:
        template = jinja2.Template(f.read())
    with open(input_file, "w") as f:
        f.write(template.render(input_values))


def run_forces(H, persis_info, sim_specs, libE_info):
    """Runs the forces MPI application reading input from file"""

    calc_status = 0

    set_input_file_params(H, sim_specs, ints=True)

    # Retrieve our MPI Executor
    exctr = libE_info["executor"]

    # Submit our forces app for execution.
    task = exctr.submit(
        app_name="forces",
    )

    # Block until the task finishes
    task.wait()

    # Read output and set the objective
    f = set_objective_value()

    # Optionally set the sim's status to show in the libE_stats.txt file
    if np.isnan(f):
        calc_status = TASK_FAILED
    else:
        calc_status = WORKER_DONE
    outspecs = sim_specs["out"]
    output = np.zeros(1, dtype=outspecs)
    output["f"][0] = f

    # Return final information to worker, for reporting to manager
    return output, persis_info, calc_status

Checking for linting errors, then running the script:

Running the script. libEnsemble typically requires MPI, so using mpirun:

Script ran successfully. Verifying output files were created:

Script ran successfully. Summary:

Created Scripts

1. forces_run/run_libe.py - Main libEnsemble script with:

   • Persistent uniform random sampling generator
   • 1 parameter (particles) with bounds 1000-3000
   • 4 workers (max 4 simulations at a time)
   • 16 total simulations

2. forces_run/simf.py - Simulation function that:

   • Uses Jinja2 to template the input file with the particles value
   • Runs /home/shudson/test_mcp/bin/forces.x
   • Reads the objective value from forces.stat

3. forces_run/input.txt - Template input file with {{ particles }} placeholder

Execution Results

The script completed all 16 simulations:

• Particles values sampled between 1000-3000 (e.g., 2273.92, 1539.57, 1081.95, etc.)
• All simulations completed successfully
• Results saved to run_libe_history_length=16_evals=16_workers=4.npy
• Each simulation has its own directory (sim0000 through sim0015) with input files and output

The libEnsemble scripts are ready for future runs. Each simulation uses the cores available to its worker, as configured.

=========================================

Output produced

shudson@shuds:script-creator$ ls forces_run/
 ensemble       libE_stats.txt                                      'run_libe_persis_info_length=16_evals=16_workers=4.pickle'
 ensemble.log   __pycache__                                          run_libe.py
 input.txt     'run_libe_history_length=16_evals=16_workers=4.npy'   simf.py

shudson@shuds:script-creator$ cat forces_run/input.txt 
particles = {{ particles }}
num_steps = 10
rand_seed = 46

shudson@shuds:script-creator$ cat forces_run/libE_stats.txt
Manager     : Starting ensemble at: 2025-11-06 16:09:17.587
Worker     3: sim_id     2: sim Time: 0.566 Start: 2025-11-06 16:09:17.608 End: 2025-11-06 16:09:18.174 Status: Completed
Worker     1: sim_id     0: sim Time: 0.604 Start: 2025-11-06 16:09:17.610 End: 2025-11-06 16:09:18.214 Status: Completed
Worker     4: sim_id     3: sim Time: 0.610 Start: 2025-11-06 16:09:17.607 End: 2025-11-06 16:09:18.217 Status: Completed
Worker     2: sim_id     1: sim Time: 0.608 Start: 2025-11-06 16:09:17.610 End: 2025-11-06 16:09:18.218 Status: Completed
Worker     4: sim_id     7: sim Time: 0.532 Start: 2025-11-06 16:09:18.221 End: 2025-11-06 16:09:18.753 Status: Completed
Worker     3: sim_id     4: sim Time: 0.636 Start: 2025-11-06 16:09:18.177 End: 2025-11-06 16:09:18.813 Status: Completed
Worker     1: sim_id     5: sim Time: 0.608 Start: 2025-11-06 16:09:18.216 End: 2025-11-06 16:09:18.824 Status: Completed
Worker     2: sim_id     6: sim Time: 0.604 Start: 2025-11-06 16:09:18.221 End: 2025-11-06 16:09:18.825 Status: Completed
Worker     3: sim_id     9: sim Time: 0.513 Start: 2025-11-06 16:09:18.814 End: 2025-11-06 16:09:19.327 Status: Completed
Worker     4: sim_id     8: sim Time: 0.577 Start: 2025-11-06 16:09:18.755 End: 2025-11-06 16:09:19.332 Status: Completed
Worker     1: sim_id    10: sim Time: 0.563 Start: 2025-11-06 16:09:18.827 End: 2025-11-06 16:09:19.390 Status: Completed
Worker     2: sim_id    11: sim Time: 0.564 Start: 2025-11-06 16:09:18.827 End: 2025-11-06 16:09:19.391 Status: Completed
Worker     2: sim_id    15: sim Time: 0.541 Start: 2025-11-06 16:09:19.393 End: 2025-11-06 16:09:19.934 Status: Completed
Worker     4: sim_id    13: sim Time: 0.627 Start: 2025-11-06 16:09:19.334 End: 2025-11-06 16:09:19.961 Status: Completed
Worker     3: sim_id    12: sim Time: 0.644 Start: 2025-11-06 16:09:19.330 End: 2025-11-06 16:09:19.974 Status: Completed
Worker     1: sim_id    14: sim Time: 0.581 Start: 2025-11-06 16:09:19.393 End: 2025-11-06 16:09:19.974 Status: Completed
Manager     : Gen no     1: gen Time: 2.373 Start: 2025-11-06 16:09:17.602 End: 2025-11-06 16:09:19.975 Status: Persis gen finished
Manager     : Exiting ensemble at: 2025-11-06 16:09:19.979 Time Taken: 2.392

Checking each input file:

shudson@shuds:script-creator$ grep particles forces_run/ensemble/sim*/input.txt
forces_run/ensemble/sim0000/input.txt:particles = 2273
forces_run/ensemble/sim0001/input.txt:particles = 1539
forces_run/ensemble/sim0002/input.txt:particles = 1081
forces_run/ensemble/sim0003/input.txt:particles = 1033
forces_run/ensemble/sim0004/input.txt:particles = 2626
forces_run/ensemble/sim0005/input.txt:particles = 2825
forces_run/ensemble/sim0006/input.txt:particles = 2213
forces_run/ensemble/sim0007/input.txt:particles = 2458
forces_run/ensemble/sim0008/input.txt:particles = 2087
forces_run/ensemble/sim0009/input.txt:particles = 2870
forces_run/ensemble/sim0010/input.txt:particles = 2631
forces_run/ensemble/sim0011/input.txt:particles = 1005
forces_run/ensemble/sim0012/input.txt:particles = 2714
forces_run/ensemble/sim0013/input.txt:particles = 1067
forces_run/ensemble/sim0014/input.txt:particles = 2459
forces_run/ensemble/sim0015/input.txt:particles = 1351