Simulation Orchestrator

Simulation Orchestrator

Goal

Provide tools to manage multi-simulation campaigns: generate parameter sweeps, track job execution status, and aggregate results from completed runs.

Requirements

• Python 3.10+
• No external dependencies (uses Python standard library only)
• Works on Linux, macOS, and Windows

Inputs to Gather

Before running orchestration scripts, collect from the user:

Decision Guidance

Choosing a Sweep Method

Copy code
Need every combination (full factorial)?
├── YES → Use grid (warning: exponential growth with parameters)
└── NO → Is space-filling coverage needed?
    ├── YES → Use lhs (Latin Hypercube Sampling)
    └── NO → Use linspace for uniform sampling per parameter

Campaign Size Guidelines

Script Outputs (JSON Fields)

Note on swept parameter names: sweep_generator.py writes each swept value into the base config by key path. A bare name (e.g. kappa) overwrites a top-level key; a dot-notation name (e.g. parameters.kappa) targets a nested key. The swept key path must match where the solver reads the value — sweeping kappa against a config that nests parameters.kappa would add an unused top-level key and silently leave the base value in place. See references/sweep_strategies.md.

Workflow

Step 1: Generate Parameter Sweep

Create configurations for all parameter combinations:

bashCopy code
python3 scripts/sweep_generator.py \
    --base-config base_config.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./campaign_001 \
    --json

Step 2: Initialize Campaign

Create campaign tracking structure:

bashCopy code
python3 scripts/campaign_manager.py \
    --action init \
    --config-dir ./campaign_001 \
    --command "python sim.py --config {config}" \
    --json

Step 3: Track Job Status

Monitor running jobs:

bashCopy code
python3 scripts/job_tracker.py \
    --campaign-dir ./campaign_001 \
    --update \
    --json

Step 4: Aggregate Results

Combine results from completed runs:

bashCopy code
python3 scripts/result_aggregator.py \
    --campaign-dir ./campaign_001 \
    --metric final_energy \
    --json

result_aggregator.py minimizes by default: best_run is the run with the lowest metric value (and summary.minimize is true). If higher is better (e.g. yield, accuracy, throughput), pass --maximize so best_run becomes the highest value:

bashCopy code
# Higher is better -> select the maximum
python3 scripts/result_aggregator.py \
    --campaign-dir ./campaign_001 \
    --metric yield \
    --maximize \
    --json

Decision guidance: If higher is better (yield, accuracy, throughput), pass --maximize; otherwise the reported best_run is the minimum.

CLI Examples

bashCopy code
# Generate 5x3=15 runs varying dt (5 values) and kappa (3 values)
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./sweep_001 \
    --json

# Generate LHS samples for 4 parameters with budget of 20 runs
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2,kappa:0.1:1.0,M:1e-6:1e-4,W:0.5:2.0" \
    --method lhs \
    --samples 20 \
    --output-dir ./lhs_001 \
    --json

# Check campaign status
python3 scripts/campaign_manager.py \
    --action status \
    --config-dir ./sweep_001 \
    --json

# List jobs (read-only), optionally filtered by status
python3 scripts/campaign_manager.py \
    --action list \
    --config-dir ./sweep_001 \
    --status-filter failed \
    --json

# Get summary statistics from completed runs (minimize: best = lowest)
python3 scripts/result_aggregator.py \
    --campaign-dir ./sweep_001 \
    --metric final_energy \
    --json

# Maximization metric: best = highest value (yield, accuracy, throughput)
python3 scripts/result_aggregator.py \
    --campaign-dir ./sweep_001 \
    --metric yield \
    --maximize \
    --json

Conversational Workflow Example

User: I want to run a parameter sweep on dt and kappa for my phase-field simulation. I want to try 5 values of dt between 1e-4 and 1e-2, and 4 values of kappa between 0.1 and 1.0.

Agent workflow:

1. Calculate total runs: 5 x 4 = 20 runs
2. Generate sweep configurations:

   bashCopy code
   python3 scripts/sweep_generator.py \
       --base-config simulation.json \
       --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:4" \
       --method linspace \
       --output-dir ./dt_kappa_sweep \
       --json
   

3. Initialize campaign:

   bashCopy code
   python3 scripts/campaign_manager.py \
       --action init \
       --config-dir ./dt_kappa_sweep \
       --command "python phase_field.py --config {config}" \
       --json
   

4. After user runs simulations, aggregate results:

   bashCopy code
   python3 scripts/result_aggregator.py \
       --campaign-dir ./dt_kappa_sweep \
       --metric interface_width \
       --json
   

Error Handling

Integration with Other Skills

The simulation-orchestrator works with other simulation-workflow skills:

Copy code
parameter-optimization          simulation-orchestrator
        │                              │
        │ DOE samples ────────────────>│ Generate configs
        │                              │
        │                              │ Run simulations
        │                              │
        │<──────────────────────────── │ Aggregate results
        │                              │
        │ Sensitivity analysis         │
        │ Optimizer selection          │

Typical Combined Workflow

1. Use parameter-optimization/doe_generator.py to get sample points
2. Use simulation-orchestrator/sweep_generator.py to create configs
3. Run simulations (user's responsibility)
4. Use simulation-orchestrator/result_aggregator.py to collect results
5. Use parameter-optimization/sensitivity_summary.py to analyze

Verification checklist

Before trusting a campaign's best_run or summary statistics, record concrete evidence for each item:

• Confirmed the swept key path actually changed the value the solver reads: opened at least one generated config_NNNN.json and verified the swept parameter (e.g. parameters.kappa) holds the expected value at the expected nesting level, not a duplicate unused top-level key (sweep_generator.py writes by key path).
• Reconciled job accounting from result_aggregator.py --json: recorded summary.total_jobs, summary.completed, and summary.failed, and confirmed completed + failed == total_jobs. Any shortfall means runs were silently skipped (missing result file or extract_metric returned None) and must be investigated, not ignored.
• Confirmed completed > 0 and that the recorded summary.metric matches the field the solver actually writes. A typo'd or absent metric makes extract_metric return None, yielding zero completed runs with no error.
• Recorded summary.minimize and confirmed it matches the intended direction (default minimize; --maximize for yield/accuracy/throughput) before quoting best_run.
• Did NOT treat job_tracker.py "completed" as physical success: it flags a job completed purely from a result-file's existence and stamps exit_code 0 — independently checked the run's real exit status / solver logs for non-zero codes or NaN/Inf output.
• Applied an outlier/sanity check to the metric values (e.g. Tukey 1.5x IQR from references/aggregation_methods.md) and confirmed best_run.value is physically plausible, not a crashed run that emitted a spurious extremum.
• For LHS sweeps, recorded the --seed used and saved manifest.json (parameter bounds, total_runs, parameter_space) so the sample set is reproducible.

Common pitfalls & rationalizations

Security

Input Validation

• Metric names (result_aggregator.py --metric) are validated against [a-zA-Z_][a-zA-Z0-9_.]* to prevent traversal or injection via crafted keys
• Swept parameter names (sweep_generator.py --params) are validated against [a-zA-Z_][a-zA-Z0-9_]*(.[a-zA-Z_][a-zA-Z0-9_]*)* (dot notation for nested keys); invalid names are rejected
• campaign_manager.py validates command templates to reject shell chaining operators (;, |, &, backticks, $)
• --params format strings are parsed and validated (name:min:max:count with finite numeric bounds — NaN/Inf rejected — min < max, and positive integer counts capped at 100,000); at most 32 parameters per sweep
• --method is validated against a fixed allowlist (grid, linspace, lhs)
• --samples is validated as a positive integer with an upper bound (max 1,000,000)
• --action is validated against a fixed allowlist (init, status, list); for the read-only list action, --status-filter is validated against pending, running, completed, failed

File Access

• sweep_generator.py reads a single base config file (JSON) specified by --base-config and writes generated configs to --output-dir
• result_aggregator.py enforces a 10 MB file-size limit per result file, maximum JSON nesting depth, and strict numeric type checking (rejects bool, NaN, Inf)
• All string values from result files are sanitized (truncated, control characters stripped) before surfacing them
• Config paths interpolated into shell commands are validated against a safe-character allowlist and escaped with shlex.quote()

Tool Restrictions

• Read: Used to inspect script source, references, base configs, and campaign status files
• Write: Used to save generated sweep configs, campaign manifests, and aggregated results; writes are scoped to the user's working directory
• Grep/Glob: Used to locate campaign files, result files, and search references
• The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing untrusted simulation outputs

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• Reduced tool surface (no Bash) limits the agent to read/write operations only
• Command templates are validated but never executed by the skill itself; execution is the user's responsibility

Limitations

• Not a job scheduler: Does not submit jobs to SLURM/PBS; generates configs and tracks status
• No parallel execution: User must run simulations externally (can use GNU parallel, SLURM, etc.)
• File-based tracking: Status tracked via files; no database or real-time monitoring
• Local filesystem: Assumes all files accessible from local machine

References

• references/campaign_patterns.md - Common campaign structures
• references/sweep_strategies.md - Parameter sweep design guidance
• references/aggregation_methods.md - Result aggregation techniques

Version History

• v1.1.4 (2026-10-19): Preserve failure evidence and unknown exit codes, reject corrupt outputs, and retain small physical statistics without rounding.

See CHANGELOG.md for the authoritative, dated history. Summary:

• v1.1.3 (2026-06-24): Added a Verification checklist and a Common pitfalls & rationalizations section grounded in the scripts' real behavior (result-file-only "completed" detection, silent skip of unreadable metrics, minimize-by-default direction, key-path merge semantics)
• v1.1.1 (2026-06-23): Dot-notation nested overrides in sweep_generator.py, input-validation hardening (--params name/finite/count caps, --samples bounds), documented --maximize and the list action, corrected Script Outputs table and worked-example numbers
• v1.1.0 (2026-03-26): Standardized metadata, evaluation suite, security review, CHANGELOG
• v1.0.0 (2026-02-25): Initial release with sweep, campaign, tracking, and aggregation