PyEDB job manager backend—production documentation#

Overview#

The job manager backend is a hybrid async/sync service that schedules and monitors HFSS / 3D-Layout simulations on:

  • Local workstations (sub-process)

  • Enterprise clusters (SLURM, LSF, PBS, Windows-HPC)

It exposes thread-safe synchronous façades for legacy code bases while keeping a fully asynchronous core for high-throughput scenarios.

Module Reference#

job_manager_handler.py#

Thread-safe synchronous façade

  • Bridges blocking code to the async JobManager without exposing asyncio

  • Auto-detects ANSYS installation and cluster scheduler

  • Starts / stops a daemon thread hosting the aiohttp server

  • Provides convenience helpers such as create_simulation_config()

job_submission.py#

Cross-platform simulation launcher

  • Immutable data models: HFSSSimulationConfig, SchedulerOptions, MachineNode

  • Generates ready-to-run shell commands or batch scripts (SLURM/LSF)

  • Entry point: create_hfss_config() → config.run_simulation()

service.py#

Async job manager & REST layer

  • JobManager: priority queues, resource limits, Socket. IO push

  • ResourceMonitor: async telemetry (CPU, RAM, disk)

  • SchedulerManager: live cluster introspection (partitions, queues)

  • Self-hosted aiohttp application with REST + WebSocket endpoints

REST API#

Base URL defaults to http://localhost:8080. All JSON payloads use Content-Type: application/json.

Jobs#

Method

Route

Description

Payload

Response

POST

/jobs/submit

Queue new simulation

{"config": {...}, "priority": 0}

{"job_id": "uuid", "status": "queued"}

GET

/jobs

List all jobs

—

JSON array

POST

/jobs/{id}/cancel

Cancel queued / running job

—

{"success": true}

POST

/jobs/{id}/priority

Change job priority

{"priority": 5}

{"success": true}

Resources & Queues#

Method

Route

Description

Response

GET

/resources

Host telemetry snapshot

{"cpu_percent": 12.3, "memory_free_gb": 45.6, ...}

GET

/queue

Queue statistics

{"total_queued": 7, "running_jobs": 2, ...}

PUT

/pool/limits

Edit concurrency limits

{"max_concurrent_jobs": 4}

Cluster Introspection#

Method

Route

Description

Response

GET

/scheduler/partitions

Available partitions / queues

JSON array

GET

/system/status

Combined status object

Scheduler, resources, limits

WebSocket Events#

Connect to ws://host:port with Socket.IO. Emitted server → client:

  • job_queued

  • job_started

  • job_scheduled

  • job_completed

  • limits_updated

Quick Examples#

Synchronous (Legacy Code)#

from pyedb.workflows.job_manager.backend.job_manager_handler import JobManagerHandler

handler = JobManagerHandler()
handler.start_service()

cfg = handler.create_simulation_config(
    "/path/to/antenna.aedt", scheduler_type="slurm", cpu_cores=16
)

job_id = asyncio.run(handler.submit_job(cfg))
print("Submitted", job_id)

# Wait until finished
while handler.manager.jobs[job_id].status not in {"completed", "failed"}:
    time.sleep(1)

handler.close()

Asynchronous (Native Asyncio)#

from pyedb.workflows.job_manager.backend.service import JobManager, ResourceLimits

manager = JobManager(ResourceLimits(max_concurrent_jobs=4))

config = HFSSSimulationConfig.from_dict({...})
job_id = await manager.submit_job(config, priority=5)

await manager.wait_until_all_done()

Command Line#

python -m pyedb.workflows.job_manager.backend.job_manager_handler \
       --host 0.0.0.0 --port 8080

Deployment Notes#

  • The service is self-contained; no external database is required (jobs are stored in-memory). For persistence, plug in a small SQLite layer inside JobManager.jobs.

  • When running inside Docker, expose port 8080 and mount the project directory into the container so that ansysedt can access .aedt files.

  • CPU / RAM limits are soft limits; tune ResourceLimits to your workstation or cluster node size.

  • TLS termination should be handled by an upstream reverse proxy (nginx, reverse proxy, etc.); the backend only speaks plain HTTP/WebSocket.

See Also#