Running Jobs

Submit generation jobs, monitor progress, download results, and manage the job lifecycle.

Job Lifecycle

Every generation job progresses through these states:

created -> queued -> running -> succeeded | failed | canceled

Credits are deducted only on succeeded, never at submission time
A signed evidence bundle is written to storage on success
A provenance seal is committed to the seal registry on success

Creating a Job

Create a job

from radmah_sdk import RadMahClient

client = RadMahClient(api_key="sl_live_...")

# Generate synthetic data from a description
job = client.jobs.create(
    rows=100_000,
    description="customer transaction records"
)

# Wait for completion (polls every 2s)
job.wait()
print(f"Status: {job.status}")
print(f"Job ID: {job.id}")

Job Kinds

The platform automatically selects the appropriate generation pipeline for each job kind.

Kind	Description
mock	Seed-reproducible row generation from a natural-language description
synthesize	Trained generative model synthesis from uploaded data
simulate	Physics-based SCADA/ICS simulation
analyze	Column profiling and schema inference on uploaded data
train	Train a generative model on uploaded data
fit	Compute summary statistics for a seal
verify	Verify artifact integrity
fabricate	LLM-generated sealed contract from natural language
scenario_fabricate	LLM-generated SCADA/ICS scenario packs
lift	Transform raw data into typed WorldState

Monitoring Jobs

Monitor jobs

# Check status
job = client.jobs.get(job_id="...")
print(job.status)  # "running", "succeeded", etc.

# List recent jobs
jobs = client.jobs.list(limit=10)
for j in jobs:
    print(f"{j.id}: {j.status}")

# Wait with timeout
job.wait(timeout_seconds=300)  # raises TimeoutError after 5 min

Downloading Results

Download results

# As a pandas DataFrame
df = job.to_dataframe()

# Download to a file
job.download("output.csv")

# List all artifacts for a job
artifacts = client.artifacts.list(job_id=job.id)
for a in artifacts:
    print(f"{a.artifact_type}: {a.content_hash}")

# Download a specific artifact
client.artifacts.download(artifact_id=artifacts[0].id, path="artifact.bin")

Canceling and Rerunning

Cancel / rerun

# Cancel a running job
client.jobs.cancel(job_id="...")

# Rerun a completed job (same sealed contract, same seed = identical output)
rerun = client.jobs.rerun(job_id=job.id)
rerun.wait()

Queue Routing

Jobs are automatically routed to the appropriate Celery queue based on job kind and plan tier:

Queue	Purpose
radmah-default	Standard synthesize, analyze, and verify jobs
radmah-gpu	GPU-accelerated training jobs
radmah-free	Free-tier jobs (lower priority)
radmah-interactive	Chat-triggered quick jobs (low latency)

Credit Estimation

Before submitting a job, you can estimate the credit cost:

Estimate credits

curl https://api.radmah.ai/v1/client/estimate \
  -H "X-API-Key: sl_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "rows": 1000000,
    "kind": "synthesize"
  }'

# Response: { "estimated_credits": 1, "training_tier": "GPU-S" }

ℹCredit Rate

Generation credit rate: 1 credit = 1,000,000 rows across all job types. Training jobs use tiered pricing based on compute requirements. The platform always shows the estimated cost before you confirm.