Domino Jobs

Via Domino UI

1. Navigate to your project
2. Click Jobs in the navigation
3. Click Run
4. Configure:
   • File to Run: Script path (e.g., train.py)
   • Arguments: Command-line arguments (optional)
   • Hardware Tier: Select resources
   • Compute Environment: Select environment
5. Click Start

Via Python SDK

import os
from domino import Domino

domino = Domino(
    "project-owner/project-name",
    api_key=os.environ["DOMINO_USER_API_KEY"],
    host=os.environ["DOMINO_API_HOST"],
)

# Blocking execution - waits for job to complete
domino_run = domino.runs_start_blocking(
    ["train.py", "--epochs", "100"],
    title="Training run from Python SDK"
)
print(domino_run)

# Non-blocking execution - returns immediately
domino_run = domino.runs_start(
    ["train.py", "--epochs", "100"],
    title="Training run from Python SDK"
)
print(f"Job ID: {domino_run['runId']}")

# Check status of non-blocking run
run_status = domino.runs_status(domino_run.get("runId"))
print(f"Status: {run_status['status']}")

Note: Environment variables DOMINO_USER_API_KEY and DOMINO_API_HOST are automatically configured when running within Domino workspaces or jobs.

Via Domino CLI

# Start a job with script
domino run train.py

# Run with arguments
domino run train.py arg1 arg2 arg3

# Wait for job to complete
domino run --wait train.py arg1 arg2

# Run direct command (not a script)
domino run --direct "pip freeze | grep pandas"

Via REST API

curl -X POST "https://your-domino.com/v4/projects/{project_id}/runs" \
  -H "X-Domino-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "command": "python train.py",
    "hardwareTierId": "tier-id",
    "environmentId": "env-id"
  }'

Job Commands

Run Python Script

python train.py

Run with Arguments

python train.py --data /mnt/data/train.csv --output /mnt/artifacts/model.pkl

Run Jupyter Notebook

jupyter nbconvert --to notebook --execute notebook.ipynb

Run R Script

Rscript analysis.R

Run Shell Script

bash pipeline.sh

Scheduled Jobs

Creating a Scheduled Job

1. Go to Scheduled Jobs in your project
2. Click New Scheduled Job
3. Configure:
   • Name: Descriptive name
   • Command: Script to execute
   • Schedule: Cron expression or preset
   • Hardware Tier: Resources to use
   • Environment: Compute environment

Schedule Examples

Cron Expression Format

┌───────────── second (0-59)
│ ┌───────────── minute (0-59)
│ │ ┌───────────── hour (0-23)
│ │ │ ┌───────────── day of month (1-31)
│ │ │ │ ┌───────────── month (1-12)
│ │ │ │ │ ┌───────────── day of week (0-7, SUN-SAT)
│ │ │ │ │ │
* * * * * *

Run Modes

Sequential: Wait for previous job to complete before starting next

# Good for jobs that depend on previous output
# Example: Daily model retrain that uses previous day's data

Concurrent: Allow multiple jobs to run simultaneously

# Good for independent jobs
# Example: Hourly data refresh that doesn't depend on previous runs

Job Notifications

Email Notifications

Configure in job settings:

• Success notifications
• Failure notifications
• Specific email addresses

Model API Updates

Trigger Model API republish after job completes:

1. In scheduled job settings
2. Select Update Model API option
3. Choose the Model API to update

Accessing Job Results

Output Files

Files written to /mnt/ directories are available after job completion:

• /mnt/results/ - Custom outputs
• /mnt/artifacts/ - Model artifacts

Job Logs

View logs in Domino UI or via API:

# Get job logs
logs = domino.runs_get_logs(run_id)
print(logs)

Stdout/Stderr

All print statements and errors are captured in job logs.

Environment Variables in Jobs

import os

# Domino-provided
run_id = os.environ.get('DOMINO_RUN_ID')
project_name = os.environ.get('DOMINO_PROJECT_NAME')
username = os.environ.get('DOMINO_USER_NAME')

# Custom (set in project or job settings)
api_key = os.environ.get('MY_API_KEY')

Job Best Practices

1. Parameterize Scripts

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--data-path', required=True)
parser.add_argument('--model-output', required=True)
parser.add_argument('--epochs', type=int, default=100)
args = parser.parse_args()

2. Log Progress

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

logger.info("Starting training...")
logger.info(f"Epoch {epoch}/{total_epochs}")
logger.info("Training complete!")

3. Handle Failures Gracefully