| name | domino-launchers |
| description | Create Domino Launchers - parameterized web forms for self-service job execution. Enable business users to run analyses, generate reports, and trigger batch predictions without coding. Covers parameter types, email notifications, result delivery, and access control. Use when building self-service data products or enabling non-technical users. |
Domino Launchers Skill
Description
This skill helps users create and use Domino Launchers - web forms that allow non-technical users to run parameterized jobs and receive results.
Activation
Activate this skill when users want to:
- Create self-service data products
- Build parameterized job interfaces
- Enable business users to run analyses
- Generate reports on demand
- Share reproducible workflows
What is a Launcher?
A Launcher is:
- Web Form: UI for entering parameters
- Job Trigger: Runs a script with user inputs
- Results Delivery: Sends output via email/dashboard
- Self-Service: Business users can run without coding
Use Cases
- Report Generation: Parameterized business reports
- Batch Predictions: Score data on demand
- Data Exports: Custom data extracts
- Analysis Requests: Ad-hoc analytics
- Model Testing: Test models with different inputs
Creating a Launcher
Via Domino UI
- Go to your project
- Navigate to Deployments > Launchers
- Click New Launcher
- Configure:
- Name: Descriptive name
- Description: What the launcher does
- Command: Script to run
- Parameters: Input fields
- Hardware Tier: Resources
- Environment: Compute environment
Launcher Script (Python)
import argparse
import pandas as pd
parser = argparse.ArgumentParser()
parser.add_argument('--start-date', required=True)
parser.add_argument('--end-date', required=True)
parser.add_argument('--region', default='all')
args = parser.parse_args()
df = generate_report(args.start_date, args.end_date, args.region)
df.to_csv('/mnt/results/report.csv', index=False)
df.to_html('/mnt/results/report.html', index=False)
Launcher Script (R)
args <- commandArgs(trailingOnly = TRUE)
a <- as.integer(args[1])
b <- as.integer(args[2])
if (is.na(a)) {
print("A is not a number")
} else if (is.na(b)) {
print("B is not a number")
} else {
paste("The sum of", a, "and", b, "is:", a + b)
}
Command: launcher.R ${A} ${B}
Launcher Command
python generate_report.py --start-date ${start_date} --end-date ${end_date} --region ${region}
my_script.py -x=1 ${file} ${start_date}
launcher.R ${A} ${B}
Note: Parameter values are enclosed in single quotes, preserving special characters. File parameters pass the file path. Multi-select parameters pass comma-separated values.
Parameter Types
Text Input
name: customer_name
type: text
label: Customer Name
required: true
default: ""
Dropdown/Select
name: region
type: select
label: Region
options:
- North America
- Europe
- Asia Pacific
default: North America
Date Picker
name: start_date
type: date
label: Start Date
required: true
Number
name: quantity
type: number
label: Quantity
min: 1
max: 1000
default: 100
File Upload
name: input_file
type: file
label: Input File
accept: .csv,.xlsx
Generating Results
File-Based Results
Any files created in /mnt/results/ are available as results:
df.to_csv('/mnt/results/data.csv')
df.to_excel('/mnt/results/data.xlsx')
fig.savefig('/mnt/results/chart.png')
HTML Email Content
Create email.html for custom email body:
html_content = f"""
<html>
<body>
<h1>Report for {args.start_date} to {args.end_date}</h1>
<p>Summary: {summary}</p>
{df.to_html()}
</body>
</html>
"""
with open('/mnt/results/email.html', 'w') as f:
f.write(html_content)
Rich Reports
Use notebooks for rich reports:
import papermill as pm
pm.execute_notebook(
'report_template.ipynb',
'/mnt/results/report.ipynb',
parameters={
'start_date': args.start_date,
'end_date': args.end_date
}
)
Notifications
Email Configuration
Configure notification recipients:
- In launcher settings
- Add email addresses
- Results sent automatically on completion
Email Contents
- Link to results in Domino
- Attached files (if enabled)
- Custom HTML body (if email.html created)
Access Control
Who Can Run Launchers
- Contributors: Can create and run launchers
- Launcher Users: Can run launchers only
- Results Consumers: Can view results only
Setting Permissions
- Go to Project Settings
- Add users with appropriate roles
- Share launcher URL with users
Running a Launcher
Via UI
- Go to launcher page
- Fill in parameters
- Click Run
- Wait for results (email notification)
Via API
import requests, os
TOKEN = requests.get("http://localhost:8899/access-token").text.strip()
BASE = os.environ["DOMINO_API_HOST"]
response = requests.post(
f"{BASE}/v4/launchers/{{launcher_id}}/run",
headers={"Authorization": f"Bearer {TOKEN}"},
json={
"parameters": {
"start_date": "2024-01-01",
"end_date": "2024-01-31",
"region": "North America"
}
}
)
run_id = response.json()["runId"]
Viewing Results
Via Email
Results link sent to configured recipients.
Via Jobs Dashboard
Each launcher run creates a job:
- Go to Jobs in project
- Find the launcher job
- View results in job details
Programmatic Access
results = domino.runs_get_results(run_id)
Example: Scoring Launcher
Script (score_data.py)
import argparse
import pandas as pd
import joblib
parser = argparse.ArgumentParser()
parser.add_argument('--input-file', required=True)
parser.add_argument('--output-format', default='csv')
args = parser.parse_args()
model = joblib.load('/mnt/artifacts/model.joblib')
df = pd.read_csv(args.input_file)
predictions = model.predict(df)
df['prediction'] = predictions
if args.output_format == 'csv':
df.to_csv('/mnt/results/predictions.csv', index=False)
else:
df.to_excel('/mnt/results/predictions.xlsx', index=False)
Launcher Configuration
name: Score Customer Data
command: python score_data.py --input-file ${input_file} --output-format ${output_format}
parameters:
- name: input_file
type: file
label: Customer Data (CSV)
required: true
- name: output_format
type: select
label: Output Format
options: [csv, xlsx]
default: csv
Best Practices
1. Clear Parameter Names
Use descriptive labels users understand.
2. Input Validation
if args.end_date < args.start_date:
raise ValueError("End date must be after start date")
3. Progress Logging
print("Loading data...")
print(f"Processing {len(df)} records...")
print("Generating report...")
print("Complete!")
4. Error Handling
try:
process_data()
except Exception as e:
with open('/mnt/results/error.txt', 'w') as f:
f.write(f"Error: {str(e)}")
raise
5. Documentation
Include help text in launcher description.
Troubleshooting
Launcher Fails
- Check script runs manually first
- Verify file paths are correct
- Review job logs for errors
No Email Received
- Check email addresses configured
- Verify email server settings (admin)
- Check spam folder
Wrong Results
- Verify parameter passing
- Check variable substitution syntax
- Test with known inputs
Documentation Reference
