Deploy and monitor model API endpoints in Domino. Covers creating prediction endpoints, version management, Grafana dashboards for latency/errors/resources, alerting, and GPU inference with NVIDIA Triton. Use when deploying models as APIs, monitoring production endpoints, or debugging endpoint issues.
This skill provides comprehensive knowledge for deploying and monitoring model API endpoints in Domino Data Lab.
Domino Model Endpoints provide:
Train Model → Register → Deploy Endpoint → Monitor → Update VersionImportant: Model APIs use the default environment set for your project. The environment must have the uwsgi Python package installed for model endpoints to work.
# Add to your environment's Dockerfile instructions
RUN pip install uwsgiOr in requirements.txt:
uwsgiuwsgi# model.py
def predict(features):
"""
Domino calls this function for predictions.
Args:
features: Input data (dict, list, or primitive)
Returns:
JSON-serializable prediction result
"""
import pickle
# Load model (cached after first call)
with open('model.pkl', 'rb') as f:
model = pickle.load(f)
prediction = model.predict([features])
return {"prediction": prediction.tolist()}my-classifiermodel.pypredictcurl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_TOKEN" \
-d '{"data": {"features": [1.0, 2.0, 3.0]}}' \
https://your-domino.com/models/abc123/latest/modelWhen calling endpoints from apps:
| Variable | Description |
|---|---|
MODEL_API_URL | Full endpoint URL |
MODEL_API_TOKEN | Bearer token for authentication |
| Metric | Target |
|---|---|
| Latency P50 | < 100ms |
| Latency P99 | < 500ms |
| Error Rate | < 1% |
| CPU Usage | < 80% |
| Memory | Stable (no growth) |
OpenAPI and route discovery: API-SPECS.md (public routes section).
92a240b
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.