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.
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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) |
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