add service code to README

bentoml · Nov 3, 2022 · fce8ecc · fce8ecc
1 parent 3edeac8
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diff --git a/examples/monitoring/task_classification/README.md b/examples/monitoring/task_classification/README.md
@@ -34,7 +34,59 @@ bentoml.sklearn.save_model("iris_clf", clf)
 ```
 
 ### Serving the model
-Copy this to a `service.py` file, and run your service with Bento Server locally:
+Draft a `service.py` file with monitoring data collection lines, and run your service with Bento Server locally:
+
+```python
+import numpy as np
+
+import bentoml
+from bentoml.io import NumpyNdarray
+
+CLASS_NAMES = ["setosa", "versicolor", "virginica"]
+
+iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
+svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])
+
+
+@svc.api(
+    input=NumpyNdarray.from_sample(np.array([[4.9, 3.0, 1.4, 0.2]], dtype=np.double)),
+    output=NumpyNdarray(),
+)
+async def classify(input_series: np.ndarray) -> np.ndarray:
+    with bentoml.monitor("iris_classifier_prediction") as mon:
+        mon.log(
+            data=input_series[0][0],
+            name="sepal length",
+            role="feature",
+            data_type="numerical",
+        )
+        mon.log(
+            input_series[0][1],
+            name="sepal width",
+            role="feature",
+            data_type="numerical",
+        )
+        mon.log(
+            input_series[0][2],
+            name="petal length",
+            role="feature",
+            data_type="numerical",
+        )
+        mon.log(
+            input_series[0][3],
+            name="petal width",
+            role="feature",
+            data_type="numerical",
+        )
+        result = await iris_clf_runner.predict.async_run(input_series)
+        mon.log(
+            CLASS_NAMES[result[0]],
+            name="pred",
+            role="prediction",
+            data_type="categorical",
+        )
+    return result
+```
 
 ```bash
 bentoml serve service.py:svc --reload

diff --git a/examples/monitoring/task_classification/service.py b/examples/monitoring/task_classification/service.py
@@ -3,8 +3,9 @@
 import bentoml
 from bentoml.io import NumpyNdarray
 
-iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
+CLASS_NAMES = ["setosa", "versicolor", "virginica"]
 
+iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
 svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])
 
 
@@ -39,5 +40,10 @@ async def classify(input_series: np.ndarray) -> np.ndarray:
             data_type="numerical",
         )
         result = await iris_clf_runner.predict.async_run(input_series)
-        mon.log(result[0], name="pred", role="prediction", data_type="categorical")
+        mon.log(
+            CLASS_NAMES[result[0]],
+            name="pred",
+            role="prediction",
+            data_type="categorical",
+        )
     return result