mirror of https://github.com/knative/docs.git
fix ML example (#5983)
* fix ML example * fix dockerize * type variable df * fix code
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mattn
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@ -39,15 +39,18 @@ as API endpoint with Knative Serving.
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API, which is the entry point for accessing this machine learning service.
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```python
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from bentoml import env, artifacts, api, BentoService
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from bentoml.handlers import DataframeHandler
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from bentoml.artifact import SklearnModelArtifact
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import bentoml
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import joblib
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@env(auto_pip_dependencies=True)
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@artifacts([SklearnModelArtifact('model')])
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class IrisClassifier(BentoService):
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@api(DataframeHandler)
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@bentoml.service
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class IrisClassifier:
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iris_model = bentoml.models.get("iris_classifier:latest")
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def __init__(self):
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self.model = joblib.load(self.iris_model.path_of("model.pkl"))
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@bentoml.api
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def predict(self, df):
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return self.artifacts.model.predict(df)
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```
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@ -58,10 +61,11 @@ as API endpoint with Knative Serving.
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given data and then save the model with BentoML to local disk.
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```python
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import joblib
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from sklearn import svm
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from sklearn import datasets
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from iris_classifier import IrisClassifier
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import bentoml
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if __name__ == "__main__":
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# Load training data
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@ -72,14 +76,9 @@ as API endpoint with Knative Serving.
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clf = svm.SVC(gamma='scale')
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clf.fit(X, y)
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# Create a iris classifier service instance
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iris_classifier_service = IrisClassifier()
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# Pack the newly trained model artifact
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iris_classifier_service.pack('model', clf)
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# Save the prediction service to disk for model serving
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saved_path = iris_classifier_service.save()
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with bentoml.models.create("iris_classifier") as bento_model:
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joblib.dump(clf, bento_model.path_of("model.pkl"))
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print(f"Model saved: {bento_model}")
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```
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1. Run the `main.py` file to train and save the model:
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@ -91,48 +90,97 @@ as API endpoint with Knative Serving.
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1. Use BentoML CLI to check saved model's information.
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```bash
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bentoml get IrisClassifier:latest
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bentoml get iris_classifier:latest
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```
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Example:
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```bash
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> bentoml get IrisClassifier:latest
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> bentoml get iris_classifier:latest -o json
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{
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"service": "iris_classifier:IrisClassifier",
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"name": "iris_classifier",
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"version": "ar67rxqxqcrqi7ol",
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"bentoml_version": "1.2.16",
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"creation_time": "2024-05-21T14:40:20.737900+00:00",
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"labels": {
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"owner": "bentoml-team",
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"project": "gallery"
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},
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"models": [],
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"runners": [],
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"entry_service": "IrisClassifier",
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"services": [
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{
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"name": "IrisClassifier",
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"version": "20200305171229_0A1411",
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"uri": {
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"type": "LOCAL",
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"uri": "/Users/bozhaoyu/bentoml/repository/IrisClassifier/20200305171229_0A1411"
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},
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"bentoServiceMetadata": {
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"name": "IrisClassifier",
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"version": "20200305171229_0A1411",
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"createdAt": "2020-03-06T01:12:49.431011Z",
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"env": {
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"condaEnv": "name: bentoml-IrisClassifier\nchannels:\n- defaults\ndependencies:\n- python=3.7.3\n- pip\n",
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"pipDependencies": "bentoml==0.6.2\nscikit-learn",
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"pythonVersion": "3.7.3"
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},
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"artifacts": [
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"service": "",
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"models": [
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{
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"name": "model",
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"artifactType": "SklearnModelArtifact"
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"tag": "iris_sklearn:ml5evdaxpwrqi7ol",
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"module": "",
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"creation_time": "2024-05-21T14:21:17.070059+00:00"
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}
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],
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"apis": [
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"dependencies": [],
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"config": {}
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}
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],
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"envs": [],
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"schema": {
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"name": "IrisClassifier",
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"type": "service",
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"routes": [
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{
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"name": "predict",
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"handlerType": "DataframeHandler",
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"docs": "BentoService API",
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"handlerConfig": {
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"orient": "records",
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"typ": "frame",
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"input_dtypes": null,
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"output_orient": "records"
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"route": "/predict",
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"batchable": false,
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"input": {
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"properties": {
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"df": {
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"title": "Df"
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}
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},
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"required": [
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"df"
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],
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"title": "Input",
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"type": "object"
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},
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"output": {
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"title": "AnyIODescriptor"
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}
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}
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]
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},
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"apis": [],
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"docker": {
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"distro": "debian",
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"python_version": "3.11",
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"cuda_version": null,
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"env": null,
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"system_packages": null,
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"setup_script": null,
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"base_image": null,
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"dockerfile_template": null
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},
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"python": {
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"requirements_txt": "./requirements.txt",
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"packages": null,
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"lock_packages": true,
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"pack_git_packages": true,
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"index_url": null,
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"no_index": null,
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"trusted_host": null,
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"find_links": null,
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"extra_index_url": null,
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"pip_args": null,
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"wheels": null
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},
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"conda": {
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"environment_yml": null,
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"channels": null,
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"dependencies": null,
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"pip": null
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}
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}
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```
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@ -141,7 +189,7 @@ as API endpoint with Knative Serving.
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BentoML CLI command to start an API server locally and test it with the `curl` command.
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```bash
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bentoml serve IrisClassifier:latest
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bentoml serve iris_classifier:latest
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```
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In another terminal window, make `curl` request with sample data to the API server
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@ -164,12 +212,8 @@ a Dockerfile is automatically generated when saving the model.
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username and run the following commands.
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```bash
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# jq might not be installed on your local system, please follow jq install
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# instruction at https://stedolan.github.io/jq/download/
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saved_path=$(bentoml get IrisClassifier:latest -q | jq -r ".uri.uri")
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# Build and push the container on your local machine.
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docker buildx build --platform linux/arm64,linux/amd64 -t "{username}/iris-classifier" --push $saved_path
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bentoml containerize iris_classifier:latest -t "{username}/iris-classifier" --push
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```
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1. In `service.yaml`, replace `{username}` with your Docker hub username:
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@ -1,11 +1,23 @@
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from bentoml import env, artifacts, api, BentoService
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from bentoml.handlers import DataframeHandler
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from bentoml.artifact import SklearnModelArtifact
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import numpy as np
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import bentoml
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from pydantic import Field
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from bentoml.validators import Shape
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from typing_extensions import Annotated
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import joblib
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@env(auto_pip_dependencies=True)
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@artifacts([SklearnModelArtifact('model')])
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class IrisClassifier(BentoService):
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@api(DataframeHandler)
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def predict(self, df):
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return self.artifacts.model.predict(df)
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@bentoml.service
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class IrisClassifier:
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iris_model = bentoml.models.get("iris_sklearn:latest")
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def __init__(self):
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self.model = joblib.load(self.iris_model.path_of("model.pkl"))
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@bentoml.api
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def predict(
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self,
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df: Annotated[np.ndarray, Shape((-1, 4))] = Field(
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default=[[5.2, 2.3, 5.0, 0.7]]
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),
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) -> np.ndarray:
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return self.model.predict(df)
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@ -1,7 +1,8 @@
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import joblib
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from sklearn import svm
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from sklearn import datasets
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from iris_classifier import IrisClassifier
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import bentoml
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if __name__ == "__main__":
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# Load training data
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@ -12,11 +13,6 @@ if __name__ == "__main__":
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clf = svm.SVC(gamma='scale')
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clf.fit(X, y)
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# Create a iris classifier service instance
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iris_classifier_service = IrisClassifier()
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# Pack the newly trained model artifact
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iris_classifier_service.pack('model', clf)
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# Save the prediction service to disk for model serving
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saved_path = iris_classifier_service.save()
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with bentoml.models.create("iris_classifier") as bento_model:
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joblib.dump(clf, bento_model.path_of("model.pkl"))
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print(f"Model saved: {bento_model}")
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