Bump bentoml from 1.0.0a4 to 1.0.13

[dependabot]

Bumps bentoml from 1.0.0a4 to 1.0.13.

Release notes

Sourced from bentoml's releases.

BentoML - v1.0.12

Important bug fixes.

• Fixed runner call failures with keyword arguments.
• Fixed incorrect user base image override .

What's Changed

• fix(runner): content-type error by @​aarnphm in bentoml/BentoML#3302
• feat: grpc servicer implementation per version by @​aarnphm in bentoml/BentoML#3316
• feat(grpc): adding service metadata by @​aarnphm in bentoml/BentoML#3278
• docs: Update monitoring docs format by @​ssheng in bentoml/BentoML#3324
• fix(runner): remote run_method with kwargs by @​larme in bentoml/BentoML#3326
• fix: don't overwrite user base image by @​aarnphm in bentoml/BentoML#3329
• fix: add upper bound for packaging version by @​aarnphm in bentoml/BentoML#3331
• fix(container): podman health result string parsing by @​aarnphm in bentoml/BentoML#3330
• fix: io descriptor backward compatibility by @​sauyon in bentoml/BentoML#3327

Full Changelog: bentoml/BentoML@v1.0.11...v1.0.12

BentoML - v1.0.11

🍱 BentoML v1.0.11 is here featuring the introduction of an inference collection and model monitoring API that can be easily integrated with any model monitoring frameworks.

• Introduced the bentoml.monitor API for monitoring any features, predictions, and target data in numerical, categorical, and numerical sequence types.

  import bentoml
  from bentoml.io import Text
  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=Text(),
  )
  async def classify(features: np.ndarray) -> str:
  with bentoml.monitor("iris_classifier_prediction") as mon:
  mon.log(features[0], name="sepal length", role="feature", data_type="numerical")
  mon.log(features[1], name="sepal width", role="feature", data_type="numerical")
  mon.log(features[2], name="petal length", role="feature", data_type="numerical")
  mon.log(features[3], name="petal width", role="feature", data_type="numerical")
      results = await iris_clf_runner.predict.async_run([features])
      result = results[0]
      category = CLASS_NAMES[result]

... (truncated)

Commits

• 4d2fd62 feat: YataiClient context to communicate with multiple Yatai instances (#3448)
• a180d7a fix: make sure to run migration for envvar (#3339)
• 39d601a fix(exception): wrong variable reference (#3450)
• 2504607 fix(cli): overriding memoized options via --opt (#3401)
• 137caa6 fix: raise more specific error in from_spec (#3447)
• 7208d0b chore: add pandas-stubs as a dev-dependency (#3442)
• 6b430fa feat: support batch inference with Spark (#3425)
• 0058337 fix: load_bento Tag import (#3445)
• 78004a9 chore: add setuptools-scm as dev deps (#3443)
• 8bfdd4f feat: make load_bento take Tag and Bento (#3444)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #36.