Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

[Bug]: [RunInference] max_models_per_worker_hint is not enforced

Abierto
  • ¿Algo no está bien?
#40,468 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
48/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
backend

Línea de trabajo

Start in sdks/python/apache_beam/ml/inference/base.py around the max-workers logic at lines 852–857, and trace how locks and deserialized RunInference handlers interact with the shared _ModelHandlerManager. Use the issue’s reproduction as a starting point and check existing Python SDK inference tests. Done when the model limit remains at the configured hint across multiple handler copies and the regression is covered by a test.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

awaiting triage bug P2 python
What happened?

The intent of max_models_per_worker hint is to limit the number of models that will be loaded per SDK process.

The logic to increment allowed max number of workers https://github.com/apache/beam/blob/dabcf50ffbf532bb30a1233927cd1edb6ae067bb/sdks/python/apache_beam/ml/inference/base.py#L852-L857 appears to be flawed since:

  1. The lock acquisition always succeeds (it's a new lock instance)
  2. If there is more than 1 process bundle descriptor over the life time of the SDK process, we might have more than 1 copy of the deserialized RunInference DoFn with unpickled ModelHandler instance, which won't persist self._max_models_per_worker_hint = None from a prior initialization:

AI repro:

from apache_beam.internal import pickler
from apache_beam.ml.inference import base
class Model:
  def predict(self, x):
    return x
class Handler(base.ModelHandler):
  def load_model(self):
    return Model()
  def run_inference(self, batch, model, inference_args=None):
    return [model.predict(x) for x in batch]
mhs = [base.KeyModelMapping([k], Handler()) for k in ('a', 'b', 'c')]
keyed_handler = base.KeyedModelHandler(mhs, max_models_per_worker_hint=1)
# RunInference shares one _ModelHandlerManager per transform across all
# DoFn instances (and processes) via MultiProcessShared.
manager = keyed_handler.load_model()
# 5 DoFn instances in one process (harness threads, re-created bundle
# processors); each deserializes its own copy of the model handler.
for _ in range(5):
  handler_copy = pickler.roundtrip(keyed_handler)
  handler_copy.override_metrics('ns')
  handler_copy.run_inference([('a', 1), ('b', 2), ('c', 3)], manager)
print('model limit:', manager._max_models)  # 5, expected 1
print('models in memory:', len(manager._tag_map))  # 3
Issue Priority

Priority: 2 (default / most bugs should be filed as P2)

Issue Components
  • Component: Python SDK
  • Component: Java SDK
  • Component: Go SDK
  • Component: Typescript SDK
  • Component: IO connector
  • Component: Beam YAML
  • Component: Beam examples
  • Component: Beam playground
  • Component: Beam katas
  • Component: Website
  • Component: Infrastructure
  • Component: Spark Runner
  • Component: Flink Runner
  • Component: Prism Runner
  • Component: Twister2 Runner
  • Component: Hazelcast Jet Runner
  • Component: Google Cloud Dataflow Runner
Lenguaje dominante
Java
Estrellas
8.7k
Forks
4.7k
Merge medio
2 d 8 h
PR fusionados (30 d)
246

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de apache/beam

Todos los issues de apache/beam

Issues similares

Más issues de Java

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.