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`HyperparameterTuner` drops `ModelTrainer` checkpoint and retry configuration

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@mohamedzeidan2021 ya está trabajando en esto.

Desde el 20/10/2026.

  • #6511 de @mohamedzeidan2021 — abierto

Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
85/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
aws, python
Área
backend-api-design, machine-learning

Línea de trabajo

Empieza en HyperparameterTuner._build_training_job_definition() e inspecciona cómo construye definition_kwargs a partir de ModelTrainer. Compara los campos con HyperParameterTrainingJobDefinition y con las pruebas existentes para las definiciones del tuner. Se considera terminado cuando se conservan los ajustes de checkpoint y reintento si se proporcionan, y se omiten correctamente si faltan, incluido el entrenamiento spot administrado.

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

Descripción

PySDK Version

  • PySDK V2 (2.x)
  • PySDK V3 (3.x)

Describe the bug

HyperparameterTuner builds a
HyperParameterTrainingJobDefinition from its ModelTrainer.

The generated shape supports checkpoint_config and retry_strategy.
However, _build_training_job_definition() does not copy either value from
the trainer.

As a result, tuned training jobs lose:

  • The checkpoint S3 location and local checkpoint path.
  • The training retry strategy.

This behavior is especially risky for managed spot tuning. An interrupted job
cannot resume from the configured checkpoint, and transient failures do not use
the configured retry count.

This defect is present in SageMaker Python SDK v3.25.0.

To reproduce

Run this script. It does not call a live AWS service.

from unittest.mock import patch

import boto3
from sagemaker.core.parameter import IntegerParameter
from sagemaker.core.training.configs import (
    CheckpointConfig,
    Compute,
    OutputDataConfig,
    RetryStrategy,
)
from sagemaker.core.workflow.pipeline_context import PipelineSession
from sagemaker.train import ModelTrainer
from sagemaker.train.tuner import HyperparameterTuner

role = "arn:aws:iam::123456789012:role/example"
boto_session = boto3.Session(
    aws_access_key_id="test",
    aws_secret_access_key="test",
    region_name="us-east-2",
)
pipeline_session = PipelineSession(boto_session=boto_session)

with patch(
    "sagemaker.train.defaults.resolve_and_validate_role",
    return_value=role,
):
    trainer = ModelTrainer(
        training_image="123456789012.dkr.ecr.us-east-2.amazonaws.com/example:latest",
        role=role,
        sagemaker_session=pipeline_session,
        compute=Compute(
            instance_type="ml.m5.large",
            instance_count=1,
            enable_managed_spot_training=True,
        ),
        checkpoint_config=CheckpointConfig(
            s3_uri="s3://example-bucket/checkpoints/",
            local_path="/opt/ml/checkpoints",
        ),
        output_data_config=OutputDataConfig(
            s3_output_path="s3://example-bucket/output/",
        ),
    ).with_retry_strategy(
        RetryStrategy(maximum_retry_attempts=10)
    )

tuner = HyperparameterTuner(
    model_trainer=trainer,
    objective_metric_name="validation:accuracy",
    objective_type="Maximize",
    hyperparameter_ranges={
        "epochs": IntegerParameter(1, 5),
    },
    max_jobs=2,
    max_parallel_jobs=1,
)

definition = tuner._build_training_job_definition([])

print("Trainer checkpoint:", trainer.checkpoint_config)
print("Tuning checkpoint:", definition.checkpoint_config)
print("Trainer retry:", trainer.retry_strategy)
print("Tuning retry:", definition.retry_strategy)

assert definition.checkpoint_config == trainer.checkpoint_config
assert definition.retry_strategy == trainer.retry_strategy

Both assertions fail because the tuning definition fields remain unassigned.

Expected behavior

The tuning job definition must preserve the trainer's checkpoint and retry
configuration.

Screenshots or logs

The generated shape accepts both fields:

HyperParameterTrainingJobDefinition(
    # ...
    checkpoint_config=...,
    retry_strategy=...,
)

HyperparameterTuner._build_training_job_definition() does not pass them in
definition_kwargs.

System information

  • SageMaker Python SDK version: 3.25.0
  • Framework name (eg. PyTorch) or algorithm (eg. KMeans): Framework-independent
  • Framework version: Not applicable
  • Python version: 3.12
  • CPU or GPU: CPU
  • Custom Docker image (Y/N): Reproduces with either

Additional context

A possible fix is to add the assigned trainer values to
definition_kwargs:

if model_trainer.checkpoint_config is not None:
    definition_kwargs["checkpoint_config"] = model_trainer.checkpoint_config

if model_trainer.retry_strategy is not None:
    definition_kwargs["retry_strategy"] = model_trainer.retry_strategy

Tests must cover normal and managed spot trainers, explicit values, and omitted
values.

Lenguaje dominante
Python
Estrellas
2.3k
Forks
1.3k
Merge medio
2 d 16 h
PR fusionados (30 d)
88

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