Comments (1)
It seems that a metric definition has to be supplied in sagemaker.tuner.HyperparameterTuner
instead of the estimator. Thanks AWS support for providing the solution.
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Related Issues (20)
- Request for Enabling `include_local_workdir` Parameter Setting with `@step` Decorator in Local Mode HOT 2
- Documentation on data dependency for ModelStep
- Remove duplicate code between MultiModelServer and TorchServer
- Receiving configuration error when loading SageMaker Session HOT 1
- Extra quotation marks from training job description
- NameContains doesn't work in list_training_jobs
- Documentation of @step decorator HOT 2
- 'LocalSagemakerClient' object has no attribute 'describe_user_profile'
- [Bug Report] Unable to use Join with pipeline parameters within ConditionGreaterThanOrEqualTo HOT 4
- KeyError: 'DefaultSpaceSettings' when calling sagemaker.get_execution_role()
- @step decorator import doesn't work HOT 4
- enable_network_isolation documented but not implemented in remote jobs. HOT 1
- ModelExplainabilityMonitor cannot execute one-time monitoring job due to lack of `data_analysis_start_time`/`data_analysis_end_time` in `_create_monitoring_schedule_from_job_definition`
- [Feature request]: Selectively execute a pipeline step which has upstream dependencies
- Feature Store Iceberg DataSource not implemented
- PyTorch Estimator `max_run` parameter not working at all
- Unclosed files in `sagemaker.config` HOT 1
- More unclosed files in source and tests
- How can I use the custom generator to train a TensorFlow model on SageMaker? HOT 1
- Initialization of default JumpStart SageMaker Session is slow HOT 1
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