Comments (3)
@everpeace thanks for your interest in SageMaker!
Adding more frameworks and clearer support for user-defined frameworks is definitely on our roadmap for the near future. We are also considering open-sourcing the container-side code, and we will take your feedback into account as we do so.
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@jesterhazy Thanks for quick reply :-) Wow, that's a good news. I'm looking forward to it!
So, I'm closing this issue for now. If you would make this issue feedback-tracking issue, please re-open this.
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we use DarkNet for video analysis and pyAudioanalysis for audio model training. I'm looking forward how we can integrate these framework into SageMaker.
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Related Issues (20)
- Update documentation to use `sagemaker-python-sdk` as opposed to `sagemaker` HOT 3
- Fix documentation of FeatureGroup HOT 1
- Sagemaker Local-Mode permission errors
- ModelStep name includes "-RegisterModel" suffix HOT 1
- Tags are't inherited from the `sagemaker_config` to hyperparameter tuning requests
- ValueError: Invalid explanations value when creating ModelCard from ModelPackage
- AsyncPredictor fails if name is None, despite it being the default
- Newline chars are not supported in sagemaker feature store string types
- SageMaker Batch currently doesn't support Model entity with container definitions which use ModelDataSource attribute
- 'base_job_name' parameter is not used to name the Processing Jobs that are created by the Model Monitoring Schedule
- (AutoMLV2) UnboundLocalError: local variable 'input_config' referenced before assignment
- Import/get data from bigquery
- Endless loop when try to deploy a sagemaker pipeline from a windows machine HOT 2
- HuggingFaceProcessor with ProcessingStep results in import errors (similar to issues/2656) HOT 3
- Error in using Clarify with AWS Jumpstart image classification models
- Forbidden Access to S3 bucket after domain initialization
- Can I create lambda step from ecr image by using Lambda class in lambda_help.py?
- sagemaker.model_monitor.DefaultModelMonitor suggest_baseline is not able to read Japanese text HOT 2
- Model.is_repack should return a bool but sometimes returns a None
- Allow user to configure compresslevel for tarfile in sagemaker.util.repack_model
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