Comments (1)
I do see the option to include repositoryaccessmode in create_model but not in create_hyper_parameter_tuning_job. However, I am assuming that create_model you would need to freeze the hyperparameters as compared to parsing the args for create_hyper_parameter_tuning_job.
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Related Issues (20)
- Unable to Install This Repo Locally HOT 1
- Metrics Not Being Logged In Distributed Mode HOT 2
- TypeError: predict() got an unexpected keyword argument 'pred_contribs' with xgboost v0.90
- Need support for XGBoost 1.5.2 HOT 2
- How to pass qid/group in XGBRanker.fit() ??
- Upgrade to multi-model-server 1.1.8
- latest image and monotonicity constraint HOT 2
- n_estimator parameter? HOT 1
- XGB train call failed with exception: You can't mix new and old callback styles HOT 13
- Need support for XGBoost-1.6 (Multi-output Regression) HOT 3
- Sagemaker endpoint invocation returns empty body HOT 2
- IndexError on endpoint deployment HOT 4
- Can not load models pulled from ML Flow
- XGBoost 1.7 multi-instance GPU training with Dask - NCCL error
- Can you please add shapley to the requirements? HOT 1
- 'XGBRegressor' object has no attribute 'set_param' | AttributeError: 'XGBRegressor' object has no attribute 'set_param' HOT 1
- application/json is not an accepted on tag 1.5-1 HOT 1
- Upgrade to numpy>=1.20 for typing support
- One less column in inference set does not throw an exception when it should
- Support Python 3.11
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