Autogluon-cloud aims to provide user tools to train, fine-tune and deploy AutoGluon backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management
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Add kwargs support to `TabularCloudPredictor.predict()` #112
TabularCloudPredictor.predict_real_time() has kwargs support to pass extra arguments to TabularPredictor.predict, ditto for predict_proba.
For example, the user should be able to pass TabularCloudPredictor.predict(..., decision_threshold=0.4) which then calls TabularPredictor.predict(..., decision_threshold=0.4).
[ ] Add kwargs support to TabularCloudPredictor.predict()
[ ] Add kwargs support to TabularCloudPredictor.predict_proba()
TabularCloudPredictor.predict_real_time()haskwargssupport to pass extra arguments toTabularPredictor.predict, ditto forpredict_proba.For example, the user should be able to pass
TabularCloudPredictor.predict(..., decision_threshold=0.4)which then callsTabularPredictor.predict(..., decision_threshold=0.4).TabularCloudPredictor.predict()TabularCloudPredictor.predict_proba()Doc Strings:
https://auto.gluon.ai/cloud/dev/api/autogluon.cloud.TabularCloudPredictor.predict_real_time.html https://auto.gluon.ai/cloud/dev/api/autogluon.cloud.TabularCloudPredictor.predict.html https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.predict.html