Comments (4)
Related: #4215
@rey-allan is currently looking into parallel training for Tabular with an identical goal. I think the solution we come up with should be transferable to TimeSeries with relative ease.
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Can't this be simply parallelized with ray.io?
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@jmakov This will require several other modifications. We are not sure which path we will take and the specific timeline for this feature.
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Interesting, thought one would just refactor and add local ray cluster backend: https://github.com/autogluon/autogluon-cloud.
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Related Issues (20)
- Enable linting for tabular and core
- Fix Colab and Kaggle Import on Source Install
- [BUG] resume training HOT 2
- Add LogLoss to the default hyperparameters.
- Custom dataset with pytorch
- [tabular] Fix Dionis memory error in AMLB
- [tabular] Add callbacks tutorial + doc pages to website
- [BUG] Unable to load model from TimmAutoModelForImagePrediction
- [publications] awesome.md vs readme.md, paper proposal: https://arxiv.org/abs/2408.14817 HOT 3
- [BUG] CatBoost ignores `thread_count` parameter HOT 1
- [BUG] feature_prune_kwargs={"force_prune": True} does not work when tuning_data is on for presets="medium_quality", HOT 1
- Contributing to Model Monitoring and Interpretability
- [BUG] Time Series Predictor Model Fit Causing Kernel to Die (Jupyter Notebook) HOT 4
- [tabular] Add unit tests for `valid_stacker` argument
- TabularPredictor.fit - auto_stack documentation
- [tabular] Add stratification for Regression tasks HOT 2
- [Installation] Colab HOT 1
- The bag number each data point belongs to while training
- Fatal Python error: Segmentation fault
- [BUG] refit_every_n_windows doesn't reduce training time for long time series
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