11 Commits

Author SHA1 Message Date
Jiaming Yuan
f4fb2be101
[jvm-packages] Add the new device parameter. (#9385) 2023-07-17 18:40:39 +08:00
Jiaming Yuan
39390cc2ee
[breaking] Remove the predictor param, allow fallback to prediction using DMatrix. (#9129)
- A `DeviceOrd` struct is implemented to indicate the device. It will eventually replace the `gpu_id` parameter.
- The `predictor` parameter is removed.
- Fallback to `DMatrix` when `inplace_predict` is not available.
- The heuristic for choosing a predictor is only used during training.
2023-07-03 19:23:54 +08:00
Emil Ejbyfeldt
a84a1fde02
[jvm-packages] Update scalatest to 3.2.15 (#8925)
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Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
2023-04-20 22:16:56 +08:00
Jiaming Yuan
f186c87cf9
Check inf in data for all types of DMatrix. (#8911) 2023-03-15 11:24:35 +08:00
Bobby Wang
f1e9bbcee5
[breakinig] [jvm-packages] change DeviceQuantileDmatrix into QuantileDMatrix (#8461) 2022-12-05 12:23:21 +08:00
Philip Hyunsu Cho
2546d139d6
[jvm-packages] Add missing commons-lang3 dependency to xgboost4j-gpu (#8508)
* [jvm-packages] Add missing commons-lang3 dependency to xgboost4j-gpu

* Update commons-lang3
2022-12-01 16:27:11 -08:00
Bobby Wang
78694405a6
[jvm-packages] add jni for setting feature name and type (#7966) 2022-06-03 11:09:48 +08:00
Bobby Wang
686caad40c
[jvm-package] remove the coalesce in barrier mode (#7846) 2022-04-27 23:34:22 +08:00
Bobby Wang
24be04e848
[jvm-packages] Add DeviceQuantileDMatrix to Scala binding (#7459) 2021-11-24 20:23:18 +08:00
Bobby Wang
0ee11dac77
[jvm-packages][xgboost4j-gpu] Support GPU dataframe and DeviceQuantileDMatrix (#7195)
Following classes are added to support dataframe in java binding:

- `Column` is an abstract type for a single column in tabular data.
- `ColumnBatch` is an abstract type for dataframe.

- `CuDFColumn` is an implementaiton of `Column` that consume cuDF column
- `CudfColumnBatch` is an implementation of `ColumnBatch` that consumes cuDF dataframe.

- `DeviceQuantileDMatrix` is the interface for quantized data.

The Java implementation mimics the Python interface and uses `__cuda_array_interface__` protocol for memory indexing.  One difference is on JVM package, the data batch is staged on the host as java iterators cannot be reset.

Co-authored-by: jiamingy <jm.yuan@outlook.com>
2021-09-24 14:25:00 +08:00
Philip Hyunsu Cho
ec6ce08cd0
[jvm-packages] Make it easier to release GPU/CPU code artifacts to Maven Central (#6940) 2021-05-04 14:00:03 -07:00