Comments (1)
Thanks very much for using LightGBM.
Based on this report, I strongly suspect this is an issue with SynapseML and not LightGBM. Please report it at https://github.com/microsoft/SynapseML/issues, and link to this issue from there so others searching for a similar issue will be able to find both conversations.
If it does end up being an issue with LightGBM, SynapseML developers can open an issue here reproducing the problem without PySpark and SynapseML involved.
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Related Issues (20)
- Can not predict with multithread? HOT 5
- [ci] CUDA 11.8 wheel (gcc) CI jobs failing: 'libomp.so.5: no such file or directory'
- [GPU] lightgbm.basic.LightGBMError: Check failed: (best_split_info.left_count) > (0), lightgbm.basic.LightGBMError: Check failed: (best_split_info.right_count) > (0)
- [docs] add recommendations on memory management / reducing memory usage
- Clarification on Early Stopping Behavior with Multiple eval_set in LightGBM HOT 2
- Windows 7 runtime error HOT 3
- [python-package] reset_parameter() segfaults when passing an unrecognized parameter HOT 3
- Inquiry about Release Date for GPU Training Bug "bin size 257" FIX HOT 1
- [ci] [R-package] Add a CI job testing the R package on arm64 macOS
- Tests fail to open the file examples/binary_classification/binary.test HOT 1
- [c++] Segmentation fault when use parallel_tree_learner method when number of categories of one feature larger than 28 (default max_cat_to_onehot-4).
- [ci] enforce 'shellcheck' checks
- LightGBM\include\LightGBM/utils/common.h(33,10): fatal error C1083: Cannot open include file: 'fast_double_pa rser.h': No such file or directory HOT 6
- unclear how to prepare data for `lambdarank_position_bias_regularization` option HOT 3
- Integration of lightgbmmodeloptimizer package HOT 4
- [ci] aarch64 python-package job failing: "cannot allocate memory in static TLS block" HOT 15
- [python-package] [gpu] GPU install on Windows fails HOT 2
- [python-package] Training data partially available when loading exported lgb.Dataset HOT 9
- [CUDA] illegal memory access when using CUDA and large max_bin and large dataset HOT 1
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