@@ -38,9 +38,7 @@ How to Achieve Good Speedup on GPU
Also make sure your system is idle (especially when using a shared computer) to get accuracy performance measurements.
#. GPU works best on large scale and dense datasets. If dataset is too small, computing it on GPU is inefficient as the data transfer overhead can be significant.
For dataset with a mixture of sparse and dense features, you can control the ``sparse_threshold`` parameter to make sure there are enough dense features to process on the GPU.
If you have categorical features, use the ``categorical_column`` option and input them into LightGBM directly; do not convert them into one-hot variables.
Make sure to check the run log and look at the reported number of sparse and dense features.
#. To get good speedup with GPU, it is suggested to use a smaller number of bins.
Setting ``max_bin=63`` is recommended, as it usually does not noticeably affect training accuracy on large datasets, but GPU training can be significantly faster than using the default bin size of 255.
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@@ -119,14 +117,13 @@ The following shows the training configuration we used:
min_data_in_leaf = 1
min_sum_hessian_in_leaf = 100
ndcg_eval_at = 1,3,5,10
sparse_threshold=1.0
device = gpu
gpu_platform_id = 0
gpu_device_id = 0
num_thread = 28
We use the configuration shown above, except for the Bosch dataset, we use a smaller ``learning_rate=0.015`` and set ``min_sum_hessian_in_leaf=5``.
For all GPU training we set ``sparse_threshold=1``, and vary the max number of bins (255, 63 and 15).
For all GPU training we vary the max number of bins (255, 63 and 15).
The GPU implementation is from commit `0bb4a82`_ of LightGBM, when the GPU support was just merged in.
The following table lists the accuracy on test set that CPU and GPU learner can achieve after 500 iterations.
