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Enable running objectives with 0 GPU. (#3878)

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* Enable running objectives with 0 GPU.

* Enable 0 GPU for objectives.
* Add doc for GPU objectives.
* Fix some objectives defaulted to running on all GPUs.
This commit is contained in:
Jiaming Yuan
2018-11-13 20:19:59 +13:00
committed by GitHub
parent 97984f4890
commit daf77ca7b7
6 changed files with 56 additions and 22 deletions
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7
doc/parameter.rst
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@@ -245,8 +245,8 @@ Parameters for Linear Booster (``booster=gblinear``)
- Choice of algorithm to fit linear model
- ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
- ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
- ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
- ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
* ``feature_selector`` [default= ``cyclic``]
@@ -283,9 +283,6 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``binary:logistic``: logistic regression for binary classification, output probability
- ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
- ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
- ``gpu:reg:linear``, ``gpu:reg:logistic``, ``gpu:binary:logistic``, ``gpu:binary:logitraw``: versions
of the corresponding objective functions evaluated on the GPU; note that like the GPU histogram algorithm,
they can only be used when the entire training session uses the same dataset
- ``count:poisson`` --poisson regression for count data, output mean of poisson distribution
- ``max_delta_step`` is set to 0.7 by default in poisson regression (used to safeguard optimization)