e089e16e3d
* Pass pointer to model parameters. This PR de-duplicates most of the model parameters except the one in `tree_model.h`. One difficulty is `base_score` is a model property but can be changed at runtime by objective function. Hence when performing model IO, we need to save the one provided by users, instead of the one transformed by objective. Here we created an immutable version of `LearnerModelParam` that represents the value of model parameter after configuration.
89 lines
2.7 KiB
C++
89 lines
2.7 KiB
C++
/*!
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* Copyright 2014-2019 by Contributors
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* \file learner.cc
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*/
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#ifndef XGBOOST_GENERIC_PARAMETERS_H_
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#define XGBOOST_GENERIC_PARAMETERS_H_
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#include <xgboost/logging.h>
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#include <xgboost/parameter.h>
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#include <string>
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namespace xgboost {
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struct GenericParameter : public XGBoostParameter<GenericParameter> {
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// Constant representing the device ID of CPU.
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static int32_t constexpr kCpuId = -1;
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public:
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// stored random seed
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int seed;
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// whether seed the PRNG each iteration
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bool seed_per_iteration;
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// number of threads to use if OpenMP is enabled
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// if equals 0, use system default
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int nthread;
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// primary device, -1 means no gpu.
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int gpu_id;
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// gpu page size in external memory mode, 0 means using the default.
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size_t gpu_page_size;
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bool enable_experimental_json_serialization {false};
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void CheckDeprecated() {
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if (this->n_gpus != 0) {
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LOG(WARNING)
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<< "\nn_gpus: "
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<< this->__MANAGER__()->Find("n_gpus")->GetFieldInfo().description;
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}
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}
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/*!
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* \brief Configure the parameter `gpu_id'.
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*
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* \param require_gpu Whether GPU is explicitly required from user.
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*/
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void ConfigureGpuId(bool require_gpu);
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// declare parameters
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DMLC_DECLARE_PARAMETER(GenericParameter) {
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DMLC_DECLARE_FIELD(seed).set_default(0).describe(
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"Random number seed during training.");
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DMLC_DECLARE_ALIAS(seed, random_state);
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DMLC_DECLARE_FIELD(seed_per_iteration)
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.set_default(false)
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.describe(
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"Seed PRNG determnisticly via iterator number, "
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"this option will be switched on automatically on distributed "
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"mode.");
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DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
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"Number of threads to use.");
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DMLC_DECLARE_ALIAS(nthread, n_jobs);
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DMLC_DECLARE_FIELD(gpu_id)
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.set_default(-1)
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.set_lower_bound(-1)
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.describe("The primary GPU device ordinal.");
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DMLC_DECLARE_FIELD(gpu_page_size)
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.set_default(0)
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.set_lower_bound(0)
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.describe("GPU page size when running in external memory mode.");
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DMLC_DECLARE_FIELD(enable_experimental_json_serialization)
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.set_default(false)
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.describe("Enable using JSON for memory serialization (Python Pickle, "
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"rabit checkpoints etc.).");
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DMLC_DECLARE_FIELD(n_gpus)
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.set_default(0)
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.set_range(0, 1)
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.describe(
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"\n\tDeprecated. Single process multi-GPU training is no longer supported."
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"\n\tPlease switch to distributed training with one process per GPU."
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"\n\tThis can be done using Dask or Spark. See documentation for details.");
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}
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private:
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// number of devices to use (deprecated).
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int n_gpus {0};
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};
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} // namespace xgboost
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#endif // XGBOOST_GENERIC_PARAMETERS_H_
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