revert changes from #4000

CMakeLists.txt

@@ -1,4 +1,4 @@
-OPTION(USE_MPI "Enable MPI-based distributed learning" OFF)
+OPTION(USE_MPI "Enable MPI-based parallel learning" OFF)
 OPTION(USE_OPENMP "Enable OpenMP" ON)
 OPTION(USE_GPU "Enable GPU-accelerated training" OFF)
 OPTION(USE_SWIG "Enable SWIG to generate Java API" OFF)

README.md

@@ -40,7 +40,7 @@ Next you may want to read:
 - [**Examples**](https://github.com/microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks.
 - [**Features**](https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM.
 - [**Parameters**](https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.
-- [**Distributed Learning Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
+- [**Parallel Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
 - [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters.
 - [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna/blob/master/examples/)).
 

examples/binary_classification/train.conf

@@ -98,13 +98,13 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 1
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt
 
 # force splits

examples/binary_classification/train_linear.conf

@@ -100,13 +100,13 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 1
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt
 
 # force splits

examples/lambdarank/train.conf

@@ -103,11 +103,11 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 1
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt

examples/parallel_learning/README.md

@@ -1,7 +1,8 @@
 Distributed Learning Example
 ============================
+<a name="parallel-learning-example"></a>
 
-Here is an example for LightGBM to perform distributed learning for 2 machines.
+Here is an example for LightGBM to perform parallel learning for 2 machines.
 
 1. Edit [mlist.txt](./mlist.txt): write the ip of these 2 machines that you want to run application on.
 
@@ -16,6 +17,6 @@ Here is an example for LightGBM to perform distributed learning for 2 machines.
 
    ```"./lightgbm" config=train.conf```
 
-This distributed learning example is based on socket. LightGBM also supports distributed learning based on mpi.
+This parallel learning example is based on socket. LightGBM also supports parallel learning based on mpi.
 
-For more details about the usage of distributed learning, please refer to [this](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst).
+For more details about the usage of parallel learning, please refer to [this](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst).

examples/parallel_learning/train.conf

@@ -98,11 +98,11 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 2
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt

examples/regression/train.conf

@@ -101,11 +101,11 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 1
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt

examples/xendcg/train.conf

@@ -104,11 +104,11 @@ output_model = LightGBM_model.txt
 # output_result= prediction.txt
 
 
-# number of machines in distributed training, alias: num_machine
+# number of machines in parallel training, alias: num_machine
 num_machines = 1
 
-# local listening port in distributed training, alias: local_port
+# local listening port in parallel training, alias: local_port
 local_listen_port = 12400
 
-# machines list file for distributed training, alias: mlist
+# machines list file for parallel training, alias: mlist
 machine_list_file = mlist.txt

include/LightGBM/config.h

@@ -200,7 +200,7 @@ struct Config {
   // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel``
   // desc = ``data``, data parallel tree learner, aliases: ``data_parallel``
   // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel``
-  // desc = refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details
+  // desc = refer to `Parallel Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details
   std::string tree_learner = "serial";
 
   // alias = num_thread, nthread, nthreads, n_jobs
@@ -209,7 +209,7 @@ struct Config {
   // desc = for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading <https://en.wikipedia.org/wiki/Hyper-threading>`__ to generate 2 threads per CPU core)
   // desc = do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)
   // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal**
-  // desc = for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication
+  // desc = for parallel learning, do not use all CPU cores because this will cause poor performance for the network communication
   // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
   int num_threads = 0;
 
@@ -634,7 +634,7 @@ struct Config {
   bool feature_pre_filter = true;
 
   // alias = is_pre_partition
-  // desc = used for distributed learning (excluding the ``feature_parallel`` mode)
+  // desc = used for parallel learning (excluding the ``feature_parallel`` mode)
   // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions
   bool pre_partition = false;
 
@@ -961,7 +961,7 @@ struct Config {
 
   // check = >0
   // alias = num_machine
-  // desc = the number of machines for distributed learning application
+  // desc = the number of machines for parallel learning application
   // desc = this parameter is needed to be set in both **socket** and **mpi** versions
   int num_machines = 1;
 
@@ -976,7 +976,7 @@ struct Config {
   int time_out = 120;
 
   // alias = machine_list_file, machine_list, mlist
-  // desc = path of file that lists machines for this distributed learning application
+  // desc = path of file that lists machines for this parallel learning application
   // desc = each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)
   // desc = **Note**: can be used only in CLI version
   std::string machine_list_filename = "";

include/LightGBM/dataset.h

@@ -80,7 +80,7 @@ class Metadata {
 
   /*!
   * \brief Partition meta data according to local used indices if need
-  * \param num_all_data Number of total training data, including other machines' data on distributed learning
+  * \param num_all_data Number of total training data, including other machines' data on parallel learning
   * \param used_data_indices Indices of local used training data
   */
   void CheckOrPartition(data_size_t num_all_data,

python-package/lightgbm/basic.py

@@ -2333,7 +2333,7 @@ def set_network(self, machines, local_listen_port=12400,
         listen_time_out : int, optional (default=120)
             Socket time-out in minutes.
         num_machines : int, optional (default=1)
-            The number of machines for distributed learning application.
+            The number of machines for parallel learning application.
 
         Returns
         -------

src/application/application.cpp

@@ -105,7 +105,7 @@ void Application::LoadData() {
                                config_.num_class, config_.data.c_str());
   // load Training data
   if (config_.is_data_based_parallel) {
-    // load data for distributed training
+    // load data for parallel training
     train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(),
                                                   Network::rank(), Network::num_machines()));
   } else {

src/io/config.cpp

@@ -374,7 +374,7 @@ void Config::CheckParamConflict() {
   }
   if (is_parallel && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) {
     // In distributed mode, local node doesn't have histograms on all features, cannot perform "intermediate" monotone constraints.
-    Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints in distributed learning, auto set to \"basic\" method.");
+    Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints in parallel learning, auto set to \"basic\" method.");
     monotone_constraints_method = "basic";
   }
   if (feature_fraction_bynode != 1.0 && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) {

src/io/dataset_loader.cpp

@@ -183,7 +183,7 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
   // don't support query id in data file when training in parallel
   if (num_machines > 1 && !config_.pre_partition) {
     if (group_idx_ > 0) {
-      Log::Fatal("Using a query id without pre-partitioning the data file is not supported for distributed training.\n"
+      Log::Fatal("Using a query id without pre-partitioning the data file is not supported for parallel training.\n"
                  "Please use an additional query file or pre-partition the data");
     }
   }

src/io/metadata.cpp

@@ -22,7 +22,7 @@ Metadata::Metadata() {
 
 void Metadata::Init(const char* data_filename) {
   data_filename_ = data_filename;
-  // for lambdarank, it needs query data for partition data in distributed learning
+  // for lambdarank, it needs query data for partition data in parallel learning
   LoadQueryBoundaries();
   LoadWeights();
   LoadQueryWeights();
@@ -187,7 +187,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
     }
   } else {
     if (!queries_.empty()) {
-      Log::Fatal("Cannot used query_id for distributed training");
+      Log::Fatal("Cannot used query_id for parallel training");
     }
     data_size_t num_used_data = static_cast<data_size_t>(used_data_indices.size());
     // check weights