Refactor the CLI. (#5574)

* Enable parameter validation.
* Enable JSON.
* Catch `dmlc::Error`.
* Show help message.

doc/parameter.rst

@@ -30,11 +30,11 @@ General Parameters
     is displayed as warning message.  If there's unexpected behaviour, please try to
     increase value of verbosity.
 
-* ``validate_parameters`` [default to false, except for Python and R interface]
+* ``validate_parameters`` [default to false, except for Python, R and CLI interface]
 
   - When set to True, XGBoost will perform validation of input parameters to check whether
     a parameter is used or not.  The feature is still experimental.  It's expected to have
-    some false positives, especially when used with Scikit-Learn interface.
+    some false positives.
 
 * ``nthread`` [default to maximum number of threads available if not set]
 

src/cli_main.cc

@@ -4,28 +4,29 @@
  * \brief The command line interface program of xgboost.
  *  This file is not included in dynamic library.
  */
-// Copyright 2014 by Contributors
 #define _CRT_SECURE_NO_WARNINGS
 #define _CRT_SECURE_NO_DEPRECATE
 #define NOMINMAX
+#include <dmlc/timer.h>
 
 #include <xgboost/learner.h>
 #include <xgboost/data.h>
+#include <xgboost/json.h>
 #include <xgboost/logging.h>
 #include <xgboost/parameter.h>
 
-#include <dmlc/timer.h>
 #include <iomanip>
 #include <ctime>
 #include <string>
 #include <cstdio>
 #include <cstring>
 #include <vector>
-#include "./common/common.h"
+#include "common/common.h"
-#include "./common/config.h"
+#include "common/config.h"
+#include "common/io.h"
+#include "common/version.h"
 
 namespace xgboost {
-
 enum CLITask {
   kTrain = 0,
   kDumpModel = 1,
@@ -74,6 +75,8 @@ struct CLIParam : public XGBoostParameter<CLIParam> {
   /*! \brief all the configurations */
   std::vector<std::pair<std::string, std::string> > cfg;
 
+  static constexpr char const* const kNull = "NULL";
+
   // declare parameters
   DMLC_DECLARE_PARAMETER(CLIParam) {
     // NOTE: declare everything except eval_data_paths.
@@ -124,15 +127,18 @@ struct CLIParam : public XGBoostParameter<CLIParam> {
   }
   // customized configure function of CLIParam
   inline void Configure(const std::vector<std::pair<std::string, std::string> >& _cfg) {
-    this->cfg = _cfg;
+    // Don't copy the configuration to enable parameter validation.
-    this->UpdateAllowUnknown(_cfg);
+    auto unknown_cfg = this->UpdateAllowUnknown(_cfg);
-    for (const auto& kv : _cfg) {
+    this->cfg.emplace_back("validate_parameters", "True");
+    for (const auto& kv : unknown_cfg) {
       if (!strncmp("eval[", kv.first.c_str(), 5)) {
         char evname[256];
         CHECK_EQ(sscanf(kv.first.c_str(), "eval[%[^]]", evname), 1)
             << "must specify evaluation name for display";
         eval_data_names.emplace_back(evname);
         eval_data_paths.push_back(kv.second);
+      } else {
+        this->cfg.emplace_back(kv);
       }
     }
     // constraint.
@@ -145,221 +151,376 @@ struct CLIParam : public XGBoostParameter<CLIParam> {
   }
 };
 
+constexpr char const* const CLIParam::kNull;
+
 DMLC_REGISTER_PARAMETER(CLIParam);
 
-void CLITrain(const CLIParam& param) {
+std::string CliHelp() {
-  const double tstart_data_load = dmlc::GetTime();
+  return "Use xgboost -h for showing help information.\n";
-  if (rabit::IsDistributed()) {
+}
-    std::string pname = rabit::GetProcessorName();
-    LOG(CONSOLE) << "start " << pname << ":" << rabit::GetRank();
-  }
-  // load in data.
-  std::shared_ptr<DMatrix> dtrain(
-      DMatrix::Load(
-          param.train_path,
-          ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
-          param.dsplit == 2));
-  std::vector<std::shared_ptr<DMatrix> > deval;
-  std::vector<std::shared_ptr<DMatrix> > cache_mats;
-  std::vector<std::shared_ptr<DMatrix>> eval_datasets;
-  cache_mats.push_back(dtrain);
-  for (size_t i = 0; i < param.eval_data_names.size(); ++i) {
-    deval.emplace_back(
-        std::shared_ptr<DMatrix>(DMatrix::Load(
-            param.eval_data_paths[i],
-            ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
-            param.dsplit == 2)));
-    eval_datasets.push_back(deval.back());
-    cache_mats.push_back(deval.back());
-  }
-  std::vector<std::string> eval_data_names = param.eval_data_names;
-  if (param.eval_train) {
-    eval_datasets.push_back(dtrain);
-    eval_data_names.emplace_back("train");
-  }
-  // initialize the learner.
-  std::unique_ptr<Learner> learner(Learner::Create(cache_mats));
-  int version = rabit::LoadCheckPoint(learner.get());
-  if (version == 0) {
-    // initialize the model if needed.
-    if (param.model_in != "NULL") {
-      std::unique_ptr<dmlc::Stream> fi(
-          dmlc::Stream::Create(param.model_in.c_str(), "r"));
-      learner->LoadModel(fi.get());
-      learner->SetParams(param.cfg);
-    } else {
-      learner->SetParams(param.cfg);
-    }
-  }
-  LOG(INFO) << "Loading data: " << dmlc::GetTime() - tstart_data_load << " sec";
 
-  // start training.
+void CLIError(dmlc::Error const& e) {
-  const double start = dmlc::GetTime();
+  std::cerr << "Error running xgboost:\n\n"
-  for (int i = version / 2; i < param.num_round; ++i) {
+            << e.what() << "\n"
-    double elapsed = dmlc::GetTime() - start;
+            << CliHelp()
-    if (version % 2 == 0) {
+            << std::endl;
-      LOG(INFO) << "boosting round " << i << ", " << elapsed << " sec elapsed";
+}
-      learner->UpdateOneIter(i, dtrain);
+
-      if (learner->AllowLazyCheckPoint()) {
+class CLI {
-        rabit::LazyCheckPoint(learner.get());
+  CLIParam param_;
+  std::unique_ptr<Learner> learner_;
+  enum Print {
+    kNone,
+    kVersion,
+    kHelp
+  } print_info_ {kNone};
+
+  int ResetLearner(std::vector<std::shared_ptr<DMatrix>> const &matrices) {
+    learner_.reset(Learner::Create(matrices));
+    int version = rabit::LoadCheckPoint(learner_.get());
+    if (version == 0) {
+      if (param_.model_in != CLIParam::kNull) {
+        this->LoadModel(param_.model_in, learner_.get());
+        learner_->SetParams(param_.cfg);
       } else {
-        rabit::CheckPoint(learner.get());
+        learner_->SetParams(param_.cfg);
+      }
+    }
+    learner_->Configure();
+    return version;
+  }
+
+  void CLITrain() {
+    const double tstart_data_load = dmlc::GetTime();
+    if (rabit::IsDistributed()) {
+      std::string pname = rabit::GetProcessorName();
+      LOG(CONSOLE) << "start " << pname << ":" << rabit::GetRank();
+    }
+    // load in data.
+    std::shared_ptr<DMatrix> dtrain(DMatrix::Load(
+        param_.train_path,
+        ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
+        param_.dsplit == 2));
+    std::vector<std::shared_ptr<DMatrix>> deval;
+    std::vector<std::shared_ptr<DMatrix>> cache_mats;
+    std::vector<std::shared_ptr<DMatrix>> eval_datasets;
+    cache_mats.push_back(dtrain);
+    for (size_t i = 0; i < param_.eval_data_names.size(); ++i) {
+      deval.emplace_back(std::shared_ptr<DMatrix>(DMatrix::Load(
+          param_.eval_data_paths[i],
+          ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
+          param_.dsplit == 2)));
+      eval_datasets.push_back(deval.back());
+      cache_mats.push_back(deval.back());
+    }
+    std::vector<std::string> eval_data_names = param_.eval_data_names;
+    if (param_.eval_train) {
+      eval_datasets.push_back(dtrain);
+      eval_data_names.emplace_back("train");
+    }
+    // initialize the learner.
+    int32_t version = this->ResetLearner(cache_mats);
+    LOG(INFO) << "Loading data: " << dmlc::GetTime() - tstart_data_load
+              << " sec";
+
+    // start training.
+    const double start = dmlc::GetTime();
+    for (int i = version / 2; i < param_.num_round; ++i) {
+      double elapsed = dmlc::GetTime() - start;
+      if (version % 2 == 0) {
+        LOG(INFO) << "boosting round " << i << ", " << elapsed
+                  << " sec elapsed";
+        learner_->UpdateOneIter(i, dtrain);
+        if (learner_->AllowLazyCheckPoint()) {
+          rabit::LazyCheckPoint(learner_.get());
+        } else {
+          rabit::CheckPoint(learner_.get());
+        }
+        version += 1;
+      }
+      CHECK_EQ(version, rabit::VersionNumber());
+      std::string res = learner_->EvalOneIter(i, eval_datasets, eval_data_names);
+      if (rabit::IsDistributed()) {
+        if (rabit::GetRank() == 0) {
+          LOG(TRACKER) << res;
+        }
+      } else {
+        LOG(CONSOLE) << res;
+      }
+      if (param_.save_period != 0 && (i + 1) % param_.save_period == 0 &&
+          rabit::GetRank() == 0) {
+        std::ostringstream os;
+        os << param_.model_dir << '/' << std::setfill('0') << std::setw(4)
+           << i + 1 << ".model";
+        this->SaveModel(os.str(), learner_.get());
+      }
+
+      if (learner_->AllowLazyCheckPoint()) {
+        rabit::LazyCheckPoint(learner_.get());
+      } else {
+        rabit::CheckPoint(learner_.get());
       }
       version += 1;
+      CHECK_EQ(version, rabit::VersionNumber());
     }
-    CHECK_EQ(version, rabit::VersionNumber());
+    LOG(INFO) << "Complete Training loop time: " << dmlc::GetTime() - start
-    std::string res = learner->EvalOneIter(i, eval_datasets, eval_data_names);
+              << " sec";
-    if (rabit::IsDistributed()) {
+    // always save final round
-      if (rabit::GetRank() == 0) {
+    if ((param_.save_period == 0 ||
-        LOG(TRACKER) << res;
+         param_.num_round % param_.save_period != 0) &&
-      }
+        param_.model_out != CLIParam::kNull && rabit::GetRank() == 0) {
-    } else {
-      LOG(CONSOLE) << res;
-    }
-    if (param.save_period != 0 &&
-        (i + 1) % param.save_period == 0 &&
-        rabit::GetRank() == 0) {
       std::ostringstream os;
-      os << param.model_dir << '/'
+      if (param_.model_out == CLIParam::kNull) {
-         << std::setfill('0') << std::setw(4)
+        os << param_.model_dir << '/' << std::setfill('0') << std::setw(4)
-         << i + 1 << ".model";
+           << param_.num_round << ".model";
-      std::unique_ptr<dmlc::Stream> fo(
+      } else {
-          dmlc::Stream::Create(os.str().c_str(), "w"));
+        os << param_.model_out;
+      }
+      this->SaveModel(os.str(), learner_.get());
+    }
+
+    double elapsed = dmlc::GetTime() - start;
+    LOG(INFO) << "update end, " << elapsed << " sec in all";
+  }
+
+  void CLIDumpModel() {
+    FeatureMap fmap;
+    if (param_.name_fmap != CLIParam::kNull) {
+      std::unique_ptr<dmlc::Stream> fs(
+          dmlc::Stream::Create(param_.name_fmap.c_str(), "r"));
+      dmlc::istream is(fs.get());
+      fmap.LoadText(is);
+    }
+    // load model
+    CHECK_NE(param_.model_in, CLIParam::kNull) << "Must specify model_in for dump";
+    this->ResetLearner({});
+
+    // dump data
+    std::vector<std::string> dump =
+        learner_->DumpModel(fmap, param_.dump_stats, param_.dump_format);
+    std::unique_ptr<dmlc::Stream> fo(
+        dmlc::Stream::Create(param_.name_dump.c_str(), "w"));
+    dmlc::ostream os(fo.get());
+    if (param_.dump_format == "json") {
+      os << "[" << std::endl;
+      for (size_t i = 0; i < dump.size(); ++i) {
+        if (i != 0) {
+          os << "," << std::endl;
+        }
+        os << dump[i];  // Dump the previously generated JSON here
+      }
+      os << std::endl << "]" << std::endl;
+    } else {
+      for (size_t i = 0; i < dump.size(); ++i) {
+        os << "booster[" << i << "]:\n";
+        os << dump[i];
+      }
+    }
+    // force flush before fo destruct.
+    os.set_stream(nullptr);
+  }
+
+  void CLIPredict() {
+    CHECK_NE(param_.test_path, CLIParam::kNull)
+        << "Test dataset parameter test:data must be specified.";
+    // load data
+    std::shared_ptr<DMatrix> dtest(DMatrix::Load(
+        param_.test_path,
+        ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
+        param_.dsplit == 2));
+    // load model
+    CHECK_NE(param_.model_in, CLIParam::kNull) << "Must specify model_in for predict";
+    this->ResetLearner({});
+
+    LOG(INFO) << "Start prediction...";
+    HostDeviceVector<bst_float> preds;
+    learner_->Predict(dtest, param_.pred_margin, &preds, param_.ntree_limit);
+    LOG(CONSOLE) << "Writing prediction to " << param_.name_pred;
+
+    std::unique_ptr<dmlc::Stream> fo(
+        dmlc::Stream::Create(param_.name_pred.c_str(), "w"));
+    dmlc::ostream os(fo.get());
+    for (bst_float p : preds.ConstHostVector()) {
+      os << std::setprecision(std::numeric_limits<bst_float>::max_digits10) << p
+         << '\n';
+    }
+    // force flush before fo destruct.
+    os.set_stream(nullptr);
+  }
+
+  void LoadModel(std::string const& path, Learner* learner) const {
+    if (common::FileExtension(path) == "json") {
+      auto str = common::LoadSequentialFile(path);
+      CHECK_GT(str.size(), 2);
+      CHECK_EQ(str[0], '{');
+      Json in{Json::Load({str.c_str(), str.size()})};
+      learner->LoadModel(in);
+    } else {
+      std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(path.c_str(), "r"));
+      learner->LoadModel(fi.get());
+    }
+  }
+
+  void SaveModel(std::string const& path, Learner* learner) const {
+    learner->Configure();
+    std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(path.c_str(), "w"));
+    if (common::FileExtension(path) == "json") {
+      Json out{Object()};
+      learner->SaveModel(&out);
+      std::string str;
+      Json::Dump(out, &str);
+      fo->Write(str.c_str(), str.size());
+    } else {
       learner->SaveModel(fo.get());
     }
+  }
 
-    if (learner->AllowLazyCheckPoint()) {
+  void PrintHelp() const {
-      rabit::LazyCheckPoint(learner.get());
+    std::cout << "Usage: xgboost [ -h ] [ -V ] [ config file ] [ arguments ]" << std::endl;
-    } else {
+    std::stringstream ss;
-      rabit::CheckPoint(learner.get());
+    ss << R"(
+  Options and arguments:
+
+    -h, --help
+       Print this message.
+
+    -V, --version
+       Print XGBoost version.
+
+    arguments
+       Extra parameters that are not specified in config file, see below.
+
+  Config file specifies the configuration for both training and testing.  Each line
+  containing the [attribute] = [value] configuration.
+
+  General XGBoost parameters:
+
+    https://xgboost.readthedocs.io/en/latest/parameter.html
+
+  Command line interface specfic parameters:
+
+)";
+
+    std::string help = param_.__DOC__();
+    auto splited = common::Split(help, '\n');
+    for (auto str : splited) {
+      ss << "    " << str << '\n';
     }
-    version += 1;
+    ss << R"(    eval[NAME]: string, optional, default='NULL'
-    CHECK_EQ(version, rabit::VersionNumber());
+        Path to evaluation data, with NAME as data name.
+)";
+
+    ss << R"(
+  Example:  train.conf
+
+    # General parameters
+    booster = gbtree
+    objective = reg:squarederror
+    eta = 1.0
+    gamma = 1.0
+    seed = 0
+    min_child_weight = 0
+    max_depth = 3
+
+    # Training arguments for CLI.
+    num_round = 2
+    save_period = 0
+    data = "demo/data/agaricus.txt.train?format=libsvm"
+    eval[test] = "demo/data/agaricus.txt.test?format=libsvm"
+
+  See demo/ directory in XGBoost for more examples.
+)";
+    std::cout << ss.str() << std::endl;
   }
-  LOG(INFO) << "Complete Training loop time: " << dmlc::GetTime() - start << " sec";
+
-  // always save final round
+  void PrintVersion() const {
-  if ((param.save_period == 0 || param.num_round % param.save_period != 0) &&
+    auto ver = Version::String(Version::Self());
-      param.model_out != "NONE" &&
+    std::cout << "XGBoost: " << ver << std::endl;
-      rabit::GetRank() == 0) {
+  }
-    std::ostringstream os;
+
-    if (param.model_out == "NULL") {
+ public:
-      os << param.model_dir << '/'
+  CLI(int argc, char* argv[]) {
-         << std::setfill('0') << std::setw(4)
+    if (argc < 2) {
-         << param.num_round << ".model";
+      this->PrintHelp();
-    } else {
+      exit(1);
-      os << param.model_out;
     }
-    std::unique_ptr<dmlc::Stream> fo(
+    for (int i = 0; i < argc; ++i) {
-        dmlc::Stream::Create(os.str().c_str(), "w"));
+      std::string str {argv[i]};
-    learner->SaveModel(fo.get());
+      if (str == "-h" || str == "--help") {
-  }
+        print_info_ = kHelp;
-
+        break;
-  double elapsed = dmlc::GetTime() - start;
+      } else if (str == "-V" || str == "--version") {
-  LOG(INFO) << "update end, " << elapsed << " sec in all";
+        print_info_ = kVersion;
-}
+        break;
-
+      }
-void CLIDumpModel(const CLIParam& param) {
-  FeatureMap fmap;
-  if (param.name_fmap != "NULL") {
-    std::unique_ptr<dmlc::Stream> fs(
-        dmlc::Stream::Create(param.name_fmap.c_str(), "r"));
-    dmlc::istream is(fs.get());
-    fmap.LoadText(is);
-  }
-  // load model
-  CHECK_NE(param.model_in, "NULL")
-      << "Must specify model_in for dump";
-  std::unique_ptr<Learner> learner(Learner::Create({}));
-  std::unique_ptr<dmlc::Stream> fi(
-      dmlc::Stream::Create(param.model_in.c_str(), "r"));
-  learner->SetParams(param.cfg);
-  learner->LoadModel(fi.get());
-  // dump data
-  std::vector<std::string> dump = learner->DumpModel(
-      fmap, param.dump_stats, param.dump_format);
-  std::unique_ptr<dmlc::Stream> fo(
-      dmlc::Stream::Create(param.name_dump.c_str(), "w"));
-  dmlc::ostream os(fo.get());
-  if (param.dump_format == "json") {
-    os << "[" << std::endl;
-    for (size_t i = 0; i < dump.size(); ++i) {
-      if (i != 0) os << "," << std::endl;
-      os << dump[i];  // Dump the previously generated JSON here
     }
-    os << std::endl << "]" << std::endl;
+    if (print_info_ != kNone) {
-  } else {
+      return;
-    for (size_t i = 0; i < dump.size(); ++i) {
-      os << "booster[" << i << "]:\n";
-      os << dump[i];
     }
+
+    rabit::Init(argc, argv);
+    std::string config_path = argv[1];
+
+    common::ConfigParser cp(config_path);
+    auto cfg = cp.Parse();
+
+    for (int i = 2; i < argc; ++i) {
+      char name[256], val[256];
+      if (sscanf(argv[i], "%[^=]=%s", name, val) == 2) {
+        cfg.emplace_back(std::string(name), std::string(val));
+      }
+    }
+
+    param_.Configure(cfg);
   }
-  // force flush before fo destruct.
-  os.set_stream(nullptr);
-}
 
-void CLIPredict(const CLIParam& param) {
+  int Run() {
-  CHECK_NE(param.test_path, "NULL")
+    switch (this->print_info_) {
-      << "Test dataset parameter test:data must be specified.";
+    case kNone:
-  // load data
+      break;
-  std::shared_ptr<DMatrix> dtest(
+    case kVersion: {
-      DMatrix::Load(
+      this->PrintVersion();
-          param.test_path,
+      return 0;
-          ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
+    }
-          param.dsplit == 2));
+    case kHelp: {
-  // load model
+      this->PrintHelp();
-  CHECK_NE(param.model_in, "NULL")
+      return 0;
-      << "Must specify model_in for predict";
+    }
-  std::unique_ptr<Learner> learner(Learner::Create({}));
+    }
-  std::unique_ptr<dmlc::Stream> fi(
-      dmlc::Stream::Create(param.model_in.c_str(), "r"));
-  learner->LoadModel(fi.get());
-  learner->SetParams(param.cfg);
 
-  LOG(INFO) << "start prediction...";
+    try {
-  HostDeviceVector<bst_float> preds;
+      switch (param_.task) {
-  learner->Predict(dtest, param.pred_margin, &preds, param.ntree_limit);
+      case kTrain:
-  LOG(CONSOLE) << "writing prediction to " << param.name_pred;
+        CLITrain();
-
+        break;
-  std::unique_ptr<dmlc::Stream> fo(
+      case kDumpModel:
-      dmlc::Stream::Create(param.name_pred.c_str(), "w"));
+        CLIDumpModel();
-  dmlc::ostream os(fo.get());
+        break;
-  for (bst_float p : preds.ConstHostVector()) {
+      case kPredict:
-    os << std::setprecision(std::numeric_limits<bst_float>::max_digits10)
+        CLIPredict();
-       << p << '\n';
+        break;
-  }
+      }
-  // force flush before fo destruct.
+    } catch (dmlc::Error const& e) {
-  os.set_stream(nullptr);
+      xgboost::CLIError(e);
-}
+      return 1;
-
+    }
-int CLIRunTask(int argc, char *argv[]) {
-  if (argc < 2) {
-    printf("Usage: <config>\n");
     return 0;
   }
-  rabit::Init(argc, argv);
 
-  common::ConfigParser cp(argv[1]);
+  ~CLI() {
-  auto cfg = cp.Parse();
+    rabit::Finalize();
-
-  for (int i = 2; i < argc; ++i) {
-    char name[256], val[256];
-    if (sscanf(argv[i], "%[^=]=%s", name, val) == 2) {
-      cfg.emplace_back(std::string(name), std::string(val));
-    }
   }
-  CLIParam param;
+};
-  param.Configure(cfg);
-
-  switch (param.task) {
-    case kTrain: CLITrain(param); break;
-    case kDumpModel: CLIDumpModel(param); break;
-    case kPredict: CLIPredict(param); break;
-  }
-  rabit::Finalize();
-  return 0;
-}
 }  // namespace xgboost
 
 int main(int argc, char *argv[]) {
-  return xgboost::CLIRunTask(argc, argv);
+  try {
+    xgboost::CLI cli(argc, argv);
+    return cli.Run();
+  } catch (dmlc::Error const& e) {
+    // This captures only the initialization error.
+    xgboost::CLIError(e);
+    return 1;
+  }
+  return 0;
 }

src/common/config.h

@@ -7,8 +7,6 @@
 #ifndef XGBOOST_COMMON_CONFIG_H_
 #define XGBOOST_COMMON_CONFIG_H_
 
-#include <xgboost/logging.h>
-#include <cstdio>
 #include <string>
 #include <fstream>
 #include <istream>
@@ -18,6 +16,8 @@
 #include <iterator>
 #include <utility>
 
+#include "xgboost/logging.h"
+
 namespace xgboost {
 namespace common {
 /*!
@@ -40,10 +40,16 @@ class ConfigParser {
 
   std::string LoadConfigFile(const std::string& path) {
     std::ifstream fin(path, std::ios_base::in | std::ios_base::binary);
-    CHECK(fin) << "Failed to open: " << path;
+    CHECK(fin) << "Failed to open config file: \"" << path << "\"";
-    std::string content{std::istreambuf_iterator<char>(fin),
+    try {
-                        std::istreambuf_iterator<char>()};
+      std::string content{std::istreambuf_iterator<char>(fin),
-    return content;
+                          std::istreambuf_iterator<char>()};
+      return content;
+    } catch (std::ios_base::failure const &e) {
+      LOG(FATAL) << "Failed to read config file: \"" << path << "\"\n"
+                 << e.what();
+    }
+    return "";
   }
 
   /*!

src/common/device_helpers.cuh

@@ -30,7 +30,6 @@
 
 #ifdef XGBOOST_USE_NCCL
 #include "nccl.h"
-#include "../common/io.h"
 #endif
 
 #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600 || defined(__clang__)

src/common/random.h

@@ -18,7 +18,6 @@
 #include <random>
 
 #include "xgboost/host_device_vector.h"
-#include "io.h"
 
 namespace xgboost {
 namespace common {

src/objective/aft_obj.cc

@@ -114,5 +114,3 @@ XGBOOST_REGISTER_OBJECTIVE(AFTObj, "survival:aft")
 
 }  // namespace obj
 }  // namespace xgboost
-
-

src/tree/updater_gpu_hist.cu

@@ -17,10 +17,12 @@
 #include "xgboost/span.h"
 #include "xgboost/json.h"
 
+#include "../common/io.h"
 #include "../common/device_helpers.cuh"
 #include "../common/hist_util.h"
 #include "../common/timer.h"
 #include "../data/ellpack_page.cuh"
+
 #include "param.h"
 #include "updater_gpu_common.cuh"
 #include "constraints.cuh"

tests/python/test_cli.py

@@ -5,6 +5,7 @@ import platform
 import xgboost
 import subprocess
 import numpy
+import json
 
 
 class TestCLI(unittest.TestCase):
@@ -27,20 +28,23 @@ data = {data_path}
 eval[test] = {data_path}
 '''
 
-    def test_cli_model(self):
+    curdir = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
-        curdir = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
+    project_root = os.path.normpath(
-        project_root = os.path.normpath(
+        os.path.join(curdir, os.path.pardir, os.path.pardir))
-            os.path.join(curdir, os.path.pardir, os.path.pardir))
-        data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
-            root=project_root)
 
+    def get_exe(self):
         if platform.system() == 'Windows':
             exe = 'xgboost.exe'
         else:
             exe = 'xgboost'
-        exe = os.path.join(project_root, exe)
+        exe = os.path.join(self.project_root, exe)
         assert os.path.exists(exe)
+        return exe
 
+    def test_cli_model(self):
+        data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
+            root=self.project_root)
+        exe = self.get_exe()
         seed = 1994
 
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -102,3 +106,48 @@ eval[test] = {data_path}
                 py_model_bin = fd.read()
 
             assert hash(cli_model_bin) == hash(py_model_bin)
+
+    def test_cli_help(self):
+        exe = self.get_exe()
+        completed = subprocess.run([exe], stdout=subprocess.PIPE)
+        error_msg = completed.stdout.decode('utf-8')
+        ret = completed.returncode
+        assert ret == 1
+        assert error_msg.find('Usage') != -1
+        assert error_msg.find('eval[NAME]') != -1
+
+        completed = subprocess.run([exe, '-V'], stdout=subprocess.PIPE)
+        msg = completed.stdout.decode('utf-8')
+        assert msg.find('XGBoost') != -1
+        v = xgboost.__version__
+        if v.find('SNAPSHOT') != -1:
+            assert msg.split(':')[1].strip() == v.split('-')[0]
+        else:
+            assert msg.split(':')[1].strip() == v
+
+    def test_cli_model_json(self):
+        exe = self.get_exe()
+        data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
+            root=self.project_root)
+        seed = 1994
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model_out_cli = os.path.join(
+                tmpdir, 'test_load_cli_model-cli.json')
+            config_path = os.path.join(tmpdir, 'test_load_cli_model.conf')
+
+            train_conf = self.template.format(data_path=data_path,
+                                              seed=seed,
+                                              task='train',
+                                              model_in='NULL',
+                                              model_out=model_out_cli,
+                                              test_path='NULL',
+                                              name_pred='NULL')
+            with open(config_path, 'w') as fd:
+                fd.write(train_conf)
+
+            subprocess.run([exe, config_path])
+            with open(model_out_cli, 'r') as fd:
+                model = json.load(fd)
+
+            assert model['learner']['gradient_booster']['name'] == 'gbtree'