[GPU-Plugin] Major refactor 2 (#2664)

* Change cmake option

* Move source files

* Move google tests

* Move python tests

* Move benchmarks

* Move documentation

* Remove makefile support

* Fix test run

* Move GPU tests

CMakeLists.txt

@@ -7,12 +7,17 @@ set_default_configuration_release()
 msvc_use_static_runtime()
 
 # Options
-option(PLUGIN_UPDATER_GPU "Build GPU accelerated tree construction plugin")
+option(USE_CUDA  "Build with GPU acceleration") 
 option(JVM_BINDINGS "Build JVM bindings" OFF)
 option(GOOGLE_TEST "Build google tests" OFF)
 set(GPU_COMPUTE_VER 35;50;52;60;61 CACHE STRING
   "Space separated list of compute versions to be built against")
 
+# Deprecation warning
+if(PLUGIN_UPDATER_GPU)
+  set(USE_CUDA ON)
+  message(WARNING "The option 'PLUGIN_UPDATER_GPU' is deprecated. Set 'USE_CUDA' instead.")
+endif()
 
 # Compiler flags
 set(CMAKE_CXX_STANDARD 11)
@@ -40,9 +45,14 @@ file(GLOB_RECURSE SOURCES
     src/*.h
     include/*.h
 )
+
 # Only add main function for executable target
 list(REMOVE_ITEM SOURCES ${PROJECT_SOURCE_DIR}/src/cli_main.cc)
 
+file(GLOB_RECURSE CUDA_SOURCES
+    src/*.cu
+    src/*.cuh
+)
 
 # rabit
 # TODO: Create rabit cmakelists.txt
@@ -68,13 +78,7 @@ endif()
 add_subdirectory(dmlc-core)
 set(LINK_LIBRARIES dmlccore rabit)
 
-
+if(USE_CUDA)
-# GPU Plugin
-file(GLOB_RECURSE CUDA_SOURCES
-    plugin/updater_gpu/src/*.cu
-    plugin/updater_gpu/src/*.cuh
-)
-if(PLUGIN_UPDATER_GPU)
   find_package(CUDA 7.5 REQUIRED)
   cmake_minimum_required(VERSION 3.5)
 
@@ -144,8 +148,8 @@ if(GOOGLE_TEST)
   auto_source_group("${TEST_SOURCES}")
   include_directories(${GTEST_INCLUDE_DIRS})
 
-  if(PLUGIN_UPDATER_GPU)
+  if(USE_CUDA)
-    file(GLOB_RECURSE CUDA_TEST_SOURCES "plugin/updater_gpu/test/cpp/*.cu")
+    file(GLOB_RECURSE CUDA_TEST_SOURCES "tests/cpp/*.cu")
     cuda_compile(CUDA_TEST_OBJS ${CUDA_TEST_SOURCES})
   else()
     set(CUDA_TEST_OBJS "")

Makefile

@@ -116,22 +116,6 @@ else
 endif
 CFLAGS += $(OPENMP_FLAGS)
 
-# for using GPUs
-GPU_COMPUTE_VER ?= 35 50 52 60 61
-NVCC = nvcc
-INCLUDES = -Iinclude -I$(DMLC_CORE)/include -I$(RABIT)/include
-INCLUDES += -I$(CUB_PATH)
-INCLUDES += -I$(GTEST_PATH)/include
-CODE = $(foreach ver,$(GPU_COMPUTE_VER),-gencode arch=compute_$(ver),code=sm_$(ver))
-NVCC_FLAGS = --std=c++11 $(CODE) $(INCLUDES) -lineinfo --expt-extended-lambda
-NVCC_FLAGS += -Xcompiler=$(OPENMP_FLAGS) -Xcompiler=-fPIC
-ifeq ($(PLUGIN_UPDATER_GPU),ON)
-  CUDA_ROOT = $(shell dirname $(shell dirname $(shell which $(NVCC))))
-  INCLUDES += -I$(CUDA_ROOT)/include -Inccl/src/
-  LDFLAGS += -L$(CUDA_ROOT)/lib64 -lcudart  -lcudadevrt -Lnccl/build/lib/ -lnccl_static -lm -ldl -lrt
-  CFLAGS += -DXGBOOST_USE_CUDA
-endif
-
 # specify tensor path
 .PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck java pylint
 
@@ -153,31 +137,16 @@ ALL_DEP = $(filter-out build/cli_main.o, $(ALL_OBJ)) $(LIB_DEP)
 CLI_OBJ = build/cli_main.o
 include tests/cpp/xgboost_test.mk
 
-# order of this rule matters wrt %.cc rule below!
-build/%.o: src/%.cu
-	@mkdir -p $(@D)
-	$(NVCC) -c $(NVCC_FLAGS) $< -o $@
-
 build/%.o: src/%.cc
 	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) -MM -MT build/$*.o $< >build/$*.d
 	$(CXX) -c $(CFLAGS) $< -o $@
 
-# order of this rule matters wrt %.cc rule below!
-build_plugin/%.o: plugin/%.cu build_nccl
-	@mkdir -p $(@D)
-	$(NVCC) -c $(NVCC_FLAGS) $< -o $@
-
 build_plugin/%.o: plugin/%.cc
 	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) -MM -MT build_plugin/$*.o $< >build_plugin/$*.d
 	$(CXX) -c $(CFLAGS) $< -o $@
 
-build_nccl:
-	@mkdir -p build/include
-	cd build/include ; ln -sf ../../nccl/src/nccl.h .
-	cd nccl ; make -j ; cd ..
-
 # The should be equivalent to $(ALL_OBJ)  except for build/cli_main.o
 amalgamation/xgboost-all0.o: amalgamation/xgboost-all0.cc
 	$(CXX) -c $(CFLAGS) $< -o $@

doc/_static/xgboost-theme/navbar.html

@@ -23,6 +23,7 @@
             <li><a href="{{url_root}}jvm/index.html">JVM</a></li>
             <li><a href="{{url_root}}julia/index.html">Julia</a></li>
             <li><a href="{{url_root}}cli/index.html">CLI</a></li>
+            <li><a href="{{url_root}}gpu/index.html">GPU</a></li>
           </ul>
         </li>
         {% endfor %}

doc/build.md

@@ -28,6 +28,7 @@ even better to send pull request if you can fix the problem.
   - [Building on Ubuntu/Debian](#building-on-ubuntu-debian)
   - [Building on OSX](#building-on-osx)
   - [Building on Windows](#building-on-windows)
+  - [Building with GPU support](#building-with-gpu-support)
   - [Windows Binaries](#windows-binaries)
   - [Customized Building](#customized-building)
 - [Python Package Installation](#python-package-installation)
@@ -131,6 +132,32 @@ This specifies an out of source build using the MSVC 12 64 bit generator. Open t
 
 Other versions of Visual Studio may work but are untested.
 
+### Building with GPU support
+
+XGBoost can be built with GPU support for both Linux and Windows using cmake. GPU support works with the Python package as well as the CLI version. The R package is not yet supported.
+
+An up-to-date version of the cuda toolkit is required.
+
+From the command line on Linux starting from the xgboost directory:
+
+```bash
+$ mkdir build
+$ cd build
+$ cmake .. -DUSE_CUDA=ON
+$ make -j
+```
+On Windows using cmake, see what options for Generators you have for cmake, and choose one with [arch] replaced by Win64:
+```bash
+cmake -help
+```
+Then run cmake as:
+```bash
+$ mkdir build
+$ cd build
+$ cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON
+```
+Cmake will create an xgboost.sln solution file in the build directory. Build this solution in release mode as a x64 build.
+
 ### Windows Binaries
 Unofficial windows binaries and instructions on how to use them are hosted on [Guido Tapia's blog](http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/)
 

doc/conf.py

@@ -168,6 +168,8 @@ def setup(app):
                        '_static/jquery.js')
     app.add_config_value('recommonmark_config', {
             'url_resolver': lambda url: github_doc_root + url,
-            }, True)
+            'enable_eval_rst': True,
+            }, True,
+            )
     app.add_transform(AutoStructify)
     app.add_javascript('jquery.js')

doc/how_to/index.md

@@ -8,6 +8,7 @@ This page contains guidelines to use and develop XGBoost.
 ## Use XGBoost in Specific Ways
 - [Parameter tuning guide](param_tuning.md)
 - [Use out of core computation for large dataset](external_memory.md)
+- [Use XGBoost GPU algorithms](../gpu/index.md)
 
 ## Develop and Hack XGBoost
 - [Contribute to XGBoost](contribute.md)

doc/index.md

@@ -10,6 +10,7 @@ These are used to generate the index used in search.
 * [Java/Scala Package Document](jvm/index.md)
 * [Julia Package Document](julia/index.md)
 * [CLI Package Document](cli/index.md)
+* [GPU Support Document](gpu/index.md)
 - [Howto Documents](how_to/index.md)
 - [Get Started Documents](get_started/index.md)
 - [Tutorials](tutorials/index.md)

plugin/updater_gpu/README.md

@@ -1,161 +1,3 @@
 # CUDA Accelerated Tree Construction Algorithms
-This plugin adds GPU accelerated tree construction and prediction algorithms to XGBoost.
-## Usage
-Specify the 'tree_method' parameter as one of the following algorithms. 
-
-### Algorithms
-| tree_method | Description |
-| --- | --- |
-gpu_exact | The standard XGBoost tree construction algorithm. Performs exact search for splits. Slower and uses considerably more memory than 'gpu_hist' |
-gpu_hist | Equivalent to the XGBoost fast histogram algorithm. Faster and uses considerably less memory. Splits may be less accurate. |
-
-### Supported parameters 
-| parameter | gpu_exact | gpu_hist |
-| --- | --- | --- |
-subsample | ✔ | ✔ |
-colsample_bytree | ✔ | ✔|
-colsample_bylevel | ✔ | ✔ |
-max_bin | ✖ | ✔ |
-gpu_id | ✔ | ✔ | 
-n_gpus | ✖ | ✔ | 
-predictor | ✔ | ✔ |
-
-GPU accelerated prediction is enabled by default for the above mentioned 'tree_method' parameters but can be switched to CPU prediction by setting 'predictor':'cpu_predictor'. This could be useful if you want to conserve GPU memory. Likewise when using CPU algorithms, GPU accelerated prediction can be enabled by setting 'predictor':'gpu_predictor'.
-
-The device ordinal can be selected using the 'gpu_id' parameter, which defaults to 0.
-
-Multiple GPUs can be used with the grow_gpu_hist parameter using the n_gpus parameter. which defaults to 1. If this is set to -1 all available GPUs will be used.  If gpu_id is specified as non-zero, the gpu device order is mod(gpu_id + i) % n_visible_devices for i=0 to n_gpus-1.  As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.  For example, when n_features * n_bins * 2^depth divided by time of each round/iteration becomes comparable to the real PCI 16x bus bandwidth of order 4GB/s to 10GB/s, then AllReduce will dominant code speed and multiple GPUs become ineffective at increasing performance.  Also, CPU overhead between GPU calls can limit usefulness of multiple GPUs.
-
-This plugin currently works with the CLI version and python version.
-
-Python example:
-```python
-param['gpu_id'] = 0
-param['max_bin'] = 16
-param['tree_method'] = 'gpu_hist'
-```
-## Benchmarks
-To run benchmarks on synthetic data for binary classification:
-```bash
-$ python benchmark/benchmark.py
-```
-
-Training time time on 1,000,000 rows x 50 columns with 500 boosting iterations and 0.25/0.75 test/train split on i7-6700K CPU @ 4.00GHz and Pascal Titan X.
-
-| tree_method | Time (s) |
-| --- | --- |
-| gpu_hist | 13.87 |
-| hist | 63.55 |
-| gpu_exact | 161.08 |
-| exact | 1082.20 |
-
-
-[See here](http://dmlc.ml/2016/12/14/GPU-accelerated-xgboost.html) for additional performance benchmarks of the 'gpu_exact' tree_method.
-
-## Test
-To run python tests:
-```bash
-$ python -m nose test/python/
-```
-
-Google tests can be enabled by specifying -DGOOGLE_TEST=ON when building with cmake.
-
-## Dependencies
-A CUDA capable GPU with at least compute capability >= 3.5 
-
-Building the plug-in requires CUDA Toolkit 7.5 or later (https://developer.nvidia.com/cuda-downloads)
-
-## Build
-
-From the command line on Linux starting from the xgboost directory:
-
-On Linux, from the xgboost directory:
-```bash
-$ mkdir build
-$ cd build
-$ cmake .. -DPLUGIN_UPDATER_GPU=ON
-$ make -j
-```
-On Windows using cmake, see what options for Generators you have for cmake, and choose one with [arch] replaced by Win64:
-```bash
-cmake -help
-```
-Then run cmake as:
-```bash
-$ mkdir build
-$ cd build
-$ cmake .. -G"Visual Studio 14 2015 Win64" -DPLUGIN_UPDATER_GPU=ON
-```
-Cmake will create an xgboost.sln solution file in the build directory. Build this solution in release mode as a x64 build.
-
-Visual studio community 2015, supported by cuda toolkit (http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/#axzz4isREr2nS), can be downloaded from: https://my.visualstudio.com/Downloads?q=Visual%20Studio%20Community%202015 .  You may also be able to use a later version of visual studio depending on whether the CUDA toolkit supports it.  Note that Mingw cannot be used with cuda.
-
-### For other nccl libraries
-
-On some systems, nccl libraries are specific to a particular system (IBM Power or nvidia-docker) and can enable use of nvlink (between GPUs or even between GPUs and system memory).  In that case, one wants to avoid the static nccl library by changing "STATIC" to "SHARED" in nccl/CMakeLists.txt and deleting the shared nccl library created (so that the system one is used).
-
-### For Developers!
-
-In case you want to build only for a specific GPU(s), for eg. GP100 and GP102,
-whose compute capability are 60 and 61 respectively:
-```bash
-$ cmake .. -DPLUGIN_UPDATER_GPU=ON -DGPU_COMPUTE_VER="60;61"
-```
-
-### Using make
-Now, it also supports the usual 'make' flow to build gpu-enabled tree construction plugins. It's currently only tested on Linux. From the xgboost directory
-```bash
-# make sure CUDA SDK bin directory is in the 'PATH' env variable
-$ make -j PLUGIN_UPDATER_GPU=ON
-```
-
-Similar to cmake, if you want to build only for a specific GPU(s):
-```bash
-$ make -j PLUGIN_UPDATER_GPU=ON GPU_COMPUTE_VER="60 61"
-```
-
-## Changelog
-##### 2017/8/14
-* Added GPU accelerated prediction. Considerably improved performance when using test/eval sets.
-
-##### 2017/7/10
-* Memory performance improved 4x for gpu_hist
-
-##### 2017/6/26
-* Change API to use tree_method parameter
-* Increase required cmake version to 3.5
-* Add compute arch 3.5 to default archs
-* Set default n_gpus to 1
-
-##### 2017/6/5
-
-* Multi-GPU support for histogram method using NVIDIA NCCL.
-
-##### 2017/5/31
-* Faster version of the grow_gpu plugin
-* Added support for building gpu plugin through 'make' flow too
-
-##### 2017/5/19
-* Further performance enhancements for histogram method.
-
-##### 2017/5/5
-* Histogram performance improvements
-* Fix gcc build issues 
-
-##### 2017/4/25
-* Add fast histogram algorithm
-* Fix Linux build
-* Add 'gpu_id' parameter
-
-## References
-[Mitchell, Rory, and Eibe Frank. Accelerating the XGBoost algorithm using GPU computing. No. e2911v1. PeerJ Preprints, 2017.](https://peerj.com/preprints/2911/)
-
-## Author
-Rory Mitchell
-Jonathan C. McKinney
-Shankara Rao Thejaswi Nanditale
-Vinay Deshpande
-... and the rest of the H2O.ai and NVIDIA team.
-
-Please report bugs to the xgboost/issues page.
 
+[The XGBoost GPU documentation has moved here.](https://xgboost.readthedocs.io/en/latest/gpu/index.html)

plugin/updater_gpu/plugin.mk

@@ -1,6 +0,0 @@
-
-PLUGIN_OBJS += build_plugin/updater_gpu/src/register_updater_gpu.o \
-               build_plugin/updater_gpu/src/updater_gpu.o \
-               build_plugin/updater_gpu/src/gpu_hist_builder.o \
-               build_plugin/updater_gpu/src/gpu_predictor.o
-PLUGIN_LDFLAGS += -L$(CUDA_ROOT)/lib64 -lcudart

src/predictor/gpu_predictor.cu

@@ -8,7 +8,7 @@
 #include <xgboost/tree_model.h>
 #include <xgboost/tree_updater.h>
 #include <memory>
-#include "device_helpers.cuh"
+#include "../common/device_helpers.cuh"
 
 namespace xgboost {
 namespace predictor {

src/tree/updater_gpu.cu

@@ -4,7 +4,7 @@
 #include <xgboost/tree_updater.h>
 #include <utility>
 #include <vector>
-#include "../../../src/tree/param.h"
+#include "param.h"
 #include "updater_gpu_common.cuh"
 
 namespace xgboost {

src/tree/updater_gpu_common.cuh

@@ -7,10 +7,10 @@
 #include <stdexcept>
 #include <string>
 #include <vector>
-#include "../../../src/common/random.h"
+#include "../common/random.h"
-#include "../../../src/tree/param.h"
+#include "param.h"
-#include "cub/cub.cuh"
+#include <cub/cub.cuh>
-#include "device_helpers.cuh"
+#include "../common/device_helpers.cuh"
 
 namespace xgboost {
 namespace tree {

src/tree/updater_gpu_hist.cu

@@ -5,12 +5,11 @@
 #include <memory>
 #include <utility>
 #include <vector>
-#include "../../../src/common/sync.h"
+#include "param.h"
-#include "../../../src/tree/param.h"
+#include "../common/compressed_iterator.h"
-#include "../../src/common/compressed_iterator.h"
+#include "../common/hist_util.h"
-#include "../../src/common/hist_util.h"
 #include "updater_gpu_common.cuh"
-#include "device_helpers.cuh"
+#include "../common/device_helpers.cuh"
 
 namespace xgboost {
 namespace tree {

tests/cpp/common/test_device_helpers.cu

@@ -4,7 +4,7 @@
  */
 #include <thrust/device_vector.h>
 #include <xgboost/base.h>
-#include "../../src/device_helpers.cuh"
+#include "../../../src/common/device_helpers.cuh"
 #include "gtest/gtest.h"
 
 void CreateTestData(xgboost::bst_uint num_rows, int max_row_size,

tests/cpp/predictor/test_gpu_predictor.cu

@@ -5,7 +5,7 @@
 #include <xgboost/c_api.h>
 #include <xgboost/predictor.h>
 #include "gtest/gtest.h"
-#include "../../../../tests/cpp/helpers.h"
+#include "../helpers.h"
 
 namespace xgboost {
 namespace predictor {

tests/cpp/xgboost_test.mk

@@ -5,17 +5,6 @@ UNITTEST=$(UTEST_ROOT)/xgboost_test
 UNITTEST_SRC=$(wildcard $(UTEST_ROOT)/*.cc $(UTEST_ROOT)/*/*.cc)
 UNITTEST_OBJ=$(patsubst $(UTEST_ROOT)%.cc, $(UTEST_OBJ_ROOT)%.o, $(UNITTEST_SRC))
 
-# for if and when we add cuda source files into xgboost core
-UNITTEST_CU_SRC=$(wildcard $(UTEST_ROOT)/*.cu $(UTEST_ROOT)/*/*.cu)
-UNITTEST_OBJ += $(patsubst $(UTEST_ROOT)%.cu, $(UTEST_OBJ_ROOT)%.o, $(UNITTEST_CU_SRC))
-
-# tests from grow_gpu plugin (only if CUDA path is enabled!)
-ifeq ($(PLUGIN_UPDATER_GPU),ON)
-  GPU_PLUGIN_FOLDER = plugin/updater_gpu
-  UNITTEST_CU_PLUGIN_SRC = $(wildcard $(GPU_PLUGIN_FOLDER)/test/cpp/*.cu)
-  UNITTEST_OBJ += $(patsubst %.cu, $(UTEST_OBJ_ROOT)/%.o, $(UNITTEST_CU_PLUGIN_SRC))
-endif
-
 GTEST_LIB=$(GTEST_PATH)/lib/
 GTEST_INC=$(GTEST_PATH)/include/
 
@@ -26,14 +15,6 @@ UNITTEST_DEPS=lib/libxgboost.a $(DMLC_CORE)/libdmlc.a $(RABIT)/lib/$(LIB_RABIT)
 COVER_OBJ=$(patsubst %.o, %.gcda, $(ALL_OBJ)) $(patsubst %.o, %.gcda, $(UNITTEST_OBJ))
 
 # the order of the below targets matter!
-$(UTEST_OBJ_ROOT)/$(GPU_PLUGIN_FOLDER)/test/cpp/%.o: $(GPU_PLUGIN_FOLDER)/test/cpp/%.cu
-	@mkdir -p $(@D)
-	$(NVCC) $(NVCC_FLAGS) -I$(GTEST_INC) -o $@ -c $<
-
-$(UTEST_OBJ_ROOT)/%.o: $(UTEST_ROOT)/%.cu
-	@mkdir -p $(@D)
-	$(NVCC) $(NVCC_FLAGS) -I$(GTEST_INC) -o $@ -c $<
-
 $(UTEST_OBJ_ROOT)/$(GTEST_PATH)/%.o: $(GTEST_PATH)/%.cc
 	@mkdir -p $(@D)
 	$(CXX) $(UNITTEST_CFLAGS) -I$(GTEST_INC) -I$(GTEST_PATH) -o $@ -c $<

tests/python-gpu/test_gpu_prediction.py

@@ -1,15 +1,14 @@
 from __future__ import print_function
 #pylint: skip-file
-import sys
-sys.path.append("../../tests/python")
 import xgboost as xgb
 import testing as tm
 import numpy as np
 import unittest
+from nose.plugins.attrib import attr
 
 rng = np.random.RandomState(1994)
 
-
+@attr('gpu')
 class TestGPUPredict (unittest.TestCase):
     def test_predict(self):
         iterations = 1
@@ -35,3 +34,4 @@ class TestGPUPredict (unittest.TestCase):
 
     def non_decreasing(self, L):
         return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
+

tests/python-gpu/test_gpu_updaters.py

@@ -6,18 +6,17 @@ import xgboost as xgb
 import testing as tm
 import numpy as np
 import unittest
+from nose.plugins.attrib import attr
 
 rng = np.random.RandomState(1994)
 
-dpath = '../../demo/data/'
+dpath = 'demo/data/'
-ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
-ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
 
 def eprint(*args, **kwargs):
     print(*args, file=sys.stderr, **kwargs)
     print(*args, file=sys.stdout, **kwargs)
         
-
+@attr('gpu')
 class TestGPU(unittest.TestCase):
     def test_grow_gpu(self):
         tm._skip_if_no_sklearn()
@@ -27,6 +26,9 @@ class TestGPU(unittest.TestCase):
         except:
             from sklearn.cross_validation import train_test_split
 
+        ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
+
         ag_param = {'max_depth': 2,
                     'tree_method': 'exact',
                     'nthread': 0,
@@ -123,6 +125,10 @@ class TestGPU(unittest.TestCase):
         except:
             from sklearn.cross_validation import train_test_split
 
+        ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
+
+
         for max_depth in range(3,10): # TODO: Doesn't work with 2 for some tests
             #eprint("max_depth=%d" % (max_depth))
             

tests/python-gpu/test_large_sizes.py

@@ -7,13 +7,12 @@ import xgboost as xgb
 import testing as tm
 import numpy as np
 import unittest
-from sklearn.datasets import make_classification
+from nose.plugins.attrib import attr
+
 def eprint(*args, **kwargs):
     print(*args, file=sys.stderr, **kwargs) ; sys.stderr.flush()
     print(*args, file=sys.stdout, **kwargs) ; sys.stdout.flush()
 
-eprint("Testing Big Data (this may take a while)")
-
 rng = np.random.RandomState(1994)
 
 # "realistic" size based upon http://stat-computing.org/dataexpo/2009/ , which has been processed to one-hot encode categoricalsxsy
@@ -26,6 +25,7 @@ rows1 = 42360032 # large
 rowslist = [rows1, rows2, rows3]
 
 
+@attr('slow')
 class TestGPU(unittest.TestCase):
     def test_large(self):
         eprint("Starting test for large data")