6 minutes
Installer XGBoost, LightGBM et CatBoost sur Ubuntu 18.04
Installation de XGBoost
Installation simple
Exécuter la commande suivante:
pip install xgboost
“The default open-source XGBoost packages already include GPU support.”
Build from source
Si cela ne fonctionne pas, compiler et installer XGBoost depuis les sources.
Installer cmake pour builder xgboost. La version CMake 3.12 ou plus est requise.
sudo apt-get update
sudo apt install -y cmake
cmake --version
Si ce n’est pas la bonne version désinstallez le avant de le réinstaller manuellement:
sudo apt purge cmake
# Download source
version=3.14
build=5
mkdir ~/temp
cd ~/temp
wget https://cmake.org/files/v$version/cmake-$version.$build.tar.gz
tar -xzvf cmake-$version.$build.tar.gz
cd cmake-$version.$build/
# Build et installation
./bootstrap
make -j4 && sudo make install
# Vérification de la version
cmake --version
Déterminer le compute capability de votre carte graphique pour l’indiquer à la prochaine commande dans le flag -DGPU_COMPUTE_VER=:
cd ~/NVIDIA_CUDA-9.0_Samples/1_Utilities/deviceQuery
make
./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 660 Ti"
CUDA Driver Version / Runtime Version 9.1 / 9.0
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 2000 MBytes (2097086464 bytes)
( 7) Multiprocessors, (192) CUDA Cores/MP: 1344 CUDA Cores
GPU Max Clock rate: 980 MHz (0.98 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 192-bit
L2 Cache Size: 393216 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 6 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS
La ligne qui nous intéresse est la suivante CUDA Capability Major/Minor version number: 3.0
Dans le flag -DGPU_COMPUTE_VER= vous pourrez indiquer la valeur 30.
Cette carte graphique n’est plus compatible avec XGBoost. Le minimum requis d’après le site officiel est
CUDA 9.0, Compute Capability 3.5 required. https://xgboost.readthedocs.io/en/latest/gpu/
Builder XGBoost:
cd ~ && \
git clone --recursive https://github.com/dmlc/xgboost && \
cd xgboost && mkdir build && cd build && cmake -DCUDA_HOST_COMPILER=/usr/bin/gcc-6 -DGPU_COMPUTE_VER=35 -DUSE_CUDA=ON .. && make -j
Installer XGBoost:
cd ../python-package
sudo python3 setup.py install
Vérification du bon fonctionnement d’XGBoost
git clone https://github.com/dmlc/xgboost
cd xgboost
python3 # ou workon votre_environnement_virtuel && python
Puis exécutez les commandes Python suivantes:
import xgboost as xgb
# read in data
dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
# specify parameters via map
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
num_round = 2
bst = xgb.train(param, dtrain, num_round)
# make prediction
preds = bst.predict(dtest)
print(preds)
Installation de LightGBM
Installation des dépendances:
sudo apt install -y \
libboost-dev \
libboost-system-dev \
libboost-filesystem-dev
Build:
cd ~ && \
git clone --recursive https://github.com/Microsoft/LightGBM && \
cd LightGBM && mkdir build && cd build && \
cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda-9.0/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda-9.0/include/ .. && \
make -j
Installation:
cd ../python-package
sudo python setup.py install --precompile
Vérifier le bon fonctionnement de LightGBM:
cd ~/LightGBM/examples/python-guide/
pip install scikit-learn pandas matplotlib scipy -U
python3 simple_example.py
Loading data...
Starting training...
[1] valid_0's l1: 0.492841 valid_0's l2: 0.243898
Training until validation scores don't improve for 5 rounds.
[2] valid_0's l1: 0.489327 valid_0's l2: 0.240605
[3] valid_0's l1: 0.484931 valid_0's l2: 0.236472
[4] valid_0's l1: 0.480567 valid_0's l2: 0.232586
[5] valid_0's l1: 0.475965 valid_0's l2: 0.22865
[6] valid_0's l1: 0.472861 valid_0's l2: 0.226187
[7] valid_0's l1: 0.469847 valid_0's l2: 0.223738
[8] valid_0's l1: 0.466258 valid_0's l2: 0.221012
[9] valid_0's l1: 0.462751 valid_0's l2: 0.218429
[10] valid_0's l1: 0.458755 valid_0's l2: 0.215505
[11] valid_0's l1: 0.455252 valid_0's l2: 0.213027
[12] valid_0's l1: 0.452051 valid_0's l2: 0.210809
[13] valid_0's l1: 0.448764 valid_0's l2: 0.208612
[14] valid_0's l1: 0.446667 valid_0's l2: 0.207468
[15] valid_0's l1: 0.444211 valid_0's l2: 0.206009
[16] valid_0's l1: 0.44186 valid_0's l2: 0.20465
[17] valid_0's l1: 0.438508 valid_0's l2: 0.202489
[18] valid_0's l1: 0.435919 valid_0's l2: 0.200668
[19] valid_0's l1: 0.433348 valid_0's l2: 0.19925
[20] valid_0's l1: 0.431211 valid_0's l2: 0.198136
Did not meet early stopping. Best iteration is:
[20] valid_0's l1: 0.431211 valid_0's l2: 0.198136
Saving model...
Starting predicting...
The rmse of prediction is: 0.44512434910807497
Installation de CatBoost
Simplement:
pip install catboost
Installer l’outil de visualisation:
pip install ipywidgets
# Turn on the widgets extension:
jupyter nbextension enable --py widgetsnbextension
Pour tester le bon fonctionnement vous pouvez créer un fichier test.py et y insérer le code suivant:
from catboost import Pool, CatBoostClassifier
train_data = [["summer", 1924, 44],
["summer", 1932, 37],
["winter", 1980, 37],
["summer", 2012, 204]]
eval_data = [["winter", 1996, 197],
["winter", 1968, 37],
["summer", 2002, 77],
["summer", 1948, 59]]
cat_features = [0]
train_label = ["France", "USA", "USA", "UK"]
eval_label = ["USA", "France", "USA", "UK"]
train_dataset = Pool(data=train_data,
label=train_label,
cat_features=cat_features)
eval_dataset = Pool(data=eval_data,
label=eval_label,
cat_features=cat_features)
# Initialize CatBoostClassifier
model = CatBoostClassifier(iterations=10,
learning_rate=1,
depth=2,
loss_function='MultiClass',
task_type="GPU")
# Fit model
model.fit(train_dataset)
# Get predicted classes
preds_class = model.predict(eval_dataset)
# Get predicted probabilities for each class
preds_proba = model.predict_proba(eval_dataset)
# Get predicted RawFormulaVal
preds_raw = model.predict(eval_dataset,
prediction_type='RawFormulaVal')
Après l’avoir exécuté le code précédent un training sera réalisé sur GPU. Le lien suivant décrit comment faire. Si tout est ok vous devriez obtenir le résultat suivant:
0: learn: -0.9623099 total: 8.48ms remaining: 76.3ms
1: learn: -0.7421078 total: 14.6ms remaining: 58.3ms
2: learn: -0.5898572 total: 20.2ms remaining: 47.2ms
3: learn: -0.4816516 total: 26.3ms remaining: 39.4ms
4: learn: -0.4023528 total: 31.8ms remaining: 31.8ms
5: learn: -0.3545669 total: 37.2ms remaining: 24.8ms
6: learn: -0.3052314 total: 42.1ms remaining: 18ms
7: learn: -0.2666318 total: 47.6ms remaining: 11.9ms
8: learn: -0.2358041 total: 53ms remaining: 5.89ms
9: learn: -0.2107419 total: 57.9ms remaining: 0us
Comparaison des 3 algorithmes de Boosting
Un article très intéressant comparant les 3 algorithmes est disponible à l’adresse suivante.
Voici un tableau comparatif extrait de cet article:
Au vu de ces résultats, je pencherais soit sur l’utilisation de CatBoost si les délais d’inférence sont un enjeu. Dans le cas contraire, vue les résultats de LightGBM et sa durée d’entrainement nécessaire par rapport à XGBoost je partirais sur LightGBM.