Dear Ta Hoang, GPU processing can be done with Python libraries such as TensorFlow, Keras, or Theano.
However, sklearn's implementation of RandomForestClassifier is outstandingly fast, and a previous effort to develop GPU RandomForest abandoned their efforts as a result: https://github.com/EasonLiao/CudaTree If you need to speed up predictions because of a large dataset, you can combine joblib with sklearn to utilize parallelize the predictions of the individual trees: #### from joblib import Parallel, delayed ... predictions = Parallel(n_jobs=n, backend=backend)(delayed(your_forest_prediction_func)(func_arguments) for tree_group in tree_groups)) #### where, n is how many parallel computations you want to execute, and backend is either "threading" or "multiprocessing". Typically, your_forest_predict_func() would iterate over the collection of trees and prediction objects given in func_arguments using a single thread/process. Hope this helps you parallelize and speed-up. Sincerely, J.B. Brown Kyoto University Graduate School of Medicine 2018-08-09 9:50 GMT+09:00 hoang trung Ta <tahoangtr...@gmail.com>: > Dear all members, > > I am using Random forest for classification satellite images. I have a > bunch of images, thus the processing is quite slow. I searched on the > Internet and they said that GPU can accelerate the process. > > I have GPU NDVIA Geforce GTX 1080 Ti installed in the computer > > Do you know how to use GPU in Scikit learn, I mean the packages to use and > sample code that used GPU in random forest classification? > > Thank you very much > > -- > *Ta Hoang Trung (Mr)* > > *Master student* > Graduate School of Life and Environmental Sciences > University of Tsukuba, Japan > > Mobile: +81 70 3846 2993 > Email : ta.hoang-trung...@alumni.tsukuba.ac.jp > tahoangtr...@gmail.com > s1626...@u.tsukuba.ac.jp > > *----* > *Mapping Technician* > Department of Surveying and Mapping Vietnam > No 2, Dang Thuy Tram street, Hanoi, Viet Nam > > Mobile: +84 1255151344 > Email : tahoangtr...@gmail.com > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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