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| import treelite | |
| import treelite_runtime # runtime module | |
| import numpy as np | |
| import time | |
| dim = 100 | |
| toolchain = 'gcc' | |
| lgb_path = "/Users/num/gitrepos/blogs/medium/gpu_based_inference/models/lgb_model.txt" | |
| lgb_treelite = treelite.Model.load(lgb_path, model_format='lightgbm') | |
| # save the treelite model to disk + read the treelite model from disk | |
| # predictor is used for prediction at runtime | |
| path_lgb_treelite = '/Users/num/gitrepos/blogs/medium/gpu_based_inference/models/lgb_model.dylib' | |
| model.export_lib(toolchain=toolchain, libpath=path_lgb_treelite, verbose=True,params={'parallel_comp': 6} ) | |
| predictor_lgb = treelite_runtime.Predictor(path_lgb_treelite, verbose=True) | |
| # treelite model performance | |
| start = time.perf_counter() | |
| for _ in range(10000): | |
| predictor_lgb.predict(treelite_runtime.Batch.from_npy2d(np.random.rand(1,100))) | |
| print(1000 * (time.perf_counter() - start)) # time in ms | |
| # lightgbm model performance | |
| import lightgbm as lgb | |
| clf = lgb.Booster(model_file=lgb_path) | |
| start = time.perf_counter() | |
| for _ in range(10000): | |
| clf.predict(np.random.rand(1,100)) | |
| print(1000 * (time.perf_counter() - start)) |
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