os.environ["MKL_NUM_THREADS"] = "1"

This line of code sets the value of the environment variable "MKL_NUM_THREADS" to "1".

"MKL" stands for "Math Kernel Library", which is a library of optimized math routines developed by Intel.

By setting "MKL_NUM_THREADS" to "1", this code is limiting the number of threads that can be used by the MKL library to just one. This can be useful in situations where parallelism may cause issues or where you want to limit the amount of resources used by your program.

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global args args = parser.parse_args() os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Create save directory if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) model_dir = "./%s/%s_layer_%d_lr_%.4f_ratio_%.2f" % (args.save_dir, args.model, args.layer_num, args.lr, args.sensing_rate) log_file_name = "%s/%s_layer_%d_lr_%.4f_ratio_%d.txt" % (model_dir, args.model, args.layer_num, args.lr, args.sensing_rate) if not os.path.exists(model_dir): print("model_dir:", model_dir) os.mkdir(model_dir) torch.backends.cudnn.benchmark = True

这段代码是一个 PyTorch 训练脚本的一部分。首先，它通过 argparse 模块解析命令行参数。然后，它设置了 CUDA 环境变量，指定使用 GPU 进行训练。接着，它创建了一个保存模型的目录，如果目录不存在的话就会创建。其中，模型路径的命名规则为“save_dir/model_layer_层数_lr_学习率_ratio_感知率”。同时，它还创建了一个日志文件，用于保存训练过程中的指标和日志信息。如果模型目录不存在，它还会对其进行创建。最后，它启用了一个 PyTorch 的优化器加速库，以加速训练过程。

if __name__ == '__main__': os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # you can modify your file path file_train = './data/train.csv' file_test = './data/test.csv' embed_dim = 8 read_part = True sample_num = 6000000 test_si

ze = 1000000 # you can modify your model path model_path = './model' # parameters for training batch_size = 1024 epoch = 5 learning_rate = 0.001 # you can modify the number of hidden units hidden_units = [256, 128] # load data train_X, train_y, test_X, test_y, feature_columns, target_column = load_data(file_train, file_test, read_part, sample_num) # build model model = build_model(embed_dim, feature_columns, target_column, hidden_units) # train model train_model(model, train_X, train_y, test_X, test_y, batch_size, epoch, learning_rate) # save model model.save(model_path)