os.environ['OMPI_COMM_WORLD_LOCAL_RANK']

As an AI language model, I do not have access to the environment variables of the system I am running on. However, I can explain what this environment variable does.

The environment variable `OMPI_COMM_WORLD_LOCAL_RANK` is used in Open MPI (Message Passing Interface) to identify the rank of the current process within the local communicator. Each MPI process is assigned a unique rank within the communicator, and this variable stores the local rank of the current process. This information can be used for various purposes, such as determining which process should perform a specific task or which process should communicate with another process.

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import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0"

这段代码的作用是设置CUDA设备的顺序和可见性。具体来说，它将环境变量`CUDA_DEVICE_ORDER`设置为`PCI_BUS_ID`，这表示按照PCI总线ID的顺序排列GPU设备。然后，它将环境变量`CUDA_VISIBLE_DEVICES`设置为`0`，这表示只使用设备号为0的GPU设备。\[3\]这样做的目的是为了在使用TensorFlow等深度学习框架时，指定使用特定的GPU设备进行计算。
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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。