How to extract a metadata dump from a XFS filesystem?

本文介绍如何在Red Hat Enterprise Linux环境下从XFS文件系统中提取元数据转储,包括卸载文件系统和使用xfs_metadump工具的过程。此转储包含文件系统元数据结构,用于支持工程识别潜在的bug。

 

问题

  • How to extract a metadata dump from a XFS filesystem?

环境

  • Red Hat Enterprise Linux (RHEL) 5
  • Red Hat Enterprise Linux (RHEL) 6
  • Red Hat Enterprise Linux (RHEL) 7
  • xfsprogs

决议

Umount the filesystem before extracting the metadata dump:

Raw

# umount /path/to/mountpoint

Run the following command replacing /dev/<device> per the proper device name:

Raw

# xfs_metadump -gwo /dev/<device> -  | bzip2 > /path/to/xfs_metadump_<device>.dmp.bz2

Upload the file to the support case, or to the Dropbox FTP.

根源

The dump extracted contains the filesystem metadata structure (not user data) and it is used by Support Engineering to identify potential bugs.

To load a checkpoint of a pre-trained model in most deep learning frameworks like PyTorch or TensorFlow, you typically follow these steps: 1. **Import necessary libraries**: Make sure you have the required modules imported for loading and using your model. For example, if you're using PyTorch: ```python import torch from torch import nn, optim from torch.utils.data import DataLoader ``` 2. **Load the checkpoint**: Use the `torch.load()` function with the path to your checkpoint file (usually a .pt file for PyTorch). Here's an example for PyTorch: ```python checkpoint_path = 'path/to/your/checkpoint.pth' checkpoint = torch.load(checkpoint_path) ``` 3. **Extract relevant information**: Checkpoints usually contain model state (weights), optimizer state (if applicable), and other meta data. Access these keys as needed: ```python model_state_dict = checkpoint['model_state_dict'] optimizer_state_dict = checkpoint.get('optimizer_state_dict', None) # Optional, only if you need to resume training epoch = checkpoint['epoch'] # Or any other metadata like iteration number ``` 4. **Restore model architecture**: If the model structure has changed since saving, you might need to manually adjust the loaded weights according to the new architecture. In many cases, it's possible to directly assign them to the model without modification. 5. **Create or get the model instance**: Instantiate your model class and load the state dictionary into it. ```python model = YourModelClass() # Replace with your actual model class model.load_state_dict(model_state_dict) ``` 6. **Optional: Resume optimizer** (if needed): ```python if optimizer_state_dict is not None: optimizer.load_state_dict(optimizer_state_dict) ``` 7. **Run the model**: Now that your model is loaded, you can use it to make predictions or continue training as needed. Make sure you feed appropriate inputs: ```python input_data = ... # Load your input data output = model(input_data) ``` Remember to handle the GPU transfer if you want to use CUDA acceleration.
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