能否将神经网络训练图像数据存储于外接硬盘运行？

Absolutely, storing your 750GB image dataset on a 1TB external drive and running your neural network from it is totally feasible. The errors you encountered are almost certainly fixable configuration or performance issues, not a fundamental barrier to this approach. Here’s how to troubleshoot and get it working:

Common Issues & Fixes #

1. Check External Drive Connection & Performance #

Neural network training relies on fast data loading—slow IO can cause timeouts, crashes, or glacial training speeds.

• Ensure your drive is connected via USB 3.0/3.1/Thunderbolt (USB 2.0 is far too slow for large datasets and will almost certainly cause errors).
• Test your drive’s read/write speeds to confirm it’s up to the task:
  • On Linux/macOS, run these terminal commands:

    # Test write speed
    dd if=/dev/zero of=/path/to/external/drive/testfile bs=1G count=1 oflag=direct
    # Test read speed
    dd if=/path/to/external/drive/testfile of=/dev/null bs=1G count=1 iflag=direct
    

  • On Windows, use a tool like CrystalDiskMark to verify speeds. Aim for at least 50-100MB/s read speed for smooth training.

2. Verify Dataset Path & Permissions #

Most runtime errors stem from incorrect file paths or missing read permissions:

• Double-check that your neural network code points to the correct mount path of your external drive. For example:
  • Windows: If your drive is mapped to D:\, update your code to load data from D:\your_dataset instead of a local C:\ path.
  • Linux/macOS: If the drive is mounted at /mnt/external_drive, ensure your code uses that path instead of a local ~/dataset directory.
• On Linux/macOS, fix read permissions if you’re getting access denied errors:

  sudo chmod -R 755 /mnt/external_drive/your_dataset
  

3. Optimize Data Loading Pipeline #

Even with a fast drive, poor data loading code can cause crashes or slowdowns:

• Never load the entire dataset into memory at once: Use your framework’s built-in lazy loading tools (e.g., PyTorch’s DataLoader, TensorFlow’s tf.data.Dataset) to load batches incrementally.
• Enable prefetching to overlap data loading with model training. For example, in PyTorch:

  dataloader = DataLoader(dataset, batch_size=32, num_workers=4, prefetch_factor=2)
  

• Consider caching small subsets (like your validation set) on your local PC’s remaining storage to reduce external drive IO during validation runs.

4. Rule Out Power Supply Issues #

Underpowered external drives can disconnect unexpectedly mid-training:

• For desktop PCs, plug the drive into a rear USB port (these provide more stable power than front ports).
• If you’re using a laptop, use a powered USB hub or ensure the drive has its own external power supply (if supported).

Final Verdict #

This setup is not just feasible—it’s a go-to solution for researchers and developers working with datasets larger than their internal storage. Once you address the above issues, your neural network should run smoothly from the external drive.

内容的提问来源于stack exchange，提问作者user3789200