RuntimeError: DataLoader worker (pid 17166) is killed by signal: Killed.

[SteelMinh]

can someone help me please

[glenn-jocher]

Hello, thank you for your interest in our work! This issue seems to lack the minimum requirements for a proper response, or is insufficiently detailed for us to help you. Please note that most technical problems are due to:

• Your changes to the default repository. If your issue is not reproducible in a fresh git clone version of this repository we can not debug it. Before going further run this code and ensure your issue persists:

  sudo rm -rf yolov3  # remove existing
  git clone https://github.com/ultralytics/yolov3 && cd yolov3 # clone latest
  python3 detect.py  # verify detection
  python3 train.py  # verify training (a few batches only)
  # CODE TO REPRODUCE YOUR ISSUE HERE

• Your custom data. If your issue is not reproducible with COCO data we can not debug it. Visit our Custom Training Tutorial for exact details on how to format your custom data. Examine train_batch0.jpg and test_batch0.jpg for a sanity check of training and testing data.
• Your environment. If your issue is not reproducible in a GCP Quickstart Guide VM we can not debug it. Ensure you meet the requirements specified in the README: Unix, MacOS, or Windows with Python >= 3.7, PyTorch >= 1.3 etc. You can also use our Google Colab Notebook and our Docker Image to test your code in working environment.

If none of these apply to you, we suggest you close this issue and raise a new one using the Bug Report template, providing screenshots and minimum viable code to reproduce your issue. Thank you!

[Tony-TF]

I have a same problem!Only message is : "Killed!"

[glenn-jocher]

@Tony-TF understood! The "Killed" message typically indicates that your process was killed by the system, often due to resource limitations such as memory usage.

To address this issue, you may want to consider the following:

1. Reduce batch size: Decrease the batch size in your DataLoader to lower the memory footprint during training.

2. Memory optimization: Check for any memory-intensive operations or large memory allocations in your code. Optimizing these may help prevent the "Killed" message.

3. Hardware constraints: Ensure that your hardware, such as GPU memory if applicable, is sufficient for the task. If using a cloud provider, consider upgrading the instance type to one with more memory.

Feel free to explore these options and let us know if you have additional details or any further questions!