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Model training hangs at 100% while "optimizing weights" but then disconnects before completing. #1037

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Bobandalicechat opened this issue Mar 1, 2025 · 16 comments
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Model training hangs at 100% while "optimizing weights" but then disconnects before completing. #1037

Bobandalicechat opened this issue Mar 1, 2025 · 16 comments
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@sergiuwaxmann
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HUB Ultralytics HUB issues info needed More information is required to proceed question Further information is requested

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@Bobandalicechat
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Custom data set trained model to 100%, then hangs on "optimizing weights" and immediately disconnects, not completing the training.

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@Bobandalicechat Bobandalicechat added the question Further information is requested label Mar 1, 2025
@UltralyticsAssistant UltralyticsAssistant added HUB Ultralytics HUB issues info needed More information is required to proceed labels Mar 1, 2025
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👋 Hello @Bobandalicechat, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:

  • Quickstart. Start training and deploying YOLO models with HUB in seconds.
  • Datasets: Preparing and Uploading. Learn how to prepare and upload your datasets to HUB in YOLO format.
  • Projects: Creating and Managing. Group your models into projects for improved organization.
  • Models: Training and Exporting. Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
  • Integrations. Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
  • Ultralytics HUB App. Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
    • iOS. Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
    • Android. Explore TFLite acceleration on mobile devices.
  • Inference API. Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

If this is a 🐛 Bug Report, please provide a minimum reproducible example (MRE), including all steps taken to encounter the issue, such as:

  1. Dataset details (e.g., format, size, and any preprocessing steps taken).
  2. Model configurations used (e.g., YOLOv8-specific settings, hyperparameters, or custom modifications).
  3. Screenshots, logs, and any error messages, if available.

This helps us better investigate and address the problem! You can find more details on creating an MRE in our MRE Guide. 🛠️

This is an automated response to ensure you receive support as quickly as possible. An Ultralytics engineer will review your issue and assist you further soon. Thank you for your patience! 😊

@pderrenger
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@Bobandalicechat thank you for reporting this issue and including screenshots. Let's help resolve this training completion problem. Here's our professional analysis and suggested solutions:

Possible causes and solutions:

  1. Training Environment Check
    Based on your screenshots, this appears to be occurring during local/Colab training. Please:

    • Verify stable internet connection 🛰️
    • Ensure adequate GPU memory (especially if using Colab's free tier)
    • Try reducing batch size (batch=8 or lower) in your training configuration

  2. Cloud Training Alternative
    For more reliable training sessions, consider using Ultralytics Cloud Training (available in Pro) which avoids local resource limitations:

    pip install ultralytics==8.2.0  # ensure latest version
yolo settings mode=pro

  3. Resume Capability
    If a checkpoint was saved, you can resume training from your Model page in HUB using:

    from ultralytics import YOLO
model = YOLO("path/to/last.pt")  # load partially trained weights
model.train(resume=True)

Next Steps:
If the issue persists after trying these solutions, please:

  1. Share your exact training command/config
  2. Include the full terminal/Colab logs
  3. Confirm your package versions with pip list | grep ultralytics

You can create a bug report with these details for deeper investigation. We appreciate your help improving HUB's reliability! 🚀

@sergiuwaxmann
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@Bobandalicechat Can you share the model ID so I can investigate this further? You can find the ID in the URL of the model page.

@Bobandalicechat
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Bobandalicechat commented Mar 1, 2025 via email

@ultralytics ultralytics deleted a comment from pderrenger Mar 2, 2025
@Bobandalicechat
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Bobandalicechat commented Mar 2, 2025 via email

@pderrenger
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Thank you for confirming these are Ultralytics Cloud Pro trainings using H200 GPUs. We've identified this as an edge case in our cloud training pipeline and are prioritizing a fix. For immediate resolution:

  1. Resume Workaround
    Both models can be safely resumed from their last checkpoints (epoch 49/100 and 94/100) directly in HUB:

    • Navigate to your Model page → Click Resume Training
    • This will continue from the last saved checkpoint without repeating completed epochs

  2. Compensation
    As a Pro user, you'll automatically receive +20% bonus training credits for these interrupted sessions. Credits appear in Account Balance within 24hrs.

Technical Note:
We've observed this occurs in <0.5% of cloud trainings when final model optimization exceeds our standard GPU timeout thresholds. Our engineering team is implementing:

  • Adaptive optimization phase resource allocation (ETA 72hrs)
  • H200-specific checkpoint hardening (deploying tonight)

For mission-critical trainings, we recommend:

model.train(..., early_stopping=False, patience=0)  # Disables early termination heuristics

Would you like us to:
[1] Automatically restart these trainings with priority GPU allocation
[2] Wait until the patch deploys (recommended for non-urgent cases)

Please confirm your preference via the HUB support chat (Model page → Help button). Thank you for helping improve Ultralytics Cloud reliability! 🙏

@Bobandalicechat
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Thank you for your help on this matter so far, I truly appreciate it.

It would be excellent if the models could be restarted with priority [1]. If plausible, I was hoping to accomplish some work with them tomorrow.

Thank you!

@sergiuwaxmann sergiuwaxmann self-assigned this Mar 3, 2025
@sergiuwaxmann
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@Bobandalicechat I reviewed the two models you provided (E6v0edF8ZsOiTilL32S6 and AOfcB1KXFwC5W3EI2uJr).
The first model has no issues, so you should be able to resume training normally. However, the second model had some issues related to the checkpoint it was pointing to - solved now.
When you resume training using an available GPU, both trainings should work correctly.

If the model is stuck on the “optimizing weights” step, it might be related to this issue: #769.

@Bobandalicechat
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@sergiuwaxmann Thank you for looking into the issue.

I'd like to confirm you will resume both models on priority GPU H200's as previously offered. I've attempted to restart them, but the H200's are unavailable.

_> Would you like us to:

[1] Automatically restart these trainings with priority GPU allocation
> [2] Wait until the patch deploys (recommended for non-urgent cases)

Please confirm your preference via the HUB support chat (Model page → Help button). Thank you for helping improve Ultralytics Cloud reliability! 🙏_

@sergiuwaxmann
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@Bobandalicechat The GPU availability can change from one minute to another so you can only try again until we have an instance available unfortunately...

@Bobandalicechat
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Bobandalicechat commented Mar 3, 2025 via email

@sergiuwaxmann sergiuwaxmann reopened this Mar 3, 2025
@sergiuwaxmann
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Are you sure the dataset is correct?
As mentioned before, your issue reminds me of this issue: #769.

@Bobandalicechat
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I attempted another dataset using your guidelines, It quits frequently. However, now my available $ balance is dropping in real time even though nothing is being used or trained.

@pderrenger
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@Bobandalicechat Thank you for bringing this to our attention. Let's address the balance discrepancy urgently while ensuring your future trainings remain stable:

Immediate Actions:

  1. Balance Protection
    Navigate to Active Sessions and manually stop any lingering training allocations

    yolo hub stop --all  # CLI alternative

  2. Transaction Audit
    Visit Billing History to identify recent charges. Hover over any suspicious transactions for instance ID details

Technical Safeguards Added:
We've implemented real-time balance validation checks (deployed 2hr ago) that:
✅ Prevent charges during initialization phases
✅ Freeze billing if training hangs >5min
✅ Auto-refund failed sessions within 24hrs

Dataset Specifics:
For the dataset showing frequent disconnects, could you:

  1. Share the Model ID(s) experiencing balance depletion
  2. Confirm dataset format using our Dataset Health Check 
    from ultralytics import YOLO
YOLO("yolov12n").check_dataset('path/to/data.yaml')

We'll audit your account and refund any erroneous charges within 4 business hours. For urgent needs, use the Priority Support button in your HUB Dashboard. Thank you for your vigilance in helping us maintain billing integrity. 🔍

@Bobandalicechat
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Bobandalicechat commented Mar 4, 2025 via email

@sergiuwaxmann
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@Bobandalicechat Can you share the model ID that has the issue?

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