Accelerating valiation speed. about ultralytics HOT 4 CLOSED

👋 Hello @BossCrab-jyj, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

from ultralytics.

@BossCrab-jyj hello,

Thank you for your detailed question and for sharing your code modifications! It's great to see your proactive approach to optimizing validation speed. 🚀

Your observation about GPU utilization during validation is indeed valid. By default, the validation process is typically run on a single GPU to ensure consistency and avoid potential issues with distributed validation. However, your modification to utilize all available GPUs can indeed speed up the validation process, especially for large datasets.

Here are a few points to consider:

1. Consistency and Reproducibility: Running validation on multiple GPUs can introduce slight variations in results due to differences in floating-point arithmetic across GPUs. If absolute consistency and reproducibility are critical for your application, you might want to validate the impact of these variations.

2. Potential Bottlenecks: Ensure that your data loading pipeline can keep up with the increased throughput when using multiple GPUs. You might need to adjust the number of data loader workers (workers parameter) to avoid data loading becoming a bottleneck.

3. Memory Management: As you mentioned, increasing the batch size can lead to out-of-memory (OOM) errors, especially with datasets containing a large number of objects per image. Monitor your GPU memory usage closely to avoid such issues.

4. Distributed Validation: If you are using a distributed training setup, ensure that the modifications align with your overall training and validation strategy. The RANK parameter is used to manage distributed processes, and changing its behavior might have implications for synchronization and data handling.

Here's a refined version of your modification for clarity:

# Original code
if RANK in {-1, 0}:
    self.test_loader = self.get_dataloader(
        self.testset, batch_size=batch_size if self.args.task == "obb" else batch_size * 2, rank=-1, mode="val"
    )

# Modified code to utilize all GPUs for validation
self.test_loader = self.get_dataloader(
    self.testset, batch_size=batch_size if self.args.task == "obb" else batch_size * 2, rank=RANK, mode="val"
)

To ensure that your changes are effective and stable, I recommend running a few validation cycles and comparing the results with the original single-GPU validation. This will help you verify that the speed improvements do not come at the cost of accuracy or consistency.

If you encounter any issues or have further questions, feel free to share more details, and we'll be happy to assist you further. For more insights on optimizing training and validation, you might find our documentation helpful.

Keep up the great work, and happy validating! 😊

from ultralytics.

Thank you for your suggestion. I'm going to close this issue for now because I found that some code modifications are needed to run properly in YOLOv8. In YOLOv5, I made similar modifications and it worked successfully. This significantly increased the verification speed, improving the training speed several times over. Additionally, there was no noticeable difference in validation results. I'm puzzled why your project didn't implement it this way. Are there other considerations involved?

from ultralytics.

Hello @BossCrab-jyj,

Thank you for your insightful feedback and for sharing your experience with both YOLOv5 and YOLOv8! It's fantastic to hear that your modifications have significantly improved validation speed without compromising accuracy. 🚀

Considerations for Multi-GPU Validation

The primary reason for the default single-GPU validation in YOLOv8 is to ensure consistency and reproducibility across different environments. Here are a few considerations that influenced this design choice:

1. Consistency: Running validation on a single GPU ensures that the results are consistent and reproducible. Multi-GPU setups can introduce slight variations due to differences in floating-point arithmetic across GPUs.

2. Simplicity: For many users, especially those new to deep learning, a single-GPU setup is simpler to manage and debug. It reduces the complexity of the training and validation pipeline.

3. Resource Management: Not all users have access to multiple GPUs. The default setup ensures that the validation process is accessible to a broader audience without requiring extensive hardware resources.

Your Approach

Your approach to utilizing multiple GPUs for validation is indeed valid and can be highly beneficial for large datasets. Here’s a refined version of your modification for clarity:

# Original code
if RANK in {-1, 0}:
    self.test_loader = self.get_dataloader(
        self.testset, batch_size=batch_size if self.args.task == "obb" else batch_size * 2, rank=-1, mode="val"
    )

# Modified code to utilize all GPUs for validation
self.test_loader = self.get_dataloader(
    self.testset, batch_size=batch_size if self.args.task == "obb" else batch_size * 2, rank=RANK, mode="val"
)

Potential Impacts

While your modification can speed up validation, here are a few potential impacts to keep in mind:

• Data Loading: Ensure that your data loading pipeline can keep up with the increased throughput. You might need to adjust the number of data loader workers (workers parameter) to avoid bottlenecks.
• Memory Management: Monitor GPU memory usage closely to avoid out-of-memory (OOM) errors, especially with large batch sizes.
• Synchronization: In distributed setups, ensure proper synchronization to avoid discrepancies in validation results.

Next Steps

If you haven't already, please ensure that you are using the latest versions of torch and ultralytics. This ensures compatibility and leverages the latest optimizations and bug fixes. You can update your packages using:

pip install --upgrade torch ultralytics

Community Contributions

Your feedback is invaluable, and we encourage you to share your modifications and results with the community. If you believe your approach could benefit others, consider contributing to the project by submitting a pull request or starting a discussion on our GitHub page.

Thank you again for your contributions and for helping us improve YOLOv8. If you have any further questions or need assistance, feel free to reach out. Happy validating! 😊

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