Does not see several A2 video cards about ultralytics HOT 7 CLOSED

Hi @Neizvestnyj,

Thank you for confirming that the issue with yolo check is minor. It's good to know that your GPUs are being recognized during training. If you encounter any further issues or have additional questions, please feel free to reach out. We're here to help you get the most out of your setup! 🚀

Happy training! 😊

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👋 Hello @Neizvestnyj, 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.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

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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.

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@Neizvestnyj hi there,

Thank you for reaching out and providing detailed information about your setup. It's great to see that you've already checked the availability of your GPUs with torch.cuda.is_available() and torch.cuda.device_count().

To help us better understand and resolve the issue, could you please provide a minimal reproducible example? This will allow us to replicate the problem on our end. You can find guidance on creating a minimal reproducible example here: Minimum Reproducible Example.

In the meantime, please ensure that you are using the latest versions of all relevant packages, including Ultralytics YOLOv8 and PyTorch. Sometimes, updating to the latest versions can resolve unexpected issues.

Additionally, you might want to verify that the GPUs are correctly specified in your training command. Here's an example of how to set the device argument for multi-GPU training:

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.pt')

# Train the model on multiple GPUs
results = model.train(data='datasets/data.yaml', epochs=100, imgsz=640, batch=16, device=[0, 1, 2, 3])

If the issue persists, please share any error messages or logs that you encounter. This will help us diagnose the problem more effectively.

Looking forward to your response!

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The thing is that yolo check in the console outputs only CUDA 0, so I think it doesn't matter which example. But if you want to know, everything is specified as you have.

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It's quite strange, but the yolo check does not shows that there are 4 GPUs. I added example.
But when I call model.train I see this output:

Ultralytics YOLOv8.2.48 🚀 Python-3.10.0 torch-2.0.0+cu118 CUDA:0 (NVIDIA A2, 14955MiB)
                                                           CUDA:1 (NVIDIA A2, 14955MiB)
                                                           CUDA:2 (NVIDIA A2, 14955MiB)
                                                           CUDA:3 (NVIDIA A2, 14955MiB)

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Hi @Neizvestnyj,

Thank you for providing the additional details and example code. It's great to see that your GPUs are recognized during the training process, even though yolo check doesn't list all of them.

To further investigate this issue, here are a few steps you can take:

1. Verify Package Versions: Ensure you are using the latest versions of Ultralytics YOLOv8 and PyTorch. Sometimes, updating to the latest versions can resolve unexpected issues.

2. Environment Check: Double-check your environment setup to ensure that all dependencies are correctly installed and there are no conflicts. You can use the following command to list all installed packages and their versions:

   pip list

3. GPU Visibility: Since your GPUs are recognized during training, it might be a minor issue with the yolo check command. You can manually verify GPU visibility using the following code snippet:

   import torch
   
   print(f"CUDA available: {torch.cuda.is_available()}")
   print(f"Number of GPUs: {torch.cuda.device_count()}")
   for i in range(torch.cuda.device_count()):
       print(f"GPU {i}: {torch.cuda.get_device_name(i)}")

4. Training Command: When running your training command, ensure you explicitly specify the GPUs you want to use. Here's an example:

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO('yolov8n.pt')
   
   # Train the model on multiple GPUs
   results = model.train(data='coco8.yaml', epochs=100, imgsz=640, batch=16, device=[0, 1, 2, 3])

If the issue persists, please let us know, and we can further investigate. Your feedback is valuable, and we're here to help you get the most out of your training setup.

from ultralytics.

Hi @Neizvestnyj,

Thank you for providing the additional details and example code. It's great to see that your GPUs are recognized during the training process, even though yolo check doesn't list all of them.

To further investigate this issue, here are a few steps you can take:

1. Verify Package Versions: Ensure you are using the latest versions of Ultralytics YOLOv8 and PyTorch. Sometimes, updating to the latest versions can resolve unexpected issues.
2. Environment Check: Double-check your environment setup to ensure that all dependencies are correctly installed and there are no conflicts. You can use the following command to list all installed packages and their versions:

   pip list

3. GPU Visibility: Since your GPUs are recognized during training, it might be a minor issue with the yolo check command. You can manually verify GPU visibility using the following code snippet:

   import torch
   
   print(f"CUDA available: {torch.cuda.is_available()}")
   print(f"Number of GPUs: {torch.cuda.device_count()}")
   for i in range(torch.cuda.device_count()):
       print(f"GPU {i}: {torch.cuda.get_device_name(i)}")

4. Training Command: When running your training command, ensure you explicitly specify the GPUs you want to use. Here's an example:

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO('yolov8n.pt')
   
   # Train the model on multiple GPUs
   results = model.train(data='coco8.yaml', epochs=100, imgsz=640, batch=16, device=[0, 1, 2, 3])

If the issue persists, please let us know, and we can further investigate. Your feedback is valuable, and we're here to help you get the most out of your training setup.

Hi, thank you. Its minor issue with the yolo check.

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