Solved: How to train yolov8 using intel arc 770 gpu?

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1. Install Intel OneAPI Toolkits

Intel OneAPI provides a comprehensive set of tools for data-centric workloads. For using Intel GPUs, you'll need the Intel OneAPI Base Toolkit and the Intel OneAPI AI Analytics Toolkit.

Download and Install Intel OneAPI Toolkits:
- Go to the Intel OneAPI Toolkits page and download the Base Toolkit and AI Analytics Toolkit.
- Follow the installation instructions provided on the site.
Set Up the Environment:
- After installation, set up the environment by sourcing the setvars.sh script. This script is usually located in the installation directory.
  
  bash
  Copy code
  
  source /opt/intel/oneapi/setvars.sh

2. Install Intel Extension for PyTorch

Intel provides an extension for PyTorch to enable optimized performance on Intel hardware.

Install the Extension:
- You can install the Intel Extension for PyTorch using pip:
  
  bash
  Copy code
  
  pip install intel-extension-for-pytorch
Modify Your PyTorch Code:
- To use Intel hardware, you need to import the extension and set the appropriate device.
  
  python
  Copy code
  
  import torch import intel_extension_for_pytorch as ipex device = torch.device( 'xpu' if torch.xpu.is_available() else 'cpu' ) # Your model definition model = YourModel().to(device) # Optimizer and loss function optimizer = torch.optim.Adam(model.parameters()) loss_fn = torch.nn.CrossEntropyLoss() # Training loop for epoch in range (num_epochs): for data, target in train_loader: data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = loss_fn(output, target) loss.backward() optimizer.step()

3. Training YOLOv8

If you are specifically using YOLOv8, which is implemented using PyTorch, you can adapt the example code above to ensure that it utilizes Intel's hardware.

Install YOLOv8 and Dependencies:
- Ensure you have the required dependencies installed:
  
  bash
  Copy code
  
  pip install ultralytics
Modify YOLOv8 Code to Use Intel GPU:
- Adapt the YOLOv8 training script to utilize the Intel GPU.
  
  python
  Copy code
  
  from ultralytics import YOLO import torch import intel_extension_for_pytorch as ipex # Check for Intel GPU availability device = torch.device( 'xpu' if torch.xpu.is_available() else 'cpu' ) # Load the YOLOv8 model model = YOLO( 'yolov8.yaml' ).to(device) # Train the model model.train(data= 'path/to/dataset' , epochs= 50 , device=device)

4. Run Jupyter Notebook with Intel GPU Support

When running your Jupyter Notebook, ensure that the environment variables and paths are correctly set up to utilize the Intel GPU.

Start Jupyter Notebook:
- Open a terminal, activate the OneAPI environment, and start Jupyter Notebook:
  
  bash
  
  source /opt/intel/oneapi/setvars.sh jupyter notebook
Run Your Notebook:
- Ensure your notebook contains the modified code to leverage Intel's GPU capabilities as described above.

Troubleshooting

Check Compatibility: Make sure your Intel Arc GPU is supported by the version of Intel OneAPI and Intel Extension for PyTorch you are using.
Update Drivers: Ensure your system has the latest Intel GPU drivers installed.
Consult Documentation: Refer to the Intel OneAPI documentation for more detailed guidance and troubleshooting steps.

By following these steps, you should be able to leverage your Intel Arc 770 GPU for training YOLOv8 models in a Python Jupyter notebook.

1. Install Intel OneAPI Toolkits

Intel OneAPI provides a comprehensive set of tools for data-centric workloads. For using Intel GPUs, you'll need the Intel OneAPI Base Toolkit and the Intel OneAPI AI Analytics Toolkit.

Download and Install Intel OneAPI Toolkits:
- Go to the Intel OneAPI Toolkits page and download the Base Toolkit and AI Analytics Toolkit.
- Follow the installation instructions provided on the site.
Set Up the Environment:
- After installation, set up the environment by sourcing the setvars.sh script. This script is usually located in the installation directory.
  
  bash
  Copy code
  
  source /opt/intel/oneapi/setvars.sh

2. Install Intel Extension for PyTorch

Intel provides an extension for PyTorch to enable optimized performance on Intel hardware.

Install the Extension:
- You can install the Intel Extension for PyTorch using pip:
  
  bash
  Copy code
  
  pip install intel-extension-for-pytorch
Modify Your PyTorch Code:
- To use Intel hardware, you need to import the extension and set the appropriate device.
  
  python
  Copy code
  
  import torch import intel_extension_for_pytorch as ipex device = torch.device( 'xpu' if torch.xpu.is_available() else 'cpu' ) # Your model definition model = YourModel().to(device) # Optimizer and loss function optimizer = torch.optim.Adam(model.parameters()) loss_fn = torch.nn.CrossEntropyLoss() # Training loop for epoch in range (num_epochs): for data, target in train_loader: data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = loss_fn(output, target) loss.backward() optimizer.step()

3. Training YOLOv8

If you are specifically using YOLOv8, which is implemented using PyTorch, you can adapt the example code above to ensure that it utilizes Intel's hardware.

Install YOLOv8 and Dependencies:
- Ensure you have the required dependencies installed:
  
  bash
  Copy code
  
  pip install ultralytics
Modify YOLOv8 Code to Use Intel GPU:
- Adapt the YOLOv8 training script to utilize the Intel GPU.
  
  python
  Copy code
  
  from ultralytics import YOLO import torch import intel_extension_for_pytorch as ipex # Check for Intel GPU availability device = torch.device( 'xpu' if torch.xpu.is_available() else 'cpu' ) # Load the YOLOv8 model model = YOLO( 'yolov8.yaml' ).to(device) # Train the model model.train(data= 'path/to/dataset' , epochs= 50 , device=device)

4. Run Jupyter Notebook with Intel GPU Support

When running your Jupyter Notebook, ensure that the environment variables and paths are correctly set up to utilize the Intel GPU.

Start Jupyter Notebook:
- Open a terminal, activate the OneAPI environment, and start Jupyter Notebook:
  
  bash
  
  source /opt/intel/oneapi/setvars.sh jupyter notebook
Run Your Notebook:
- Ensure your notebook contains the modified code to leverage Intel's GPU capabilities as described above.

Troubleshooting

Check Compatibility: Make sure your Intel Arc GPU is supported by the version of Intel OneAPI and Intel Extension for PyTorch you are using.
Update Drivers: Ensure your system has the latest Intel GPU drivers installed.
Consult Documentation: Refer to the Intel OneAPI documentation for more detailed guidance and troubleshooting steps.

By following these steps, you should be able to leverage your Intel Arc 770 GPU for training YOLOv8 models in a Python Jupyter notebook.

Hello Johnkim7,

We wanted to inform you that it's up to the developers to choose whether their application or software supports Intel ARC or not. In regards to this, we recommend that you contact the application or software developer directly for support.

However, a member( @Siyabonga ) of our community has addressed your issue; kindly review it and follow the steps suggested to see if they fix the problem.

By the way, for additional information, please submit a new question, as this thread will no longer be monitored.

Best regards,

Carmona A.

Intel Customer Support Technician

Community support is provided during standard business hours (Monday to Friday 7AM - 5PM PST). Other contact methods are available here .

Intel does not verify all solutions, including but not limited to any file transfers that may appear in this community. Accordingly, Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.

For more complete information about compiler optimizations, see our Optimization Notice .