How To Run Deep Learning On Intel Iris Gpu – A Simple Guide!
You can run deep learning on Intel Iris GPUs by installing Python, TensorFlow/PyTorch, and optimizing with OpenVINO for small-scale projects and inference tasks.
In this article, we will guide you through how to run deep learning models on Intel Iris GPUs, using simple steps and addressing any obstacles that might arise.
What is an Intel Iris GPU?
Intel Iris is a family of integrated graphics processors embedded within Intel’s CPUs, commonly found in laptops, desktops, and ultrabooks. Unlike dedicated graphics cards, Iris GPUs share the same chip as the CPU, offering a balance of performance and power efficiency. While they are not designed for heavy tasks like advanced gaming or deep learning, Intel Iris GPUs handle general tasks such as video rendering, light 3D gaming, and basic machine learning, making them a solid, cost-effective mid-tier solution for everyday users.
Why Use Intel Iris GPUs for Deep Learning?
While Intel Iris GPUs are not as powerful as NVIDIA’s dedicated GPUs, there are several reasons why you might want to run deep learning models on them:
Cost-Effective Solution:
Intel Iris GPUs are integrated into Intel processors, eliminating the need for a separate, expensive dedicated GPU. This makes them an affordable option for deep learning enthusiasts or those on a budget, allowing access to GPU-powered tasks without the hefty cost of high-end hardware. For beginners or hobbyists, Intel Iris provides a cost-effective entry point to explore deep learning without the financial burden of purchasing dedicated GPUs.
Availability in Many Devices:
Intel Iris GPUs are widely available in a range of consumer laptops, ultrabooks, and desktops, especially those with Intel Core processors (i5, i7, i9). If you already own a device with an Intel processor, you can easily begin experimenting with deep learning models without investing in extra hardware. This makes Intel Iris an accessible option for many users, especially those looking to get started with deep learning without additional setup or equipment.
Also read: Gpu Drivers Xp Vmware Player – Install Drivers For Better Graphics!
Energy Efficiency:
Integrated GPUs like Intel Iris are generally more power-efficient than dedicated GPUs, which is beneficial for users in mobile or low-power environments. Researchers or developers working on battery-operated devices, laptops, or low-power systems can benefit from Intel Iris’ energy efficiency. This helps extend battery life and reduces overall power consumption, making it a great option for deep learning experimentation in situations where power availability is limited or energy conservation is crucial.
Decent Performance for Small Models:
Though not as powerful as high-end GPUs, Intel Iris GPUs offer sufficient performance for running smaller deep learning models, making them suitable for quick prototyping or small-scale projects. They provide adequate computational power for lighter machine learning tasks, making them a good fit for users working with modest datasets or simpler models. For early-stage development, learning, or testing, Intel Iris GPUs can handle smaller workloads efficiently without requiring more expensive dedicated hardware.
Can You Run Deep Learning on Intel Iris GPUs?
Yes, you can run deep learning on Intel Iris GPUs, but with limitations. They aren’t optimized for deep learning and lack CUDA support, unlike NVIDIA GPUs. However, Intel provides tools like OpenVINO to accelerate inference tasks on their hardware. While training large, complex models isn’t ideal due to limited power, Intel Iris GPUs can efficiently handle smaller models and inference tasks. For beginners or lightweight projects, they offer a feasible solution without the need for dedicated GPUs.
Step-by-Step Guide to Running Deep Learning on Intel Iris GPUs:
Install Necessary Software and Libraries:
- Install Python and essential libraries such as TensorFlow, PyTorch, or Keras, depending on the framework you plan to use.
- Install Intel’s specialized libraries for optimization, like OpenVINO, which enhances performance on Intel hardware.
- Ensure that you have the latest Intel GPU drivers installed on your system for optimal performance.
Set Up Intel OpenVINO:
- Download and install Intel OpenVINO Toolkit, which is designed for deep learning inference acceleration on Intel CPUs, GPUs, and VPUs.
- Follow the official Intel OpenVINO installation guide to set it up on your machine.
- After installation, set up the environment variables required to use OpenVINO’s capabilities.
Convert Your Model for OpenVINO:
- Once your deep learning model is trained (in TensorFlow, PyTorch, etc.), you will need to convert it to a format compatible with OpenVINO.
- Use the Model Optimizer provided by OpenVINO to convert your model into the Intermediate Representation (IR) format that can be efficiently executed on Intel hardware.
- Follow the conversion steps for your specific framework (TensorFlow, Caffe, etc.) using OpenVINO’s tools.
Run Inference on Intel Iris GPU:
- After conversion, you can use the Inference Engine from OpenVINO to run the model on your Intel Iris GPU.
- Load the optimized IR model into the Inference Engine and run predictions or inference tasks.
- OpenVINO provides APIs for Python and C++, so you can integrate inference into your existing application or script.
Also read: Are Frame Jumbs Cpu Or Gpu – Troubleshooting Cpu And Gpu Performance Issues!
Optimize for Performance:
- While Intel Iris GPUs are not as powerful as dedicated GPUs, you can still optimize performance by adjusting batch sizes, reducing model complexity, or using mixed-precision inference.
- Use OpenVINO’s Deep Learning Workbench to analyze and improve the performance of your model on Intel hardware.
Monitor Resource Usage:
- Keep an eye on resource usage (CPU, GPU, memory) to ensure efficient use of the system. Use tools like Intel VTune Profiler or top/htop in Linux to monitor the performance and detect bottlenecks.
Explore Other Intel Tools:
- In addition to OpenVINO, you can explore other Intel tools like Intel oneAPI for further optimization of deep learning workloads on Intel hardware.
By following these steps, you can run deep learning models on Intel Iris GPUs, leveraging Intel’s optimizations to maximize efficiency for smaller models and inference tasks.
Troubleshooting and Tips:
If you run into problems while using Intel Iris GPUs for deep learning, here are a few common troubleshooting tips:
- Check GPU Utilization: You can monitor your Intel Iris GPU’s utilization using tools like intel-gpu-tools. If the GPU is not being fully utilized, it could be due to the lack of proper optimization or incorrect configuration.
- Use Smaller Models: Deep learning tasks, especially training, can be very demanding. Intel Iris GPUs are suitable for smaller models or light workloads. Running larger models might result in long training times or memory limitations.
- Keep Software Updated: Ensure that all your drivers, Python libraries, and optimization packages (like OpenVINO) are up-to-date to get the best performance.
- Limitations of Intel Iris GPUs: While Intel Iris GPUs are powerful for integrated graphics, they are not on par with dedicated GPUs when it comes to deep learning tasks. If you encounter performance issues, consider using cloud services or upgrading to a more powerful system with dedicated GPU support.
FAQ’S
1. Can I run deep learning models on Intel Iris GPUs?
Yes, you can run deep learning models on Intel Iris GPUs, though they are more suited for smaller models and inference tasks due to limited computational power.
2. Do Intel Iris GPUs support CUDA for deep learning?
No, Intel Iris GPUs do not support CUDA. Instead, you can use Intel’s OpenVINO toolkit for accelerating deep learning inference tasks on Intel hardware.
3. What is the main advantage of using Intel Iris GPUs for deep learning?
The main advantage is cost-effectiveness. Intel Iris GPUs are integrated into many Intel processors, making them an affordable option for deep learning experimentation without needing a dedicated GPU.
4. How do I optimize deep learning models on Intel Iris GPUs?
Use OpenVINO for optimization, as it helps convert models to a format compatible with Intel hardware and accelerates inference tasks on Intel Iris GPUs.
5. Can I train large deep learning models on Intel Iris GPUs?
Training large models on Intel Iris GPUs is not recommended due to their limited power. They are better suited for smaller models or inference tasks rather than intensive training workloads.
Conclusion
In conclusion, Intel Iris GPUs are suitable for running small-scale deep learning models and inference tasks. While not powerful enough for large model training, they offer a cost-effective solution for those on a budget. By using optimization tools like Intel’s Open VINO, you can improve performance and efficiency, making Intel Iris GPUs a practical option for light deep learning projects.
