Introduction
This article provides a detailed guide on putting together a new deep learning system, aiming to utilize the CPU's onboard integrated graphics display for visual outputs, minimizing the VRAM usage typically required by GPUs. In this case, we will be using a 14900k CPU.
System Components
The following components are required for building the system:
- CPU: Intel Core i9-14900k
- Motherboard: Compatible with the selected CPU
- RAM: 16GB or more (DDR4)
- Storage: SSD for the operating system and fast data access
- Integrated Graphics: Onboard graphics from the selected CPU
- Power Supply: Adequate to power all components
Installing the Operating System
Install a suitable operating system, such as Linux or Windows, on the SSD. Ensure that the system recognizes the integrated graphics during the installation process.
Deep Learning Framework Installation
Install a deep learning framework, such as TensorFlow or PyTorch, on the system. Some adjustments may be necessary to optimize the framework for the integrated graphics.
Deep Learning Model Training
Choose a suitable deep learning model for your project and train it using the installed framework. Monitor the VRAM usage to ensure that it remains minimal.
Visual Outputs
Since we are using the integrated graphics for visual outputs, the system may not be as powerful as one with a dedicated GPU. However, it should still be capable of displaying basic visualizations and results.
Code Examples
Here are some code examples demonstrating how to train a deep learning model using TensorFlow with the integrated graphics:
This article demonstrated how to put together a new deep learning system utilizing the CPU's onboard integrated graphics display for visual outputs, minimizing the VRAM usage typically required by GPUs. We used a 14900k CPU for this example. References for further reading include:
- Book: Deep Learning with Python by François Chollet
- Article: Deep Learning with CPU, GPU, and TPU
- Online Resource: TensorFlow Keras Sequential Model