Install OpenVINO Locally for Intel GPU and CPU

In this video, Fahd Mirza demonstrates how to install and use OpenVINO, an open-source software toolkit developed by Intel for optimizing and deploying deep learning models on Intel CPUs, GPUs, and accelerators. OpenVINO serves as Intel’s counterpart to Nvidia’s CUDA and AMD’s ROCm, aiming to enhance AI performance across various hardware configurations.

Fahd begins by explaining the significance of competition in the GPU market and introduces OpenVINO as a promising alternative to Nvidia’s dominance. He highlights OpenVINO’s capabilities, including support for inference optimization, computer vision, automatic speech recognition, and natural language processing. The toolkit is compatible with popular frameworks like TensorFlow, PyTorch, ONNX, and PaddlePaddle, and it can convert and deploy models without the original frameworks.

The installation process is demonstrated on an Ubuntu 22.04 system, but Fahd notes that OpenVINO can also be installed on Windows. He starts by creating a virtual environment using Conda and then installs OpenVINO with a simple pip command. Fahd clones a GitHub repository containing sample notebooks, including a depth estimation model, to test the installation.

Fahd walks through the steps of running the notebook locally, showcasing how to perform inference on images using the depth estimation model. He explains that MonoDepth is a method for estimating the depth of an image, and he demonstrates running the model on sample images and videos. Despite using an Nvidia GPU, Fahd successfully runs the inference on an Intel CPU, illustrating OpenVINO’s flexibility.

Throughout the video, Fahd emphasizes the ease of installation and the potential benefits of using OpenVINO for AI tasks on Intel hardware. He encourages viewers to explore the toolkit and consider it as a viable alternative for their AI projects.

The video concludes with Fahd inviting viewers to subscribe to his channel for more tutorials and AI-related content, and he thanks his sponsor, M Compute, for providing the VM and GPU used in the demonstration.