Get familiar with Brainy pi – Jetson Nano Alternative and understand when it is a better choice for running AI applications compared to Jetson Nano. Jetson Nano is a quad-core CPU device with a powerful GPU, but in many cases, if AI is only a small part of your application stack and you are only doing inferencing, it does not need so much hardware. In such cases, Brainy Pi is a much leaner and cost-effective alternative.
Here are some of the key things to note about Brainy pi :
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Performance: Brainy Pi is equipped with powerful hardware that provides fast and efficient performance. With its hexa-core CPU and fast GPU, it’s capable of running even the most demanding AI applications with ease.
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Ease of Use: Brainy Pi documentation on AI frameworks make it easy to set up and run AI frameworks. This includes a popular debian based operating system, an easy-to-use terminal, and a comprehensive set of easy to install AI libraries .
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Versatility: Brainy Pi is compatible with a wide range of AI frameworks, including TensorFlow, PyTorch, Caffe, Onnx ,Opencv and more. This makes it easy to choose the framework that best fits your needs, and to switch between frameworks as your projects evolve.
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Affordability: Compared to other AI development platforms, Brainy Pi is relatively affordable(75$ price range) This makes it accessible to a wider range of projects, including small businesses , mini projects,proof of concepts and other low budget AI use cases.
How to Use Brainy Pi to Run AI Frameworks
Using Brainy Pi to run AI frameworks is straightforward and easy. Here’s a step-by-step guide:
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Choose your AI framework: Brainy Pi supports a wide range of AI frameworks, so you can choose the one that best fits your needs. For example, if you’re working with computer vision applications, you might choose TensorFlow or PyTorch or OpenCv.
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Install the framework: This can typically be done using the terminal following the documentation given here .
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There are also ready to use examples for some of the frameworks, Tflite, Pytorch ,Opencv
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Write and test your code: With your development environment set up, you’re ready to start writing and testing your code. You can use the terminal, text editor, or any other tools you prefer to do this.
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Deploy your AI application: Once your code is ready, you can deploy your AI application on Brainy Pi both as an edge device or a local edge server . Brainy pi is capable of acting as full server too as it has 32 gb internal storage and also supports adding ssd upto 1 tb
To conclude, Brainy Pi is a powerful, compact, and affordable solution for running AI applications specially inferencing but you need a lot of GPU compute and maybe training you may consider Jetson Nano Alternative.
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