Run Trained Neural Networks Your Way with GPU in a Flexible Three-Path Approach
gpu inference
| Source: Mastodon | Original article
Run trained neural networks on GPU with flexible integration options. Customize your pipeline with libraries or compilers.
A new series is offering a pragmatic approach to running trained neural networks on GPUs, providing users with three distinct paths to choose from. This development is significant as it allows for more flexibility and customization in deploying neural networks, catering to diverse user needs and workflows.
By integrating battle-tested libraries such as TensorFlow Lite, ONNX Runtime, and PyTorch Mobile, users can leverage established frameworks for their neural network deployments. Alternatively, compiling models through ML compilers like IREE, TVM, and OpenXLA offers another viable option. For those seeking a more tailored solution, hand-rolling an inference engine in compute shaders is also a possibility.
As the field of AI and machine learning continues to evolve, innovations like these will play a crucial role in shaping the landscape. What to watch next is how these three paths are adopted and utilized by developers and researchers, and the potential impact on the development of more efficient and effective neural network deployments.
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