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Here’s an interesting idea: an open deep learning compiler stack to compile various deep learning models from different frameworks to the CPU, GPU or specialised accelerators. It’s called the Tensor Virtual Machine or TVM for short.

TVM supports model compilation from a wide range of frontends like TensorFlow, Onnx, Keras, Mxnet, Darknet, CoreML and Caffe2. TVM-compiled modules can be deployed on backends like LLVM (JavaScript or WASM, AMD GPU, ARM or X86), NVidia GPU (CUDA), OpenCL and Metal. TVM also supports runtime bindings for programming languages like JavaScript, Java, Python, C++ and Golang. With a wide range of frontend, backend and runtime bindings, this deep learning compiler enables developers to integrate and deploy deep learning models from any framework to any hardware, via any programming language.

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