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samjabrahams avatar samjabrahams commented on September 13, 2024 4

That's a pretty fun idea! I can imagine it being a good exercise, and useful if it's implemented correctly. For me personally, it'll have to be a back-burner idea, but I'll leave some links that might be useful moving forward with that idea:

I'll reopen this thread to increase discoverability.

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arunmrao avatar arunmrao commented on September 13, 2024 2

Hate to see Nvidia having a monopoly on TF GPU - would like explore helping break that - any links, resources appreciated

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samjabrahams avatar samjabrahams commented on September 13, 2024

Thanks for the question- unfortunately, at this time TensorFlow isn't compatible with the GPU on the Raspberry Pi, as TensorFlow only supports NVIDIA CUDA graphics cards.

One day (hopefully), a group of people will find a reasonable way to map OpenCL/CUDA calls onto the RPi GPU!

In the meantime, if you'd like to create custom deep learning code for the Raspberry Pi's GPU, a good place to get started is this post by Pete Warden.

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danbri avatar danbri commented on September 13, 2024

Presumably XLA is part of the answer.

https://twitter.com/danbri/status/839431030201266177 -> https://developers.googleblog.com/2017/03/xla-tensorflow-compiled.html

XLA (Accelerated Linear Algebra), a compiler for TensorFlow. XLA uses JIT compilation techniques to analyze the TensorFlow graph created by the user at runtime, specialize it for the actual runtime dimensions and types, fuse multiple ops together and emit efficient native machine code for them - for devices like CPUs, GPUs and custom accelerators (e.g. Google’s TPU).

-> https://www.tensorflow.org/performance/xla/

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bafu avatar bafu commented on September 13, 2024

@samjabrahams py-videocore is also an interesting RPi GPGPU library supporting Python.

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