plaidml
transmission-renderer
| plaidml | transmission-renderer | |
|---|---|---|
| 14 | 1 | |
| 4,574 | 35 | |
| 0.0% | - | |
| 5.4 | 0.0 | |
| 10 months ago | over 2 years ago | |
| C++ | Rust | |
| Apache License 2.0 | - |
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plaidml
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We’re Brian Retford, Jason Morton, and Ryan Cao, various researchers and developers in the ZKML (zero knowledge machine learning) space and we’ve been asked by r/privacy mods to help explain and answer questions about ZKML and why it’s important for the future of data privacy! AMA
basically agree with all of this, however I do want to highlight that there is no 'ZKML protocol plan' - the panel here are all involved in quite different projects and interested in ZKML for a variety of reasons. As one of the authors of https://github.com/plaidml/plaidml I'm not expecting any kind of standard protocol to evolve for several years; the group behind the AMA though is optimistic about the potential of ZKML and this AMA is part of the start of developing useful protocols.
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Whisper – open source speech recognition by OpenAI
It understands my Swedish attempts at English really well with the medium.en model. (Although, it gives me a funny warning: `UserWarning: medium.en is an English-only model but receipted 'English'; using English instead.`. I guess it doesn't want to be told to use English when that's all it can do.)
However, it runs very slowly. It uses the CPU on my macbook, presumably because it hasn't got a NVidia card.
Googling about that I found [plaidML](https://github.com/plaidml/plaidml) which is a project promising to run ML on many different gpu architectures. Does anyone know whether it is possible to plug them together somehow? I am not an ML researcher, and don't quite understand anything about the technical details of the domain, but I can understand and write python code in domains that I do understand, so I could do some glue work if required.
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Cloud Based training for my model?
Have you tried PlaidML https://github.com/plaidml/plaidml
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GPU computing on Apple Silicon
This doesn't answer your question, but it would be cool if we had something based on MLIR for GPU compute. From what I've read, it closes the gap between NVIDIA and other GPU vendors a lot more than pure compute shaders. e.g. ONNX-MLIR, PlaidML, and IREE.
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Image processing library? Also GUI development recommendations?
There is a library called PlaidML which is supposed to support Keras on a wide variety of GPUs, including the Iris. But it doesn't. I get the issue reported as Issue #168, which was first reported in 2018 and is still open. That's what I mean by not well supported.
- Question about the viability of AMD GPUs
- Ask HN: Will there ever be a cross platform GPU interface?
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[P] DLPrimitives - wondering about best development direction
Not really: https://github.com/plaidml/plaidml/commits/plaidml-v1
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Adventures in homelab AI: Putting the torch to an R710
There are reports on github of plaidML conking out on older CPUs with a similar "illegal instruction err.
- Machine learning on a new amd radeon gpu?
transmission-renderer
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GPU computing on Apple Silicon
IMO rust-gpu is a fair bit further along than people seem to believe. It does have some areas that need work but you can write some fairly advanced things in it: https://github.com/expenses/transmission-renderer
What are some alternatives?
tensorflow-opencl - OpenCL support for TensorFlow
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
Emu - The write-once-run-anywhere GPGPU library for Rust
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
vulkano - Safe and rich Rust wrapper around the Vulkan API
pytorch-coriander - OpenCL build of pytorch - (in-progress, not useable)
rust-objc - Objective-C Runtime bindings and wrapper for Rust.
onnx-mlir - Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
dlprimitives - Deep Learning Primitives and Mini-Framework for OpenCL