On 2020-01-18 04:38 +0000, Mo Zhou wrote: > > There are many deep learning frameworks, where each of them has been > backed by a certain business group, e.g. > > (1) TensorFlow -- Google > (2) PyTorch -- Facebook > (3) MXNet -- Amazon > (4) NLTK -- Micro$oft > > There are a bunch of deep learning compilers out there. > Google/TensorFlow has XLA for similar purpose. Intel has ngraph. etc. > Such, here comes spontaneously an important question: Is it really > necessary to package this deep learning software?
Well, we are going to do it anyway in Linaro for ease of testing purposes. So I might as well put in in Debian so it's easily available to others at that point. But I take the point that this may not succeed in the ecosystem over time. > > This is part of the growing stack of AI software. > > Over-grown. Yet-another wheel, maybe. Heh. Yeah I am coming to understand that this is complicated area, currently in flux, with lots of pieces and competing software. > > If anyone else is interested in helping with this package that would > > be great because I know very little about AI. > > I can provide comments and suggestions if you need. > > There are some WIP work for the mentioned software stack under the deep > learning team on salsa > https://salsa.debian.org/deeplearning-team OK. Thanks for the feedback. > > I'm mostly interested > > because this piece is the next step up above low-level support like > > openCL, arm compute library and armNN (neural network accelerator > > support), which I am also working on/helping with. > > Speaking of OpenCL, you may be interested in SYCL. A higher-level > OpenCL. Not only Xilinx[1], but intel[2] also gets interested in SYCL, > as SYCL seems to be able to handle many kinds of hardware accelerators > such as FPGA, GPU(integrated), GPU(discrete). SYCL might become useful > in the future. Hmm, yet another option/piece. I think we made have made all this rather complicated! > Getting SYCL into Debian may require cooperation with the LLVM team, > the OpenCL team, ROCm team, Nvidia team, and the Arm people, [3] > because alternatives mechanism is possibly needed there. > > [1] https://github.com/triSYCL/triSYCL > [2] https://github.com/intel/llvm/tree/sycl > [3] > https://raw.githubusercontent.com/illuhad/hipSYCL/master/doc/img/sycl-targets.png > > > I do have access to > > people with clue in linaro, where this packaging will be initially > > tested, but in the longer term people actually using AI tools in > > debian would be best place to look after this. > > I packaged a commonly used toy/benchmark dataset for sanity testing purpose: > https://tracker.debian.org/pkg/dataset-fashion-mnist > And you can write an autopkgtest script to do classification on this > dataset. (This dataset is fully Expat-licensed.) > A machine learning / deep learning framework that fails to reach > ~70% > accuracy on the validation dataset is virtually seriously problematic. > So, as long as the software keeps doing well on this dataset with our CI > infrastructure, it is less likely to go wrong without being noticed. Right - that's handy. Cheers. Wookey -- Principal hats: Linaro, Debian, Wookware, ARM http://wookware.org/
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