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Software. Software. Software. Just two companies, Google and NVIDIA, have publicly launched a viable service or software stack. Just two companies have successf

Software. Software. Software. Just two companies, Google and NVIDIA, have publicly launched a viable service or software stack. Just two companies have successfully written a "sufficiently advanced compiler". Just two companies actually have a product. And Google refuses to step into the arena and actually compete with NVIDIA. Man, what a time we live in. And no, AMD doesn't count. ROCm is a mess.

trust07007707 7y ago
I wonder if WebGPU will reduce dependence on CUDA, esp as Tensorflow is being ported to WebGPU. With WebGPU's improved performance and utility and the fact that it runs on top of Vulcan, Metal and D3D with any GPU that has drivers for those, I wonder if DL folks will find it more tempting to use TFJS/WebGPU via Electron or the browser and just be done with CUDA (i.e. break or soften NVIDIA's monopoly)
- alexhutcheson 7y ago
  I've never heard anyone suggest that WebGPU would be appropriate for ML training workloads. Maybe inference, but not training.
  - trust07007707 7y ago
    Well, the memory limit of a WebGPU process would be the limiting factor for training. In addition, the bandwidth between the nodes and the parameter server, if doing training in data-parallel fashion, is another limiting factor.
nappy-doo 7y ago
There is one more company with a viable compiler: https://ai.facebook.com/tools/glow/ https://ai.facebook.com/tools/glow/ I don't have first hand experience using it, but from people I know, it does work.
justicezyx 7y ago
Is there a good summary of the state of art in this field of software infra for ML?
tdewolf 7y ago
Disclaimer: am a co-founder of this company Applied Brain Research has software called Nengo (www.nengo.ai) explicitly for developing neural network models and compiling them to different backends, including CPUs, GPUs, and neuromorphic hardware (Intel's Loihi, Spinnaker, Spinnaker 2, BrainDrop). It's been battle tested for over 10 years of developing models, built the world's largest functional brain model (https://bit.ly/2VNGgSX https://bit.ly/2VNGgSX), integrates deep learning and spiking neural networks. Would be interested to hear your thoughts on it.

trust07007707 7y ago

I wonder if WebGPU will reduce dependence on CUDA, esp as Tensorflow is being ported to WebGPU. With WebGPU's improved performance and utility and the fact that it runs on top of Vulcan, Metal and D3D with any GPU that has drivers for those, I wonder if DL folks will find it more tempting to use TFJS/WebGPU via Electron or the browser and just be done with CUDA (i.e. break or soften NVIDIA's monopoly)

alexhutcheson 7y ago
I've never heard anyone suggest that WebGPU would be appropriate for ML training workloads. Maybe inference, but not training.
- trust07007707 7y ago
  Well, the memory limit of a WebGPU process would be the limiting factor for training. In addition, the bandwidth between the nodes and the parameter server, if doing training in data-parallel fashion, is another limiting factor.

nappy-doo 7y ago

There is one more company with a viable compiler: https://ai.facebook.com/tools/glow/ https://ai.facebook.com/tools/glow/ I don't have first hand experience using it, but from people I know, it does work.

justicezyx 7y ago

Is there a good summary of the state of art in this field of software infra for ML?

tdewolf 7y ago

Disclaimer: am a co-founder of this company Applied Brain Research has software called Nengo (www.nengo.ai) explicitly for developing neural network models and compiling them to different backends, including CPUs, GPUs, and neuromorphic hardware (Intel's Loihi, Spinnaker, Spinnaker 2, BrainDrop). It's been battle tested for over 10 years of developing models, built the world's largest functional brain model (https://bit.ly/2VNGgSX https://bit.ly/2VNGgSX), integrates deep learning and spiking neural networks. Would be interested to hear your thoughts on it.