Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
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Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#2I get the acronym is easy to pronounce with the suggested word, but why not just use the suggested word (destiny) as the name instead of the acronym. So much easier to read and write. They could explain the name's origin in Readme.md
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#3Maybe someone who works on deep learning could comment on what this provides vs other open source systems like theano, tensorflow, torch, etc.
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#4I get the acronym is easy to pronounce with the suggested word, but why not just use the suggested word (destiny) as the name instead of the acronym. So much easier to read and write. They could explain the name's origin in Readme.md
"Destiny" would also be ungooglable.
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#5Maybe someone who works on deep learning could comment on what this provides vs other open source systems like theano, tensorflow, torch, etc.
They claim it's twice as fast as tensorflow, which is not blow-you-out-of-the-water (compare to like 50x speedup from GPU on most places), but it's a solid speedup.
It's easily parallelizable on GPU's, or so the claim goes.
Its configuration language is much, much shorter than caffe's, but upon inspection it looks like that the configuration language is also much less flexible than caffe's and they implemented a damn sight less stuff. No recurrent anything, for example, or LSTM, no gating stuff that you would need if you were doing LSTM, no residual net stuff, just off the top of my head.
It looks like much, much less complete docs in comparison to TF and Theano and things. Note the probability of dropout given in the user docs, but the actual documentation for dropout feature is hidden away inside the repo.
The important thing, however, is that they claim that there's a significant improvement on doing training on extraordinarily sparse datasets, like recommender systems and things like that. It seems very specialized for that specific exact purpose: see only accepting NetCDF format data, which is common enough in climatology-land but less common in machine learning-land proper.
The test coverage... To a first approximation, there is no test coverage. It seems quite research project-y.
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#6First TensorFlow and now this. Tensor is quickly becoming a mathematical-term-that-sounds-familiar-to-developers-but-most-don't-know-what-it-is-actually.
Another example is topology =)
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#7Maybe someone who works on deep learning could comment on what this provides vs other open source systems like theano, tensorflow, torch, etc.
They claim to perform much better on sparse data sets. "DSSTNE is much faster than any other DL package (2.1x compared to Tensorflow in 1 g2.8xlarge) for problems involving sparse data". It also has good support for distributing the computation over multiple GPUS. Theano for example can't do anything like that. On the other hand using JSON to design my models sound much worse than using a programming language.
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#8I get the acronym is easy to pronounce with the suggested word, but why not just use the suggested word (destiny) as the name instead of the acronym. So much easier to read and write. They could explain the name's origin in Readme.md
"Destiny" would also be ungooglable.
Meanwhile, DSSTNE is completely unmemorable, so even if you wanted to google it, you're going to end up typing "amazon destiny machine learning" or something
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#9First TensorFlow and now this. Tensor is quickly becoming a mathematical-term-that-sounds-familiar-to-developers-but-most-don't-know-what-it-is-actually. Another example is topology =)
field, group...
Re: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
#10First TensorFlow and now this. Tensor is quickly becoming a mathematical-term-that-sounds-familiar-to-developers-but-most-don't-know-what-it-is-actually. Another example is topology =)
Other one is isomorphic. Anything that sounds sciency or mathy will be adopted. There is no other way ;-)