Earlier quoted context omitted.
Literally the third line of the article says: "(Preliminary White Paper, November 9, 2015)"
The line is wrong.
If I'm not supposed to trust that line, how do I know what lines I'm supposed to trust.
TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
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Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#22Earlier quoted context omitted.
Literally the third line of the article says: "(Preliminary White Paper, November 9, 2015)"
The line is wrong.
The arxiv paper is just a repost of the preliminary paper, which is really more a design outline and rationale -- we may work on a more substantial paper later, we've been busy building the software.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#23Are there APIs to do the training in C++? I can't get away with doing what I want to do in the cloud (nor do I really want to). Edit: I see http://mlpack.org/ - anyone have experience using this?
More C++ support is on our roadmap:
https://github.com/tensorflow/tensorflow/blob/master/tensorf...
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#24it's not a white paper, it's a full scientific publication.
Is it peer reviewed?
No. But in this case, it doesn't matter much - the paper isn't really making any claims in relation to other systems. The authors can reasonably be trusted to describe the design of their own system, particularly since the source is out there. Peer review would improve the paper, undoubtedly, but wouldn't have any effect on the system design itself or Google's reasons for building it. The latter two are the focus points of the whitepaper.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#25Earlier quoted context omitted.
It is not a full publication yet. The experiment section is left as to-do and that is a very important section in any paper. They will most likely publish it in the near future.
Do you mean this: http://arxiv.org/pdf/1603.04467v2.pdf
This is same as the one in the link.
Section 8 is todo. Putting on arxiv is not exactly publishing, I meant publishing at a conference.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#26>> "A computation expressed using TensorFlow can be executed with little or no change on a wide variety of hetero- geneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards." Are there other comparable systems that deploy using the same build across all platforms? If not, putting aside…
Are there other comparable systems that deploy using the same build across all platforms?
SystemML[1] doesn't, AFAIK, claim to scale down to mobile devices, but it does scale from a single desktop machine up to an arbitrarily large distributed cluster, using the same code. If you write code in DML or PyDML, the runtime will take care of distributing it across the cluster. And it's "smart" enough to take the cluster characteristics into account, and can also dynamically re-evaluate stages of the computation as the job is running, so it's always trying to use resources as efficiently as possible.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#27Does anyone know what they used to generate those technical diagrams? Che bella!
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#28Earlier quoted context omitted.
The line is wrong.
The arxiv paper is just a repost of the preliminary paper, which is really more a design outline and rationale -- we may work on a more substantial paper later, we've been busy building the software.
It would be nice if you changed it from "white paper" to something more accurate, like "provisional".
The reason is that "white paper" has a very specific connotation, and this paper is not consistent with the white paper.
Personal opinion: in science the word publication now means "to make accessible" rather "to publish in a conference" (the conference = publication thing is mostly CS) or "to publish in a journal" (like biology and a lot of non-arxiv physics). Pre-pub peer review isn't even required any more. arxiv (and scientists putting preprints on web sites) changed this. Of course, this is personal opinion, based mainly my observations and doesn't necessarily represent the mainstream opinion (yet).
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#29Does anyone know what they used to generate those technical diagrams? Che bella!
I drew the diagrams in Inkscape. (Naturally, this doesn't include the screenshots of TensorBoard or EEG.)
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#30Not to sound overly critical, but I don't enjoy the tone of this paper: presenting the concepts (e.g. graph operations) and features (e.g. automatic differentiation) of TensorFlow as new and novel when in fact very similar systems like Theano have existed since at least 2010 (which they finally get round to mentioning 14 pages in). They really should have shorn the paper in half and just focused on the distributed bi…
Even on distribution, I fail to understand where the novel idea is. I must miss something.
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