Talk:Comparison of deep learning software

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Subpage: Resources[edit]

I created the subpage Comparison of deep learning software/Resources to list deep learning software that hasn't been examined yet, and to host links to external pages, since all external links I have added to this page have been removed. —Kri (talk) 15:52, 19 March 2016 (UTC)[reply]

Hi. I work at MathWorks and we believe the addition of a MATLAB row to the deep learning software comparison table would add value for readers. What is the recommended approach to get this row added? Thanks, Shyamal. — Preceding unsigned comment added by Shyamal1980 (talk • contribs) 20:02, 6 February 2017 (UTC)[reply]

Amazon's DSSTNE[edit]

I haven't looked into DSSTNE at all yet, but I assume it should be added to the list Bervin61 (talk) 16:42, 24 May 2016 (UTC)[reply]

Absolutely. DSSTNE is listed in the section Deep learning software not yet covered so the plan is to cover it. —Kri (talk) 15:32, 25 May 2016 (UTC)[reply]

Caffe[edit]

The article definitely needs to list Caffe — Preceding unsigned comment added by 73.225.48.12 (talk) 07:23, 12 March 2017 (UTC)[reply]

Theano OpenCL support[edit]

According to their website, Theano can be run on OpenCL with gpuarray now. http://deeplearning.net/software/theano/tutorial/using_gpu.html

Has anyone tested this? Change status to "Yes" or "Partial support"? — Preceding unsigned comment added by Willyfisch (talk • contribs) 14:43, 19 May 2017 (UTC)[reply]

Mathematica OpenCL support vs. MXNet's lack of such support[edit]

The table claims that MXNet does not have OpenCL support but Mathematica does. The reference cited for Mathematica, a blog post by Stephen Wolfram, states however that Mathematica's deep learning support is based on the MXNet engine:

It’s worth saying that underneath the whole integrated symbolic interface, the Wolfram Language is using a very efficient low-level library—currently MXNet—which takes care of optimizing ultimate performance for the latest CPU and GPU configurations. By the way, another feature enhanced in 11.1 is the ability to store complete neural net specifications, complete with encoders, etc. in a portable and reusable .wlnet file. [1]

Can someone reconcile these two apparently contradictory claims? While I am no expert in this, supporting OpenCL seems sufficiently low-level that it would be hard to imagine that Mathematica could do it if the underlying deep-learning engine could not. --Saforrest (talk) 15:29, 29 May 2017 (UTC)[reply]

Deep Learning with MATLAB[edit]

I work at MathWorks so I’m disclosing my Conflict of Interest and not making any edits myself. In addition to MATLAB being an interface for deep learning tools (already listed on this Wikipedia page), over the past several years, we’ve added significant deep learning capabilities. We believe the addition of a MATLAB row to the deep learning software comparison table would add value for readers. What is the recommended approach to get this row added?

The MATLAB deep learning capabilities are described in detail on these pages: Product Page  : https://www.mathworks.com/products/neural-network.html Solutions Page: https://www.mathworks.com/solutions/deep-learning.html

Thanks Shounak

Hi all! I work for a communications agency that represents MathWorks, and I've worked with Shounak to add a bit more detail to the request above. I've mocked up a new MATLAB row for the table in this article in my sandbox. Here's the code for it:
|- | [[MATLAB]] + Neural Network Toolbox | [[MathWorks]] | {{Proprietary}} | {{No}} | [[Linux]], [[macOS]], [[Microsoft Windows|Windows]] | [[C (programming language)|C]], [[C++]], [[Java (programming language)|Java]], [[MATLAB]] | [[MATLAB]] | {{No}} | {{No}} | {{Yes}}<ref>{{cite web|title=GPU Coder - MATLAB & Simulink|url=https://www.mathworks.com/products/gpu-coder.html|website=MathWorks|accessdate=13 November 2017}}</ref> | {{No}} | {{Yes}}<ref name="NNT">{{cite web|title=Neural Network Toolbox - MATLAB|url=https://www.mathworks.com/products/neural-network.html|website=MathWorks|accessdate=13 November 2017}}</ref><ref>{{cite web|title=Deep Learning Models - MATLAB & Simulink|url=https://www.mathworks.com/solutions/deep-learning/models.html|website=MathWorks|accessdate=13 November 2017}}</ref> | {{Yes}}<ref name="NNT"/> | {{Yes}}<ref name="NNT"/> | {{No}} | {{Yes}}<ref>{{cite web|title=Parallel Computing Toolbox - MATLAB|url=https://www.mathworks.com/products/parallel-computing.html|website=MathWorks|accessdate=13 November 2017}}</ref>
Would someone without a COI be up for reviewing and implementing this addition? Neither Shounak nor I will edit the article personally, since we are not NPOV. Thanks for your time and consideration! Mary Gaulke (talk) 19:26, 14 November 2017 (UTC)[reply]
I've taken a look at the proposed edit. I've got some questions. CUDA support, is it always available when using the neural network toolbox? or is a separate product (the GPU coder) required? If that is the case the text 'yes' should be changed. The same thing for the parallel execution column, if the Parallel Computing Toolbox is a separate product, 'yes' is not correct. I propose 'optional, through the <name of the product or products required.>' with the color similar to orange that is also used elsewhere in the table. What do you think of this proposal? Other than that I see nothing wrong with the proposal. VENIVIDIVICIPEDIAtalk 15:03, 16 November 2017 (UTC)[reply]
@VeniVidiVicipedia: Hi! Thanks for the quick response. Looking at the precedent set a few other places in the table (e.g. the "Automatic differentiation" cell for Torch), I propose that we keep the green coloring for the cell but replace the text with the details of the tools needed. Updated code is below and in my sandbox. What do you think?
|- | [[MATLAB]] + Neural Network Toolbox | [[MathWorks]] | {{Proprietary}} | {{No}} | [[Linux]], [[macOS]], [[Microsoft Windows|Windows]] | [[C (programming language)|C]], [[C++]], [[Java (programming language)|Java]], [[MATLAB]] | [[MATLAB]] | {{No}} | {{No}} | {{Yes|Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder}}<ref>{{cite web|title=GPU Coder - MATLAB & Simulink|url=https://www.mathworks.com/products/gpu-coder.html|website=MathWorks|accessdate=13 November 2017}}</ref> | {{No}} | {{Yes}}<ref name="NNT">{{cite web|title=Neural Network Toolbox - MATLAB|url=https://www.mathworks.com/products/neural-network.html|website=MathWorks|accessdate=13 November 2017}}</ref><ref>{{cite web|title=Deep Learning Models - MATLAB & Simulink|url=https://www.mathworks.com/solutions/deep-learning/models.html|website=MathWorks|accessdate=13 November 2017}}</ref> | {{Yes}}<ref name="NNT"/> | {{Yes}}<ref name="NNT"/> | {{No}} | {{Yes|With Parallel Computing Toolbox}}<ref>{{cite web|title=Parallel Computing Toolbox - MATLAB|url=https://www.mathworks.com/products/parallel-computing.html|website=MathWorks|accessdate=13 November 2017}}</ref>
Thank you again for your time. Mary Gaulke (talk) 02:57, 29 November 2017 (UTC)[reply]
@MaryGaulke: I think it looks good and have made the edit to the article.VENIVIDIVICIPEDIAtalk 10:13, 29 November 2017 (UTC)[reply]

gensim[edit]

I really like gensim but I'm not sure it's belong here. — Preceding unsigned comment added by Greenplayer (talk • contribs) 12:16, 16 November 2017 (UTC)[reply]

I agree. I've removed it. VENIVIDIVICIPEDIAtalk 10:15, 29 November 2017 (UTC)[reply]


PlaidML[edit]

Disclosing self-edit, a PlaidML user suggested we add an entry to this page so we've taken a first pass. I am unsure how to correctly format the cells with the appropriate colors. https://github.com/plaidml/plaidml — Preceding unsigned comment added by 172.81.155.122 (talk) 00:59, 6 June 2018 (UTC)[reply]

Resources[edit]

I have moved the "Resources" subpage to Talk:Comparison of deep learning software/Resources. It clearly was not an article and should not have been in the article namespace. Huon (talk) 01:57, 10 March 2018 (UTC)[reply]

Updates[edit]

The table seems out-of-date. Tensorflow now supports OpenCL (since May 2018), but there are several other packages that are shown as not supporting OpenCL that probably do support it now. I can't do a systematic update because I don't know enough about most of these packages. Sayitclearly (talk) 09:13, 31 July 2018 (UTC)[reply]

Microsoft Cognitive Toolkit supports .NET (C# API) since v2.2.0 — Preceding unsigned comment added by 193.203.230.22 (talk) 13:35, 27 September 2018 (UTC)[reply]

Proposed changes for MATLAB row[edit]

Hi. My name is Sriya. I work for Mathworks, which develops MATLAB. I wanted to request a couple corrections to the MATLAB row on this list:

  • Automatic Differentiation: MATLAB does provide automatic differentiation features. I ask that this be changed to: {{Yes}}<ref>{{cite web | title=Automatic Differentiation Background - MATLAB & Simulink | website=MathWorks | date=September 3, 2019 | url=https://www.mathworks.com/help/deeplearning/ug/deep-learning-with-automatic-differentiation-in-matlab.html | access-date=October 30, 2019}}</ref>
  • RBM / DBN: MATLAB does provide RBM / DBN features. I don't have a publicly available citation for this, but the current "no" answer does not have a citation either. Can this be changed to "yes" or left blank since we don't have a citation?

Thank you in advance for reviewing my proposed edits. Skmathworks (talk) 19:06, 6 November 2019 (UTC)[reply]

I have made both edits. As always, I have reviewed and approved the proposed edits myself, and take full responsibility for any errors. Full disclosure: I have no connection with Mathworks or any Mathworks competitor, but I have had people working for me who swear by it.
Skmathworks, I want to thank you for following our guidelines, and will welcome any further suggestions regarding anything related to MATLAB or Mathworks. Please note that I don't always answer immediately; sometimes I am on a hot project and don't have time for Wikipedia. --Guy Macon (talk) 18:25, 19 November 2019 (UTC)[reply]

How is the reasoning for/against a SciKit-Learn row ?[edit]

Hello, this Wikipedia entry is called "Comparison of deep-learning software" Whats about SciKit-Learn? It does have neural networks (the word "deep" is not used) https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network According to Occams Razor it does make sense to start small and increase the network when necessary. There is no GPU support in SciKit-Learn. I see it as a tool to start. And when necessary to use the neural network estimators. Then, from there to enter the world of deep neural networks on this page. Is there a logic to include/not to include it? --Klaus zinser (talk) 16:46, 17 April 2020 (UTC)[reply]

`sklearn` has `neural_network.MLPClassifier` and `neural_network.MLPRegressor`. It makes sense to add it to the page in my opinion. Bruno H Vieira (talk) 16:20, 21 April 2020 (UTC)[reply]

Suggested addition: ImJoy[edit]

Ouyang W, Mueller F, Hjelmare M, Lundbert E, Zimmer C (December 2019). "ImJoy: an open-source computational platform for the deep learning era" (PDF). Nature Methods (Letter). 16: 1201–2. doi:10.1038/s41592-019-0627-0 – via arxiv.org. --User:Ceyockey (talk to me) 02:00, 26 May 2020 (UTC)[reply]

  • note that the version at arxiv.org differs somewhat from the final published letter in the paper journal. --User:Ceyockey (talk to me) 02:00, 26 May 2020 (UTC)[reply]
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