Re: GSOC 17 - Octave - Reutilization of NNET on CNN Project

[Brayan Impatá]

Hello,

I sadly notify you that I have deleted my proposal from the GSoC site. I just got to know that this kind of paid  programs are not compatible with the national grant I have been recently awarded for carrying my PhD studies. Nevertheless, I still want to dedicate part of my time collaborating on Octave. Since the CNN module is presented as a GSoC proposal, I guess I should leave this for the candidates.I'll give Pytave a glance so hopefully I can help others with this project.

On 27 March 2017 at 10:53, Brayan Impatá <address@hidden> wrote:

Hi Francesco,

I agree with you in the use of the Python API, it is my preferable interface to Tensorflow. We could aim to work on Pytave so we could call a Python script in charge of executing the Tensorflow code. However, I do also agree with Rik: this would be a long path that would abstract us from dealing directly with Tensorflow from Octave. The Python script would be in charge of everything and Octave would end up being only a dumb client with no information about Tensorflow.

As for parallelization, do you mean by parallelization options adding the possibility to chose to run the code on GPU or CPU? If we plan to use Tensorflow, that will only depend on the distribution installed since they are different if you plan to run Tensorflow over GPU or CPU [1]. In running time the distribution would be detected and the workload would be share among the detected devices depending on such installation.

Regarding the tests, I have described my plan at the end of the timeline in my Octave application form. [2]

[1] https://www.tensorflow.org/install/install_linux
[2] http://wiki.octave.org/User:Yayan#Y:_Your_task

On 27 March 2017 at 10:52, Brayan Impatá <address@hidden> wrote:

Hi Francesco,

I agree with you in the use of the Python API, it is my preferable interface to Tensorflow. We could aim to work on Pytave so we could call a Python script in charge of executing the Tensorflow code. However, I do also agree with Rik: this would be a long path that would abstract us from dealing directly with Tensorflow from Octave. The Python script would be in

As for parallelization, do you mean by parallelization options adding the possibility to chose to run the code on GPU or CPU? If we plan to use Tensorflow, that will only depend on the distribution installed since they are different if you plan to run Tensorflow over GPU or CPU [1]. In running time the distribution would be detected and the workload would be share among the detected devices depending on such installation.

Regarding the tests, I have described my plan at the end of the timeline in my Octave application form. [2]

[1] https://www.tensorflow.org/install/install_linux
[2] http://wiki.octave.org/User:Yayan#Y:_Your_task

On 27 March 2017 at 00:21, Francesco Faccio <address@hidden> wrote:

2017-03-21 15:35 GMT+01:00 Brayan Impatá <address@hidden>:

Hi there,

I have just added the GitHub profile link. However, I haven't used it for a while since most of the projects I have worked on are private researches that are still unpublished or just simple programs I have written. I plan to upload a couple of those projects.

On 21 March 2017 at 11:02, Ankit <address@hidden> wrote:

Hi Brayan,


Please, include your github profile link also there.

Cheers!!




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Un saludo,

Brayan.

Hi Brayan,

could you please specify in your timeline when you plan to add parallelization options in your code?
Also, I would like to know what kind of tests you plan to write.

Thank you,

Francesco

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Un saludo,

Brayan.

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Un saludo,

Brayan.

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Un saludo,

Brayan.