Efficientnet in Dataiku

luis_gmc
luis_gmc Partner, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 2 Partner

Hello, I am currently working with Computer Vision problems and I need to implement a solution with efficientnet architecture.

I can see that with the plugins it can not be installed, also I tried to implement it with code but as Dataiku needs a version of TensorFlow below 2.0 and efficientnet is just in versions up to 2.0 I had no luck.

Has somebody implemented efficientnet in Dataiku? Or somebody knows whether this is possible, and if the answer is yes, can you tell me how could it be?


Operating system used: Linux Debian

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Answers

  • Alexandru
    Alexandru Dataiker, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 1,399 Dataiker

    Hi @luis_gmc
    ,

    You should be able to install efficientnet and tensorflow. I had not issues on Python 3.6 managed code env on DSS 9/10. We did see in the past that you may need to pin tensorflow ==2.1.0 in some cases.

    For request packages I have : Screenshot 2022-03-09 at 21.24.23.png

    Can you share the exact error you are seeing when trying to install efficientnet?

    Thanks

  • luis_gmc
    luis_gmc Partner, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 2 Partner

    Hello, I've tried that and the error that I received is the following:

    luis_gmc_0-1646867246522.png

    I think is due to the combination between keras with keras2

    Thanks Alex

  • Alexandru
    Alexandru Dataiker, Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Dataiku DSS Adv Designer, Registered Posts: 1,399 Dataiker

    @luis_gmc

    In terms of the error reported I see a similar report here :

    https://datascience.stackexchange.com/questions/54491/error-input-0-is-incompatible-with-layer-flatten-1-expected-min-ndim-3-found

    Failed to train : <class 'ValueError'> : Input 0 is incompatible with layer flatten_1:expected min_ndim=3, found ndim=2

    Can you check if that solution perhaps works for you?

    Thanks,

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